https://www.na-mic.org/w/api.php?action=feedcontributions&feedformat=atom&user=Bmenze 2026-06-08T01:28:04Z User contributions MediaWiki 1.33.0 https://www.na-mic.org/w/index.php?title=Projects:TumorModeling&diff=65605 2011-03-25T14:27:07Z

<p>Bmenze: </p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]<br /> __NOTOC__<br /> = Modeling tumor growth in patients with glioma =<br /> [[Image:Multimodal_glioma.png|thumb|left|300px| Figure 1: Multi-modal image data from a patient with low-grade glioma. A large number of different modalities and derived parameter volumes are acquired during the monitoring of tumor growth.]]<br /> <br /> We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth.<br /> <br /> This aims at two directions: First, it aims at making complex information from longitudinal multi-modal data set accessible for diagnostic radiology through physiological models. This will allow us to estimate features such as degree of infiltration, speed of growth, or mass effect in a quantitative fashion; for therapy it will allow us to identify regions at risk for progression. Second, it aims at providing the means to test different macroscopic tumor models from theoretical biology on real clinical data.<br /> <br /> The project has three main aims: 1) the automated segmentation of tumors in large multi-modal image data sets to make information of different MR image modalities accessible for the tumor model, 2) the development of methods for the image-based estimation of parameters in reaction-diffusion type models of tumor growth, and 3) the processing and analysis of magnetic resonance spectroscopic images (MRSI) as a potential application of the tumor model.<br /> <br /> <br /> == Segmenting tumors in large multimodal data sets ==<br /> <br /> <br /> [[Image:Tumor_segmentation_lesion_atlas.png|thumb|left|300px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]<br /> <br /> To segment all MR image volumes available for a patient we developed an approach for learning patient-specific lesion atlases (Figure 2) with limited user interaction. Figure 2 shows the manual segmentation of the tumor from different raters (red, green, blue) and the automatic segmentation using the patient-specific lesion atlas (black) in T1-MRI, T1-MRI and the fractional anisotropy map from DTI. <br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Lashkari D, Weber MA, Ayache N and Golland P. A generative model for brain tumor segmentation in multi-modal images. Proc MICCAI 2010. LNCS 6362, 151-59''<br /> <br /> <br /> == Image-based modeling of tumor growth ==<br /> <br /> <br /> [[Image:fisher-state-space_tumor-shapes.png|thumb|left|300px| Figure 3: Variation of tumor shapes for different parameterizations of the Fisher-Kolmogorov tumor model. All tumors have the same size, they vary in Diffusivity 'D' and proliferation 'rho'. In our approach this shape information is used to infer general properties of the tumor.]]<br /> <br /> We propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a Fisher-Kolmogorov reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.<br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Honkela A, Konukoglu E, Weber MA, Ayache N and Golland P. A generative approach for image-based modeling of tumor growth. Proc IPMI 2011. LNCS.''<br /> <br /> === Results inverse modeling ===<br /> Figure 4 illustrates the adaptive grid sampling of the parameter space for a modeled tumor. The size of the green sampling points indicates how often the indicated location parameter of the tumor model was evaluated under different parametrizations. The ground truth is indicated by the pink cross. Most adaptively chosen sampling points are close to the ground truth. The figure also shows isolines of tumor cell density (red), the predicted extensions of the T2 hyper-intense area (yellow) and tissue boundaries (black).<br /> <br /> Figure 5 shows results of the proposed approach: Green samples are obtained from the proposed sparse grid approach while blue sample are obtained via standard MCMC. Black circles indicate means of the two distributions. Ground truth for A and B are indicated by the pink cross. In D the previously estimated speed of growth [7] is shown by the pink line. The sparse grid sampling approximation performs better than the direct MCMC (A-B). Estimates correlate well with priviously published results, but provide a more accurate characterization of the state of disease (D).<br /> <br /> [[Image:Sampling_spatial-parameters.png‎|thumb|left|230px| Figure 4: Adaptive grid sampling of the parameter space for a modeled tumor. Green dots show sampling points. Red and yellow lines show isolines of tumor cell infiltration. (Also see text above.)]]<br /> <br /> [[Image:Results_Tumor-Model.png|thumb|left|500px| Figure 5: MCMC sampling results for 'D' and 'rho' using different synthetic and real data sets. The proposed method (green samples) shows less variation when compared to the standard sampling approach (blue) and is at least as close to the ground truth (pink). (Also see text above.) ]]<br /> <br /> <br /> == Processing magnetic resonance spectroscopic images ==<br /> <br /> <br /> [[Image:Sivic_slicer_slc_mets.jpg|thumb|left|300px| Figure 6: MRSI metabolite maps generated in Slicer-SIVIC module referenced to anatomical (FLAIR) image.]]<br /> <br /> To make the metabolic information of magnetic resonance spectroscopic images available for modeling the evolution of glioma growth we are implementing an [http://wiki.na-mic.org/Wiki/index.php/2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface MRSI processing module] for Slicer jointly with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] project at UCSF. <br /> <br /> We envision that physiological tumor models may be used to interpret MRSI, integrating the highly specific metabolic information of the spectroscopic images with other clinical MRI modalities in a principled fashion.<br /> <br /> <br /> = Key Investigators =<br /> <br /> * MIT: [http://people.csail.mit.edu/menze Bjoern Menze], [http://people.csail.mit.edu/tammy Tammy Riklin Raviv], [http://people.csail.mit.edu/koen Koen Van Leemput], [http://people.csail.mit.edu/polina Polina Golland]<br /> * Harvard: William M. Wells<br /> * INRIA Sophia-Antipolis, France: Ezequiel Geremia, Olivier Clatz, Nicholas Ayache<br /> * Microsoft Research, UK: Ender Konukoglu<br /> * DKFZ Heidelberg, Germany: Bram Stieltjes, Marc-Andre Weber<br /> <br /> = Publications =<br /> *[http://www.na-mic.org/publications/pages/display?search=Projects%3ATumorModeling&amp;submit=Search&amp;words=all&amp;title=checked&amp;keywords=checked&amp;authors=checked&amp;abstract=checked&amp;sponsors=checked&amp;searchbytag=checked| NA-MIC Publications Database on brain tumor segmentation and modeling]</div>

Bmenze https://www.na-mic.org/w/index.php?title=Projects:TumorModeling&diff=65604 2011-03-25T14:22:55Z

<p>Bmenze: /* Image-based modeling of tumor growth */</p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]<br /> __NOTOC__<br /> = Modeling tumor growth in patients with glioma =<br /> [[Image:Multimodal_glioma.png|thumb|left|300px| Figure 1: Multi-modal image data from a patient with low-grade glioma. A large number of different modalities and derived parameter volumes are acquired during the monitoring of tumor growth.]]<br /> <br /> We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth.<br /> <br /> This aims at two directions: First, it aims at making complex information from longitudinal multi-modal data set accessible for diagnostic radiology through physiological models. This will allow us to estimate features such as degree of infiltration, speed of growth, or mass effect in a quantitative fashion; for therapy it will allow us to identify regions at risk for progression. Second, it aims at providing the means to test different macroscopic tumor models from theoretical biology on real clinical data.<br /> <br /> The project has three main aims: 1) the automated segmentation of tumors in large multi-modal image data sets to make information of different MR image modalities accessible for the tumor model, 2) the development of methods for the image-based estimation of parameters in reaction-diffusion type models of tumor growth, and 3) the processing and analysis of magnetic resonance spectroscopic images (MRSI) as a potential application of the tumor model.<br /> <br /> <br /> == Segmenting tumors in large multimodal data sets ==<br /> [[Image:Tumor_segmentation_lesion_atlas.png|thumb|left|300px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]<br /> <br /> To segment all MR image volumes available for a patient we developed an approach for learning patient-specific lesion atlases (Figure 2) with limited user interaction. Figure 2 shows the manual segmentation of the tumor from different raters (red, green, blue) and the automatic segmentation using the patient-specific lesion atlas (black) in T1-MRI, T1-MRI and the fractional anisotropy map from DTI. <br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Lashkari D, Weber MA, Ayache N and Golland P. A generative model for brain tumor segmentation in multi-modal images. Proc MICCAI 2010. LNCS 6362, 151-59''<br /> <br /> <br /> == Image-based modeling of tumor growth ==<br /> [[Image:fisher-state-space_tumor-shapes.png|thumb|left|300px| Figure 3: Variation of tumor shapes for different parameterizations of the Fisher-Kolmogorov tumor model. All tumors have the same size, they vary in Diffusivity 'D' and proliferation 'rho'. In our approach this shape information is used to infer general properties of the tumor.]]<br /> <br /> We propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a Fisher-Kolmogorov reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.<br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Honkela A, Konukoglu E, Weber MA, Ayache N and Golland P. A generative approach for image-based modeling of tumor growth. Proc IPMI 2011. LNCS.''<br /> <br /> === Results inverse modeling ===<br /> Figure 4 illustrates the adaptive grid sampling of the parameter space for a modeled tumor. The size of the green sampling points indicates how often the indicated location parameter of the tumor model was evaluated under different parametrizations. The ground truth is indicated by the pink cross. Most adaptively chosen sampling points are close to the ground truth. The figure also shows isolines of tumor cell density (red), the predicted extensions of the T2 hyper-intense area (yellow) and tissue boundaries (black).<br /> <br /> Figure 5 shows results of the proposed approach: Green samples are obtained from the proposed sparse grid approach while blue sample are obtained via standard MCMC. Black circles indicate means of the two distributions. Ground truth for A and B are indicated by the pink cross. In D the previously estimated speed of growth [7] is shown by the pink line. The sparse grid sampling approximation performs better than the direct MCMC (A-B). Estimates correlate well with priviously published results, but provide a more accurate characterization of the state of disease (D).<br /> <br /> <br /> [[Image:Sampling_spatial-parameters.png‎|thumb|left|230px| Figure 4: Adaptive grid sampling of the parameter space for a modeled tumor. Green dots show sampling points. Red and yellow lines show isolines of tumor cell infiltration. (Also see text above.)]]<br /> <br /> [[Image:Results_Tumor-Model.png|thumb|left|500px| Figure 5: MCMC sampling results for 'D' and 'rho' using different synthetic and real data sets. The proposed method (green samples) shows less variation when compared to the standard sampling approach (blue) and is at least as close to the ground truth (pink). (Also see text above.) ]]<br /> <br /> == Processing magnetic resonance spectroscopic images ==<br /> [[Image:Sivic_slicer_slc_mets.jpg|thumb|left|300px| Figure 6: MRSI metabolite maps generated in Slicer-SIVIC module referenced to anatomical (FLAIR) image.]]<br /> <br /> To make the metabolic information of magnetic resonance spectroscopic images available for modeling the evolution of glioma growth we are implementing an [http://wiki.na-mic.org/Wiki/index.php/2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface MRSI processing module] for Slicer jointly with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] project at UCSF. <br /> <br /> We envision that physiological tumor models may be used to interpret MRSI, integrating the highly specific metabolic information of the spectroscopic images with other clinical MRI modalities in a principled fashion.<br /> <br /> = Key Investigators =<br /> * MIT: [http://people.csail.mit.edu/menze Bjoern Menze], [http://people.csail.mit.edu/tammy Tammy Riklin Raviv], [http://people.csail.mit.edu/koen Koen Van Leemput], [http://people.csail.mit.edu/polina Polina Golland]<br /> * Harvard: William M. Wells<br /> * INRIA Sophia-Antipolis, France: Ezequiel Geremia, Olivier Clatz, Nicholas Ayache<br /> * Microsoft Research, UK: Ender Konukoglu<br /> * DKFZ Heidelberg, Germany: Bram Stieltjes, Marc-Andre Weber<br /> <br /> = Publications =<br /> *[http://www.na-mic.org/publications/pages/display?search=Projects%3ATumorModeling&amp;submit=Search&amp;words=all&amp;title=checked&amp;keywords=checked&amp;authors=checked&amp;abstract=checked&amp;sponsors=checked&amp;searchbytag=checked| NA-MIC Publications Database on brain tumor segmentation and modeling]</div>

Bmenze https://www.na-mic.org/w/index.php?title=Projects:TumorModeling&diff=65603 2011-03-25T14:20:52Z

<p>Bmenze: /* Image-based modeling of tumor growth */</p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]<br /> __NOTOC__<br /> = Modeling tumor growth in patients with glioma =<br /> [[Image:Multimodal_glioma.png|thumb|left|300px| Figure 1: Multi-modal image data from a patient with low-grade glioma. A large number of different modalities and derived parameter volumes are acquired during the monitoring of tumor growth.]]<br /> <br /> We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth.<br /> <br /> This aims at two directions: First, it aims at making complex information from longitudinal multi-modal data set accessible for diagnostic radiology through physiological models. This will allow us to estimate features such as degree of infiltration, speed of growth, or mass effect in a quantitative fashion; for therapy it will allow us to identify regions at risk for progression. Second, it aims at providing the means to test different macroscopic tumor models from theoretical biology on real clinical data.<br /> <br /> The project has three main aims: 1) the automated segmentation of tumors in large multi-modal image data sets to make information of different MR image modalities accessible for the tumor model, 2) the development of methods for the image-based estimation of parameters in reaction-diffusion type models of tumor growth, and 3) the processing and analysis of magnetic resonance spectroscopic images (MRSI) as a potential application of the tumor model.<br /> <br /> <br /> == Segmenting tumors in large multimodal data sets ==<br /> [[Image:Tumor_segmentation_lesion_atlas.png|thumb|left|300px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]<br /> <br /> To segment all MR image volumes available for a patient we developed an approach for learning patient-specific lesion atlases (Figure 2) with limited user interaction. Figure 2 shows the manual segmentation of the tumor from different raters (red, green, blue) and the automatic segmentation using the patient-specific lesion atlas (black) in T1-MRI, T1-MRI and the fractional anisotropy map from DTI. <br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Lashkari D, Weber MA, Ayache N and Golland P. A generative model for brain tumor segmentation in multi-modal images. Proc MICCAI 2010. LNCS 6362, 151-59''<br /> <br /> <br /> == Image-based modeling of tumor growth ==<br /> [[Image:fisher-state-space_tumor-shapes.png|thumb|left|300px| Figure 3: Variation of tumor shapes for different parameterizations of the Fisher-Kolmogorov tumor model. All tumors have the same size, they vary in Diffusivity 'D' and proliferation 'rho'. In our approach this shape information is used to infer general properties of the tumor.]]<br /> <br /> We propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a Fisher-Kolmogorov reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.<br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Honkela A, Konukoglu E, Weber MA, Ayache N and Golland P. A generative approach for image-based modeling of tumor growth. Proc IPMI 2011. LNCS.''<br /> <br /> === Inverse Modeling ===<br /> Figure 4 illustrates the adaptive grid sampling of the parameter space for a modeled tumor. The size of the green sampling points indicates how often the indicated location parameter of the tumor model was evaluated under different parametrizations. The ground truth is indicated by the pink cross. Most adaptively chosen sampling points are close to the ground truth. The figure also shows isolines of tumor cell density (red), the predicted extensions of the T2 hyper-intense area (yellow) and tissue boundaries (black).<br /> <br /> Figure 5 shows results of the proposed approach: Green samples are obtained from the proposed sparse grid approach while blue sample are obtained via standard MCMC. Black circles indicate means of the two distributions. Ground truth for A and B are indicated by the pink cross. In D the previously estimated speed of growth [7] is shown by the pink line. The sparse grid sampling approximation performs better than the direct MCMC (A-B). Estimates correlate well with priviously published results, but provide a more accurate characterization of the state of disease (D).<br /> <br /> <br /> [[Image:Sampling_spatial-parameters.png‎|thumb|left|230px| Figure 4: Adaptive grid sampling of the parameter space for a modeled tumor. Green dots show sampling points. Red and yellow lines show isolines of tumor cell infiltration. (Also see text above.)]]<br /> <br /> [[Image:Results_Tumor-Model.png|thumb|left|500px| Figure 5: MCMC sampling results for 'D' and 'rho' using different synthetic and real data sets. The proposed method (green samples) shows less variation when compared to the standard sampling approach (blue) and is at least as close to the ground truth (pink). (Also see text above.) ]]<br /> <br /> == Processing magnetic resonance spectroscopic images ==<br /> [[Image:Sivic_slicer_slc_mets.jpg|thumb|left|300px| Figure 6: MRSI metabolite maps generated in Slicer-SIVIC module referenced to anatomical (FLAIR) image.]]<br /> <br /> To make the metabolic information of magnetic resonance spectroscopic images available for modeling the evolution of glioma growth we are implementing an [http://wiki.na-mic.org/Wiki/index.php/2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface MRSI processing module] for Slicer jointly with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] project at UCSF. <br /> <br /> We envision that physiological tumor models may be used to interpret MRSI, integrating the highly specific metabolic information of the spectroscopic images with other clinical MRI modalities in a principled fashion.<br /> <br /> = Key Investigators =<br /> * MIT: [http://people.csail.mit.edu/menze Bjoern Menze], [http://people.csail.mit.edu/tammy Tammy Riklin Raviv], [http://people.csail.mit.edu/koen Koen Van Leemput], [http://people.csail.mit.edu/polina Polina Golland]<br /> * Harvard: William M. Wells<br /> * INRIA Sophia-Antipolis, France: Ezequiel Geremia, Olivier Clatz, Nicholas Ayache<br /> * Microsoft Research, UK: Ender Konukoglu<br /> * DKFZ Heidelberg, Germany: Bram Stieltjes, Marc-Andre Weber<br /> <br /> = Publications =<br /> *[http://www.na-mic.org/publications/pages/display?search=Projects%3ATumorModeling&amp;submit=Search&amp;words=all&amp;title=checked&amp;keywords=checked&amp;authors=checked&amp;abstract=checked&amp;sponsors=checked&amp;searchbytag=checked| NA-MIC Publications Database on brain tumor segmentation and modeling]</div>

Bmenze https://www.na-mic.org/w/index.php?title=Projects:TumorModeling&diff=65602 2011-03-25T14:14:54Z

<p>Bmenze: </p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]<br /> __NOTOC__<br /> = Modeling tumor growth in patients with glioma =<br /> [[Image:Multimodal_glioma.png|thumb|left|300px| Figure 1: Multi-modal image data from a patient with low-grade glioma. A large number of different modalities and derived parameter volumes are acquired during the monitoring of tumor growth.]]<br /> <br /> We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth.<br /> <br /> This aims at two directions: First, it aims at making complex information from longitudinal multi-modal data set accessible for diagnostic radiology through physiological models. This will allow us to estimate features such as degree of infiltration, speed of growth, or mass effect in a quantitative fashion; for therapy it will allow us to identify regions at risk for progression. Second, it aims at providing the means to test different macroscopic tumor models from theoretical biology on real clinical data.<br /> <br /> The project has three main aims: 1) the automated segmentation of tumors in large multi-modal image data sets to make information of different MR image modalities accessible for the tumor model, 2) the development of methods for the image-based estimation of parameters in reaction-diffusion type models of tumor growth, and 3) the processing and analysis of magnetic resonance spectroscopic images (MRSI) as a potential application of the tumor model.<br /> <br /> <br /> == Segmenting tumors in large multimodal data sets ==<br /> [[Image:Tumor_segmentation_lesion_atlas.png|thumb|left|300px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]<br /> <br /> To segment all MR image volumes available for a patient we developed an approach for learning patient-specific lesion atlases (Figure 2) with limited user interaction. Figure 2 shows the manual segmentation of the tumor from different raters (red, green, blue) and the automatic segmentation using the patient-specific lesion atlas (black) in T1-MRI, T1-MRI and the fractional anisotropy map from DTI. <br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Lashkari D, Weber MA, Ayache N and Golland P. A generative model for brain tumor segmentation in multi-modal images. Proc MICCAI 2010. LNCS 6362, 151-59''<br /> <br /> <br /> == Image-based modeling of tumor growth ==<br /> [[Image:fisher-state-space_tumor-shapes.png|thumb|left|300px| Figure 3: Variation of tumor shapes for different parameterizations of the Fisher-Kolmogorov tumor model. All tumors have the same size, they vary in Diffusivity 'D' and proliferation 'rho'. In our approach this shape information is used to infer general properties of the tumor.]]<br /> <br /> We propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a Fisher-Kolmogorov reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.<br /> <br /> Figure 4 illustrates the adaptive grid sampling of the parameter space for a modeled tumor. The size of the green sampling points indicates how often the indicated location parameter of the tumor model was evaluated under different parametrizations. The ground truth is indicated by the pink cross. Most adaptively chosen sampling points are close to the ground truth. The figure also shows isolines of tumor cell density (red), the predicted extensions of the T2 hyper-intense area (yellow) and tissue boundaries (black).<br /> <br /> Figure 5 shows results of the proposed approach: Green samples are obtained from the proposed sparse grid approach while blue sample are obtained via standard MCMC. Black circles indicate means of the two distributions. Ground truth for A and B are indicated by the pink cross. In D the previously estimated speed of growth [7] is shown by the pink line. The sparse grid sampling approximation performs better than the direct MCMC (A-B). Estimates correlate well with priviously published results, but provide a more accurate characterization of the state of disease (D).<br /> <br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Honkela A, Konukoglu E, Weber MA, Ayache N and Golland P. A generative approach for image-based modeling of tumor growth. Proc IPMI 2011. LNCS.''<br /> <br /> <br /> [[Image:Sampling_spatial-parameters.png‎|thumb|left|230px| Figure 4: Adaptive grid sampling of the parameter space for a modeled tumor. Green dots show sampling points. Red and yellow lines show isolines of tumor cell infiltration. (Also see text above.)]]<br /> <br /> [[Image:Results_Tumor-Model.png|thumb|left|500px| Figure 5: MCMC sampling results for 'D' and 'rho' using different synthetic and real data sets. The proposed method (green samples) shows less variation when compared to the standard sampling approach (blue) and is at least as close to the ground truth (pink). (Also see text above.) ]]<br /> <br /> == Processing magnetic resonance spectroscopic images ==<br /> [[Image:Sivic_slicer_slc_mets.jpg|thumb|left|300px| Figure 6: MRSI metabolite maps generated in Slicer-SIVIC module referenced to anatomical (FLAIR) image.]]<br /> <br /> To make the metabolic information of magnetic resonance spectroscopic images available for modeling the evolution of glioma growth we are implementing an [http://wiki.na-mic.org/Wiki/index.php/2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface MRSI processing module] for Slicer jointly with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] project at UCSF. <br /> <br /> We envision that physiological tumor models may be used to interpret MRSI, integrating the highly specific metabolic information of the spectroscopic images with other clinical MRI modalities in a principled fashion.<br /> <br /> = Key Investigators =<br /> * MIT: [http://people.csail.mit.edu/menze Bjoern Menze], [http://people.csail.mit.edu/tammy Tammy Riklin Raviv], [http://people.csail.mit.edu/koen Koen Van Leemput], [http://people.csail.mit.edu/polina Polina Golland]<br /> * Harvard: William M. Wells<br /> * INRIA Sophia-Antipolis, France: Ezequiel Geremia, Olivier Clatz, Nicholas Ayache<br /> * Microsoft Research, UK: Ender Konukoglu<br /> * DKFZ Heidelberg, Germany: Bram Stieltjes, Marc-Andre Weber<br /> <br /> = Publications =<br /> *[http://www.na-mic.org/publications/pages/display?search=Projects%3ATumorModeling&amp;submit=Search&amp;words=all&amp;title=checked&amp;keywords=checked&amp;authors=checked&amp;abstract=checked&amp;sponsors=checked&amp;searchbytag=checked| NA-MIC Publications Database on brain tumor segmentation and modeling]</div>

Bmenze https://www.na-mic.org/w/index.php?title=Projects:TumorModeling&diff=65601 2011-03-25T14:08:47Z

<p>Bmenze: </p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]<br /> __NOTOC__<br /> = Modeling tumor growth in patients with glioma =<br /> [[Image:Multimodal_glioma.png|thumb|left|300px| Figure 1: Multi-modal image data from a patient with low-grade glioma. A large number of different modalities and derived parameter volumes are acquired during the monitoring of tumor growth.]]<br /> <br /> We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth.<br /> <br /> This aims at two directions: First, it aims at making complex information from longitudinal multi-modal data set accessible for diagnostic radiology through physiological models. This will allow us to estimate features such as degree of infiltration, speed of growth, or mass effect in a quantitative fashion; for therapy it will allow us to identify regions at risk for progression. Second, it aims at providing the means to test different macroscopic tumor models from theoretical biology on real clinical data.<br /> <br /> The project has three main aims: 1) the automated segmentation of tumors in large multi-modal image data sets to make information of different MR image modalities accessible for the tumor model, 2) the development of methods for the image-based estimation of parameters in reaction-diffusion type models of tumor growth, and 3) the processing and analysis of magnetic resonance spectroscopic images (MRSI) as a potential application of the tumor model.<br /> <br /> <br /> == Segmenting tumors in large multimodal data sets ==<br /> [[Image:Tumor_segmentation_lesion_atlas.png|thumb|left|300px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]<br /> <br /> To segment all MR image volumes available for a patient we developed an approach for learning patient-specific lesion atlases (Figure 2) with limited user interaction. Figure 2 shows the manual segmentation of the tumor from different raters (red, green, blue) and the automatic segmentation using the patient-specific lesion atlas (black) in T1-MRI, T1-MRI and the fractional anisotropy map from DTI. <br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Lashkari D, Weber MA, Ayache N and Golland P. A generative model for brain tumor segmentation in multi-modal images. Proc MICCAI 2010. LNCS 6362, 151-59''<br /> <br /> <br /> == Image-based modeling of tumor growth ==<br /> [[Image:fisher-state-space_tumor-shapes.png|thumb|left|300px| Figure 3: Variation of tumor shapes for different parameterizations of the Fisher-Kolmogorov tumor model. All tumors have the same size, they vary in Diffusivity 'D' and proliferation 'rho'. In our approach this shape information is used to infer general properties of the tumor.]]<br /> <br /> We propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a Fisher-Kolmogorov reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.<br /> <br /> Figure 4 illustrates the adaptive grid sampling of the parameter space for a modeled tumor. The size of the green sampling points indicates how often the indicated location parameter of the tumor model was evaluated under different parametrizations. The ground truth is indicated by the pink cross. Most adaptively chosen sampling points are close to the ground truth. The figure also shows isolines of tumor cell density (red), the predicted extensions of the T2 hyper-intense area (yellow) and tissue boundaries (black).<br /> <br /> Figure 5 shows results of the proposed approach: Green samples are obtained from the proposed sparse grid approach while blue sample are obtained via standard MCMC. Black circles indicate means of the two distributions. Ground truth for A and B are indicated by the pink cross. In D the previously estimated speed of growth [7] is shown by the pink line. The sparse grid sampling approximation performs better than the direct MCMC (A-B). Estimates correlate well with priviously published results, but provide a more accurate characterization of the state of disease (D).<br /> <br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Honkela A, Konukoglu E, Weber MA, Ayache N and Golland P. A generative approach for image-based modeling of tumor growth. Proc IPMI 2011. LNCS.''<br /> <br /> <br /> [[Image:Sampling_spatial-parameters.png‎|thumb|left|230px| Figure 4: Adaptive grid sampling of the parameter space for a modeled tumor. Green dots show sampling points. Red and yellow lines show isolines of tumor cell infiltration. (Also see text above.)]]<br /> <br /> [[Image:Results_Tumor-Model.png|thumb|left|500px| Figure 5: MCMC sampling results for 'D' and 'rho' using different synthetic and real data sets. The proposed method (green samples) shows less variation when compared to the standard sampling approach (blue) and is at least as close to the ground truth (pink). (Also see text above.) ]]<br /> <br /> == Processing magnetic resonance spectroscopic images ==<br /> [[Image:Sivic_slicer_slc_mets.jpg|thumb|left|300px| Figure 6: MRSI metabolite maps generated in Slicer-SIVIC module referenced to anatomical (FLAIR) image.]]<br /> <br /> To make the metabolic information of magnetic resonance spectroscopic images available for modeling the evolution of glioma growth we are implementing an [http://wiki.na-mic.org/Wiki/index.php/2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface MRSI processing module] for Slicer jointly with the [http://sourceforge.net/apps/trac/sivic/|SIVI] project. <br /> <br /> We envision that physiological tumor models may be used to interpret MRSI -- integrating the highly specific metabolic information of the spectroscopic images with other clinical MRI modalities in a principled fashion.<br /> <br /> = Key Investigators =<br /> * MIT: [http://people.csail.mit.edu/menze Bjoern Menze], [http://people.csail.mit.edu/tammy Tammy Riklin Raviv], [http://people.csail.mit.edu/koen Koen Van Leemput], [http://people.csail.mit.edu/polina Polina Golland]<br /> * Harvard: William M. Wells<br /> * INRIA Sophia-Antipolis, France: Ezequiel Geremia, Olivier Clatz, Nicholas Ayache<br /> * DKFZ Heidelberg, Germany: Bram Stieltjes, Marc-Andre Weber<br /> <br /> = Publications =<br /> *[http://www.na-mic.org/publications/pages/display?search=Projects%3ATumorModeling&amp;submit=Search&amp;words=all&amp;title=checked&amp;keywords=checked&amp;authors=checked&amp;abstract=checked&amp;sponsors=checked&amp;searchbytag=checked| NA-MIC Publications Database on brain tumor segmentation and modeling]</div>

Bmenze https://www.na-mic.org/w/index.php?title=Projects:TumorModeling&diff=65600 2011-03-25T13:55:31Z

<p>Bmenze: /* Image-based modeling of tumor growth */</p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]<br /> __NOTOC__<br /> = Modeling tumor growth in patients with glioma =<br /> <br /> [[Image:Multimodal_glioma.png|thumb|left|350px| Figure 1: Multi-modal image data from a patient with low-grade glioma. A large number of different modalities and derived parameter volumes are acquired during the monitoring of tumor growth.]]<br /> <br /> We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth.<br /> <br /> <br /> This aims at two directions: First, it aims at making complex information from longitudinal multimodal data set accessible for diagnostic radiology through physiological models. This will allow to estimate features such as degree of infiltration, speed of growth, or mass effect in a quantitative fashion; for therapy it will allow to identify regions at risk for progression. Second, it aims at providing the means to test different macroscopic tumor models from theoretical biology on real clinical data.<br /> <br /> <br /> To realize these aims, the project comprises a number of aspects -- automated segmentation of tumors in large multimodal image data sets, making information of different MR image modalities accessible for the tumor model, with a focus on the processing of magnetic resonance spectroscopic images (MRSI), and the development of methods for the image-based estimation of parameters in reaction-diffusion type models of tumor growth.<br /> <br /> <br /> <br /> == Segmenting tumors in large multimodal data sets ==<br /> [[Image:Tumor_segmentation_lesion_atlas.png|thumb|left|300px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]<br /> <br /> To segment all MR image volumes available for a patient we developed an approach for learning patient-specific lesion atlases (Figure 2) with limited user interaction. Figure 2 shows the manual segmentation of the tumor from different raters (red, green, blue) and the automatic segmentation using the patient-specific lesion atlas (black) in T1-MRI, T1-MRI and the fractional anisotropy map from DTI. <br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Lashkari D, Weber MA, Ayache N and Golland P. A generative model for brain tumor segmentation in multi-modal images. Proc MICCAI 2010. LNCS 6362, 151-59''<br /> <br /> <br /> == Image-based modeling of tumor growth ==<br /> [[Image:fisher-state-space_tumor-shapes.png|thumb|left|300px| Figure 3: Variation of tumor shapes for different parameterizations of the Fisher-Kolmogorov tumor model. All tumors have the same size, they vary in Diffusivity 'D' and proliferation 'rho'. In our approach this shape information is used to infer general properties of the tumor.]]<br /> <br /> We propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a Fisher-Kolmogorov reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.<br /> <br /> Figure 4 illustrates the adaptive grid sampling of the parameter space for a modeled tumor. The size of the green sampling points indicates how often the indicated location parameter of the tumor model was evaluated under different parametrizations. The ground truth is indicated by the pink cross. Most adaptively chosen sampling points are close to the ground truth. The figure also shows isolines of tumor cell density (red), the predicted extensions of the T2 hyper-intense area (yellow) and tissue boundaries (black).<br /> <br /> Figure 5 shows results of the proposed approach: Green samples are obtained from the proposed sparse grid approach while blue sample are obtaine via standard MCMC. Black circles indicate means of the two distributions. Ground truth for A and B are indicated by the pink cross. In D the previously estimated speed of growth [7] is shown by the pink line. The sparse grid sampling approximation performs better than the direct MCMC (A-B). Estimates correlate well with privously published results, but provide a more accurate characterization of the state of disease (D).<br /> <br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Honkela A, Konukoglu E, Weber MA, Ayache N and Golland P. A generative approach for image-based modeling of tumor growth. Proc IPMI 2011. LNCS.''<br /> <br /> <br /> [[Image:Sampling_spatial-parameters.png‎|thumb|left|200px| Figure 4: Adaptive grid sampling of the parameter space for a modeled tumor. Green dots show sampling points. Red and yellow lines show isolines of tumor cell infiltration. (Also see text above.)]]<br /> <br /> [[Image:Results_Tumor-Model.png|thumb|left|500px| Figure 5: MCMC sampling results for 'D' and 'rho' using different synthetic and real data sets. The proposed method (green samples) shows less variation when compared to the standard sampling approach (blue) and is at least as close to the ground truth (pink). (Also see text above.) ]]<br /> <br /> == Processing magnetic resonance spectroscopic images ==<br /> [[Image:Sivic_slicer_slc_mets.jpg|thumb|left|300px| Figure 5: MRSI metabolite maps generated in Slicer-SIVIC module referenced to anatomical (FLAIR) image.]]<br /> <br /> To make the metabolic information of magnetic resonance spectroscopic images available for modeling the evolution of glioma growth we are implementing an [http://wiki.na-mic.org/Wiki/index.php/2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface MRSI processing module] for Slicer.<br /> <br /> = Key Investigators =<br /> * MIT: [http://people.csail.mit.edu/menze Bjoern Menze], [http://people.csail.mit.edu/tammy Tammy Riklin Raviv], [http://people.csail.mit.edu/koen Koen Van Leemput], [http://people.csail.mit.edu/polina Polina Golland]<br /> * Harvard: William M. Wells<br /> * INRIA Sophia-Antipolis, France: Ezequiel Geremia, Olivier Clatz, Nicholas Ayache<br /> * DKFZ Heidelberg, Germany: Bram Stieltjes, Marc-Andre Weber<br /> <br /> = Publications =<br /> *[http://www.na-mic.org/publications/pages/display?search=Projects%3ATumorModeling&amp;submit=Search&amp;words=all&amp;title=checked&amp;keywords=checked&amp;authors=checked&amp;abstract=checked&amp;sponsors=checked&amp;searchbytag=checked| NA-MIC Publications Database on brain tumor segmentation and modeling]</div>

Bmenze https://www.na-mic.org/w/index.php?title=File:Sampling_spatial-parameters.png&diff=65599 2011-03-25T13:45:52Z

<p>Bmenze: Adaptive sampling of the parameter space for the synthetic high-grade data set. Sampling points (green) for the {x; y} coordinates of the initial tumor growth location. The figure also shows isolines of tumor cell density (red), the predicted extensions o</p> <hr /> <div>Adaptive sampling of the parameter space for the synthetic high-grade data<br /> set. Sampling points (green) for the {x; y} coordinates of the initial tumor growth location. The figure also shows isolines of tumor cell density (red), the predicted extensions of the T2 hyper-intense area (yellow) and tissue boundaries (black). The size of the green sampling points indicates how often the indicated parameter was evaluated under different combinations. The ground truth is indicated by the pink cross. Most adaptively chosen sampling points are close to the ground truth.</div>

Bmenze https://www.na-mic.org/w/index.php?title=Projects:TumorModeling&diff=65590 2011-03-24T22:56:20Z

<p>Bmenze: /* Processing magnetic resonance spectroscopic images */</p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]<br /> __NOTOC__<br /> = Modeling tumor growth in patients with glioma =<br /> <br /> [[Image:Multimodal_glioma.png|thumb|left|350px| Figure 1: Multi-modal image data from a patient with low-grade glioma. A large number of different modalities and derived parameter volumes are acquired during the monitoring of tumor growth.]]<br /> <br /> We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth.<br /> <br /> <br /> This aims at two directions: First, it aims at making complex information from longitudinal multimodal data set accessible for diagnostic radiology through physiological models. This will allow to estimate features such as degree of infiltration, speed of growth, or mass effect in a quantitative fashion; for therapy it will allow to identify regions at risk for progression. Second, it aims at providing the means to test different macroscopic tumor models from theoretical biology on real clinical data.<br /> <br /> <br /> To realize these aims, the project comprises a number of aspects -- automated segmentation of tumors in large multimodal image data sets, making information of different MR image modalities accessible for the tumor model, with a focus on the processing of magnetic resonance spectroscopic images (MRSI), and the development of methods for the image-based estimation of parameters in reaction-diffusion type models of tumor growth.<br /> <br /> <br /> <br /> == Segmenting tumors in large multimodal data sets ==<br /> [[Image:Tumor_segmentation_lesion_atlas.png|thumb|left|300px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]<br /> <br /> To segment all MR image volumes available for a patient we developed an approach for learning patient-specific lesion atlases (Figure 2) with limited user interaction. Figure 2 shows the manual segmentation of the tumor from different raters (red, green, blue) and the automatic segmentation using the patient-specific lesion atlas (black) in T1-MRI, T1-MRI and the fractional anisotropy map from DTI. <br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Lashkari D, Weber MA, Ayache N and Golland P. A generative model for brain tumor segmentation in multi-modal images. Proc MICCAI 2010. LNCS 6362, 151-59''<br /> <br /> <br /> == Image-based modeling of tumor growth ==<br /> [[Image:fisher-state-space_tumor-shapes.png|thumb|left|300px| Figure 3: Variation of tumor shapes for different parameterizations of the Fisher-Kolmogorov tumor model. All tumors have the same size, they vary in Diffusivity 'D' and proliferation 'rho'. In our approach this shape information is used to infer general properties of the tumor.]]<br /> <br /> We propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a Fisher-Kolmogorov reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.<br /> <br /> Figure 4 illustrates results of the proposed approach: Green samples are obtained from the proposed sparse grid approach while blue sample are obtaine via standard MCMC. Black circles indicate means of the two distributions. Ground truth for A and B are indicated by the pink cross. In D the previously estimated speed of growth [7] is shown by the pink line. The sparse grid sampling approximation performs better than the direct MCMC (A-B). Estimates correlate well with privously published results, but provide a more accurate characterization of the state of disease (D).<br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Honkela A, Konukoglu E, Weber MA, Ayache N and Golland P. A generative approach for image-based modeling of tumor growth. Proc IPMI 2011. LNCS.''<br /> <br /> [[Image:Results_Tumor-Model.png|thumb|left|800px| Figure 4: MCMC sampling results for 'D' and 'rho' using different synthetic and real data sets. The proposed method (green samples) shows less variation when compared to the standard sampling approach (blue) and is at least as close to the ground truth (pink). (Also see text above.) ]]<br /> <br /> == Processing magnetic resonance spectroscopic images ==<br /> [[Image:Sivic_slicer_slc_mets.jpg|thumb|left|300px| Figure 5: MRSI metabolite maps generated in Slicer-SIVIC module referenced to anatomical (FLAIR) image.]]<br /> <br /> To make the metabolic information of magnetic resonance spectroscopic images available for modeling the evolution of glioma growth we are implementing an [http://wiki.na-mic.org/Wiki/index.php/2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface MRSI processing module] for Slicer.<br /> <br /> = Key Investigators =<br /> * MIT: [http://people.csail.mit.edu/menze Bjoern Menze], [http://people.csail.mit.edu/tammy Tammy Riklin Raviv], [http://people.csail.mit.edu/koen Koen Van Leemput], [http://people.csail.mit.edu/polina Polina Golland]<br /> * Harvard: William M. Wells<br /> * INRIA Sophia-Antipolis, France: Ezequiel Geremia, Olivier Clatz, Nicholas Ayache<br /> * DKFZ Heidelberg, Germany: Bram Stieltjes, Marc-Andre Weber<br /> <br /> = Publications =<br /> *[http://www.na-mic.org/publications/pages/display?search=Projects%3ATumorModeling&amp;submit=Search&amp;words=all&amp;title=checked&amp;keywords=checked&amp;authors=checked&amp;abstract=checked&amp;sponsors=checked&amp;searchbytag=checked| NA-MIC Publications Database on brain tumor segmentation and modeling]</div>

Bmenze https://www.na-mic.org/w/index.php?title=Projects:TumorModeling&diff=65589 2011-03-24T22:55:29Z

<p>Bmenze: /* Image-based modeling of tumor growth */</p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]<br /> __NOTOC__<br /> = Modeling tumor growth in patients with glioma =<br /> <br /> [[Image:Multimodal_glioma.png|thumb|left|350px| Figure 1: Multi-modal image data from a patient with low-grade glioma. A large number of different modalities and derived parameter volumes are acquired during the monitoring of tumor growth.]]<br /> <br /> We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth.<br /> <br /> <br /> This aims at two directions: First, it aims at making complex information from longitudinal multimodal data set accessible for diagnostic radiology through physiological models. This will allow to estimate features such as degree of infiltration, speed of growth, or mass effect in a quantitative fashion; for therapy it will allow to identify regions at risk for progression. Second, it aims at providing the means to test different macroscopic tumor models from theoretical biology on real clinical data.<br /> <br /> <br /> To realize these aims, the project comprises a number of aspects -- automated segmentation of tumors in large multimodal image data sets, making information of different MR image modalities accessible for the tumor model, with a focus on the processing of magnetic resonance spectroscopic images (MRSI), and the development of methods for the image-based estimation of parameters in reaction-diffusion type models of tumor growth.<br /> <br /> <br /> <br /> == Segmenting tumors in large multimodal data sets ==<br /> [[Image:Tumor_segmentation_lesion_atlas.png|thumb|left|300px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]<br /> <br /> To segment all MR image volumes available for a patient we developed an approach for learning patient-specific lesion atlases (Figure 2) with limited user interaction. Figure 2 shows the manual segmentation of the tumor from different raters (red, green, blue) and the automatic segmentation using the patient-specific lesion atlas (black) in T1-MRI, T1-MRI and the fractional anisotropy map from DTI. <br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Lashkari D, Weber MA, Ayache N and Golland P. A generative model for brain tumor segmentation in multi-modal images. Proc MICCAI 2010. LNCS 6362, 151-59''<br /> <br /> <br /> == Image-based modeling of tumor growth ==<br /> [[Image:fisher-state-space_tumor-shapes.png|thumb|left|300px| Figure 3: Variation of tumor shapes for different parameterizations of the Fisher-Kolmogorov tumor model. All tumors have the same size, they vary in Diffusivity 'D' and proliferation 'rho'. In our approach this shape information is used to infer general properties of the tumor.]]<br /> <br /> We propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a Fisher-Kolmogorov reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.<br /> <br /> Figure 4 illustrates results of the proposed approach: Green samples are obtained from the proposed sparse grid approach while blue sample are obtaine via standard MCMC. Black circles indicate means of the two distributions. Ground truth for A and B are indicated by the pink cross. In D the previously estimated speed of growth [7] is shown by the pink line. The sparse grid sampling approximation performs better than the direct MCMC (A-B). Estimates correlate well with privously published results, but provide a more accurate characterization of the state of disease (D).<br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Honkela A, Konukoglu E, Weber MA, Ayache N and Golland P. A generative approach for image-based modeling of tumor growth. Proc IPMI 2011. LNCS.''<br /> <br /> [[Image:Results_Tumor-Model.png|thumb|left|800px| Figure 4: MCMC sampling results for 'D' and 'rho' using different synthetic and real data sets. The proposed method (green samples) shows less variation when compared to the standard sampling approach (blue) and is at least as close to the ground truth (pink). (Also see text above.) ]]<br /> <br /> == Processing magnetic resonance spectroscopic images ==<br /> [[Image:Sivic_slicer_slc_mets.jpg|thumb|left|350px| Figure 4: MRSI metabolite maps generated in Slicer-SIVIC module referenced to anatomical (FLAIR) image.]]<br /> <br /> To make the metabolic information of magnetic resonance spectroscopic images available for modeling the evolution of glioma growth we are implementing an [http://wiki.na-mic.org/Wiki/index.php/2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface MRSI processing module] for Slicer.<br /> <br /> = Key Investigators =<br /> * MIT: [http://people.csail.mit.edu/menze Bjoern Menze], [http://people.csail.mit.edu/tammy Tammy Riklin Raviv], [http://people.csail.mit.edu/koen Koen Van Leemput], [http://people.csail.mit.edu/polina Polina Golland]<br /> * Harvard: William M. Wells<br /> * INRIA Sophia-Antipolis, France: Ezequiel Geremia, Olivier Clatz, Nicholas Ayache<br /> * DKFZ Heidelberg, Germany: Bram Stieltjes, Marc-Andre Weber<br /> <br /> = Publications =<br /> *[http://www.na-mic.org/publications/pages/display?search=Projects%3ATumorModeling&amp;submit=Search&amp;words=all&amp;title=checked&amp;keywords=checked&amp;authors=checked&amp;abstract=checked&amp;sponsors=checked&amp;searchbytag=checked| NA-MIC Publications Database on brain tumor segmentation and modeling]</div>

Bmenze https://www.na-mic.org/w/index.php?title=File:Results_Tumor-Model.png&diff=65582 2011-03-24T22:47:54Z

<p>Bmenze: MCMC sampling results in the space spanned by model parameters D and rho, for the four experiments. Green samples are obtained from the sparse grid interpolation Eq. (12), blue-purple samples come from the direct sampling in Eq. (10). Black circles indica</p> <hr /> <div>MCMC sampling results in the space spanned by model parameters D and rho, for the four experiments. Green samples are obtained from the sparse grid interpolation Eq. (12), blue-purple samples come from the direct sampling in Eq. (10). Black circles indicate means of the two distributions. Ground truth for A and B are indicated by the pink cross. In D the previously estimated speed of growth [7] is shown by the pink line. The sparse grid sampling approximation performs better than the direct MCMC (A-B). Estimates correlate well with results from [7], but provide a more accurate characterization of the state of disease (D).</div>

Bmenze https://www.na-mic.org/w/index.php?title=Projects:TumorModeling&diff=65484 2011-03-23T21:47:28Z

<p>Bmenze: /* Modeling tumor growth in patients with glioma */</p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]<br /> __NOTOC__<br /> = Modeling tumor growth in patients with glioma =<br /> <br /> [[Image:Multimodal_glioma.png|thumb|left|350px| Figure 1: Multi-modal image data from a patient with low-grade glioma. A large number of different modalities and derived parameter volumes are acquired during the monitoring of tumor growth.]]<br /> <br /> We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth.<br /> <br /> <br /> This aims at two directions: First, it aims at making complex information from longitudinal multimodal data set accessible for diagnostic radiology through physiological models. This will allow to estimate features such as degree of infiltration, speed of growth, or mass effect in a quantitative fashion; for therapy it will allow to identify regions at risk for progression. Second, it aims at providing the means to test different macroscopic tumor models from theoretical biology on real clinical data.<br /> <br /> <br /> To realize these aims, the project comprises a number of aspects -- automated segmentation of tumors in large multimodal image data sets, making information of different MR image modalities accessible for the tumor model, with a focus on the processing of magnetic resonance spectroscopic images (MRSI), and the development of methods for the image-based estimation of parameters in reaction-diffusion type models of tumor growth.<br /> <br /> <br /> <br /> == Segmenting tumors in large multimodal data sets ==<br /> [[Image:Tumor_segmentation_lesion_atlas.png|thumb|left|300px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]<br /> <br /> To segment all MR image volumes available for a patient we developed an approach for learning patient-specific lesion atlases (Figure 2) with limited user interaction. Figure 2 shows the manual segmentation of the tumor from different raters (red, green, blue) and the automatic segmentation using the patient-specific lesion atlas (black) in T1-MRI, T1-MRI and the fractional anisotropy map from DTI. <br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Lashkari D, Weber MA, Ayache N and Golland P. A generative model for brain tumor segmentation in multi-modal images. Proc MICCAI 2010. LNCS 6362, 151-59''<br /> <br /> <br /> == Image-based modeling of tumor growth ==<br /> [[Image:fisher-state-space_tumor-shapes.png|thumb|left|300px| Figure 3: Variation of tumor shapes for different parameterizations of the Fisher-Kolmogorov tumor model. All tumors have the same size, they vary in Diffusivity 'D' and proliferation 'rho'. In our approach this shape information is used to infer general properties of the tumor.]]<br /> <br /> We propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a Fisher-Kolmogorov reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.<br /> <br /> '''Reference:''' ''Menze BH, Van Leemput K, Honkela A, Konukoglu E, Weber MA, Ayache N and Golland P. A generative approach for image-based modeling of tumor growth. Proc IPMI 2011. LNCS, to appear.''<br /> <br /> <br /> <br /> == Processing magnetic resonance spectroscopic images ==<br /> [[Image:Sivic_slicer_slc_mets.jpg|thumb|left|350px| Figure 4: MRSI metabolite maps generated in Slicer-SIVIC module referenced to anatomical (FLAIR) image.]]<br /> <br /> To make the metabolic information of magnetic resonance spectroscopic images available for modeling the evolution of glioma growth we are implementing an [http://wiki.na-mic.org/Wiki/index.php/2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface MRSI processing module] for Slicer.<br /> <br /> = Key Investigators =<br /> * MIT: [http://people.csail.mit.edu/menze Bjoern Menze], [http://people.csail.mit.edu/tammy Tammy Riklin Raviv], [http://people.csail.mit.edu/koen Koen Van Leemput], [http://people.csail.mit.edu/polina Polina Golland]<br /> * Harvard: William M. Wells<br /> * INRIA Sophia-Antipolis, France: Ezequiel Geremia, Olivier Clatz, Nicholas Ayache<br /> * DKFZ Heidelberg, Germany: Bram Stieltjes, Marc-Andre Weber<br /> <br /> = Publications =<br /> *[http://www.na-mic.org/publications/pages/display?search=Projects%3ATumorModeling&amp;submit=Search&amp;words=all&amp;title=checked&amp;keywords=checked&amp;authors=checked&amp;abstract=checked&amp;sponsors=checked&amp;searchbytag=checked| NA-MIC Publications Database on brain tumor segmentation and modeling]</div>

Bmenze https://www.na-mic.org/w/index.php?title=File:Fisher-state-space_tumor-shapes.png&diff=65481 2011-03-23T21:32:17Z

<p>Bmenze: Parameter space of a Fisher-type reaction-diffusion tumor model with tumor cell diffusivity D and proliferation rate rho. Shown are the shapes of tumors with the same size but different parametrization.</p> <hr /> <div>Parameter space of a Fisher-type reaction-diffusion tumor model with tumor cell diffusivity D and proliferation rate rho. Shown are the shapes of tumors with the same size but different parametrization.</div>

Bmenze https://www.na-mic.org/w/index.php?title=Algorithm:MIT&diff=65480 2011-03-23T21:25:32Z

<p>Bmenze: /* Brain Tumor Segmentation and Modeling */</p> <hr /> <div> Back to [[Algorithm:Main|NA-MIC Algorithms]]<br /> __NOTOC__<br /> = Overview of MIT Algorithms (PI: Polina Golland) =<br /> <br /> Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.<br /> <br /> = MIT Projects =<br /> <br /> {| cellpadding=&quot;10&quot; style=&quot;text-align:left;&quot;<br /> || [[Image:Segmentation_example2.png|250px]]<br /> ||<br /> <br /> == [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==<br /> <br /> We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,<br /> given a training set of images and corresponding label maps. The resulting inference algorithms we<br /> develop rely on pairwise registrations between the test image and individual training images. The<br /> training labels are then transferred to the test image and fused to compute a final segmentation of<br /> the test subject. [[Projects:NonparametricSegmentation|More...]]<br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010. <br /> <br /> <br /> <br /> |-<br /> <br /> | | [[Image:TGIt.gif|center| 150px]]<br /> | |<br /> <br /> == [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==<br /> <br /> Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas. <br /> [[Projects:LatentAtlasSegmentation|More...]]<br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Special Issue of Medical Image Analysis (MedIA), 14(5):654-665, 2010. <br /> <br /> <br /> <br /> <br /> |-<br /> <br /> || [[Image:Mdepa_MDE_scar_seg_3D.png| 250px]]<br /> ||<br /> <br /> == [[Projects:AblationScarSegmentation | Cardiac Ablation Scar Segmentation]] ==<br /> <br /> Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to automatically segment the scar created by RF ablation in delayed enhancement MR images acquired after the procedure. This will then provide surgeons with a visualization which will help them to rapidly evaluate the success of the procedure.<br /> [[Projects:AblationScarSegmentation|More...]]<br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010. <br /> <br /> |-<br /> <br /> | | [[Image:Tumor_model.jpg‎|center|150px]]<br /> | |<br /> <br /> == [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==<br /> <br /> We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]<br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; Menze BH, Van Leemput K, Honkela A, Konukoglu E, Weber MA, Ayache N and Golland P. A generative approach for image-based modeling of tumor growth. Proc IPMI 2011. LNCS. 12p<br /> <br /> <br /> |-<br /> <br /> {| cellpadding=&quot;10&quot; style=&quot;text-align:left;&quot;<br /> || [[Image:lh.pm14686.BA2.gif|250px]]<br /> ||<br /> <br /> == [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==<br /> <br /> We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]<br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010. <br /> <br /> <br /> <br /> |-<br /> <br /> | | [[Image:CoordinateChart.png|250px]]<br /> | |<br /> <br /> == [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==<br /> <br /> We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]<br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.<br /> <br /> <br /> |-<br /> <br /> | | [[Image:epi_correction_small.jpg|200px]]<br /> | |<br /> <br /> == [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==<br /> <br /> In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]<br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; Poynton C., Jenkinson M., Wells III W. Atlas-Based Improved Prediction of Magnetic Field Inhomogeneity for Distortion Correction of EPI Data. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 5761:951-959, 2009.<br /> <br /> |-<br /> <br /> | | <br /> | |<br /> <br /> == [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==<br /> <br /> Quantifying magnetic susceptibility in the brain from the phase of the MR signal<br /> provides a non-invasive means for measuring the accumulation<br /> of iron believed to occur with aging and neurodegenerative disease. We<br /> describe a variational approach to susceptibility estimation that<br /> incorporates a tissue-air atlas to resolve ambiguity in the forward model, while eliminating additional biasfields<br /> through application of the Laplacian. [[Projects:QuantitativeSusceptibilityMapping|More...]]<br /> <br /> |-<br /> <br /> | | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]<br /> | |<br /> <br /> == [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==<br /> <br /> One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]] <br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; G. Langs, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Functional Geometry Alignment for Localization of Brain Areas. To appear in Proc. NIPS: Neural Information Processing Systems, 2010. <br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6361:191-199, 2010. <br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.<br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.<br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.<br /> <br /> |-<br /> <br /> {| cellpadding=&quot;10&quot; style=&quot;text-align:left;&quot; <br /> || [[Image:TetrahedralAtlasWarp.gif‎ |250px]]<br /> ||<br /> <br /> == [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==<br /> <br /> The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]<br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, and B. Fischl. Automated Segmentation of Hippocampal Subfields from Ultra-High Resolution In Vivo MRI. Hippocampus, 19:549-557, 2009. <br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; K. Van Leemput. Encoding Probabilistic Atlases Using Bayesian Inference. IEEE Transactions on Medical Imaging, 28(6):822-837, 2009.<br /> <br /> <br /> |-<br /> <br /> | | [[Image:ICluster_templates.gif|250px]]<br /> | |<br /> <br /> == [[Projects:MultimodalAtlas|Multimodal Atlas]] ==<br /> <br /> In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.<br /> [[Projects:MultimodalAtlas|More...]]<br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging, 28(9):1473 - 1487, 2009.<br /> <br /> <br /> <br /> <br /> <br /> |-<br /> <br /> | | [[Image:GroupwiseSummary.PNG|200px]]<br /> | |<br /> <br /> == [[Projects:GroupwiseRegistration|Groupwise Registration]] ==<br /> <br /> We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.<br /> <br /> In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]<br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; Asymmetric Image-Template Registration, M.R. Sabuncu, B.T. Thomas Yeo, T. Vercauteren, K. Van Leemput, P. Golland. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 5761:565-573, 2009.<br /> <br /> <br /> |-<br /> <br /> | | [[Image:JointRegSeg.png|200px]]<br /> | |<br /> <br /> == [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==<br /> <br /> We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image fidelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.<br /> [[Projects:RegistrationRegularization|More...]]<br /> <br /> |-<br /> <br /> | | [[Image:FoldingSpeedDetection.png|150px|]]<br /> | |<br /> <br /> == [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==<br /> <br /> In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]<br /> <br /> |-<br /> <br /> | | [[Image:Models.jpg|200px]]<br /> | |<br /> <br /> == [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==<br /> <br /> The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]<br /> <br /> |-<br /> <br /> | | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]<br /> | |<br /> <br /> == [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==<br /> <br /> This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]<br /> <br /> <br /> <br /> |-<br /> <br /> | | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]<br /> | |<br /> <br /> == [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==<br /> <br /> The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]<br /> <br /> |-<br /> <br /> | | [[Image:brain.png|200px]]<br /> | |<br /> <br /> == [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==<br /> <br /> The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]<br /> <br /> |-<br /> <br /> | | [[Image:FMRIEvaluationchart.jpg|200px]]<br /> | |<br /> <br /> == [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==<br /> <br /> We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]<br /> <br /> &lt;font color=&quot;red&quot;&gt;'''New: '''&lt;/font&gt; W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.<br /> <br /> <br /> |-<br /> <br /> | | [[Image:Thalamus_algo_outline.png|200px]]<br /> | |<br /> <br /> == [[Projects:DTISegmentation|DTI-based Segmentation]] ==<br /> <br /> Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]<br /> <br /> |-<br /> <br /> | | [[Image:ConnectivityMap.png|200px]]<br /> | |<br /> <br /> == [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==<br /> <br /> This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]<br /> <br /> |-<br /> <br /> | | [[Image:HippocampalShapeDifferences.gif|200px]]<br /> | |<br /> <br /> == [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==<br /> <br /> Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]<br /> <br /> |}</div>

Bmenze https://www.na-mic.org/w/index.php?title=2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface&diff=63732 2011-01-14T16:09:45Z

<p>Bmenze: /* Project */</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Figure 1 - Fitting metabolite models to the MRS signal of tumorous brain tissue.<br /> Image:Mrsi parametric-map-NAA 3d.jpg|Figure 2 - MRSI metabolite maps visualized in Slicer<br /> Image:Mrsi_slicer_sivic.jpg| Figure 3 - Screenshot of the SIVIC MRSI module integrated into Slicer. <br /> Image:Sivic_slicer_slc_mets.jpg| Figure 4 - MRSI metabolite maps generated in SIVIC module referenced to anatomical (FLAIR) image.<br /> Image:SIVICSlicerMultitrace.png| Figure 5 - Multiple trace functionality added to svk (SIVIC kit) during project week and then made accessible through the SIVIC MRSI Module. <br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Mangpo Phothilimthana, Polina Golland<br /> * UCSF: Beck Olson, Jason Crane, Sarah Nelson ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> * BWH: Nicole Aucoin<br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> More specifically we want to integrate the previously developed signal processing routines (Fig. 1) into the svk library of the SIVIC framework, and realize the joint display of metabolic maps (Fig. 2) and spectral information (Fig. 3) within the current prototype of the Slicer module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> &lt;/div&gt;<br /> * We implemented a basic quantification algorithm (Fig. 4) integrating over predefined spectral regions and made progress towards implementing the other fitting routines. <br /> <br /> * Metabolic maps generated in Slicer can now be displayed (Fig. 4).<br /> <br /> * The display of individual spectra in Slicer now also supports multiple traces from the spectral fitting (Fig. 5).<br /> <br /> * We improved the integration of SIVIC with Slicer through a better use of the MRML architecture.<br /> <br /> * The new functionality is now available through SIVIC and a Slicer Module prototype.<br /> <br /> &lt;/div&gt;<br /> <br /> <br /> &lt;div style=&quot;width: 97%; float: left;&quot;&gt;<br /> <br /> ==Delivery Mechanism==<br /> <br /> This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)<br /> <br /> #ITK Module<br /> #Slicer Module<br /> ##Built-in<br /> ##Extension -- commandline<br /> ##Extension -- loadable YES <br /> #Other (Please specify) YES: part of the 'vtk style' svk library of the SIVIC framework<br /> <br /> ==References==<br /> * Jason C. Crane, Marram P. Olson, Sarah J. Nelson. SIVIC: An Extensible Open-Source DICOM MR Spectroscopy Software Framework and Application Suite. Proc ISMRM 2010. 3354. <br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface&diff=63729 2011-01-14T16:04:16Z

<p>Bmenze: /* Project */</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Figure 1 - Fitting metabolite models to the MRS signal of tumorous brain tissue.<br /> Image:Mrsi parametric-map-NAA 3d.jpg|Figure 2 - MRSI metabolite maps visualized in Slicer<br /> Image:Mrsi_slicer_sivic.jpg| Figure 3 - Screenshot of the SIVIC MRSI module integrated into Slicer. <br /> Image:Sivic_slicer_slc_mets.jpg| Figure 4 - MRSI metabolite maps generated in SIVIC module referenced to anatomical (FLAIR) image.<br /> Image:SIVICSlicerMultitrace.png| Figure 5 - Multiple trace functionality added to svk (SIVIC kit) during project week and then made accessible through the SIVIC MRSI Module. <br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Mangpo Phothilimthana, Polina Golland<br /> * UCSF: Beck Olson, Jason Crane, Sarah Nelson ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> * BWH: Nicole Aucoin<br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> More specifically we want to integrate the previously developed signal processing routines (Fig. 1) into the svk library of the SIVIC framework, and realize the joint display of metabolic maps (Fig. 2) and spectral information (Fig. 3) within the current prototype of the Slicer module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> &lt;/div&gt;<br /> * We implemented a basic quantification algorithm (Fig. 4) integrating over predefined spectral regions and made progress towards implementing the other fitting routines. <br /> <br /> * Metabolic maps generated in SLICER can now be displayed (Fig. 4).<br /> <br /> * The display of individual spectra in SLICER now also supports multiple traces from the spectral fitting (Fig. 5).<br /> <br /> * We improved the integration of SIVIC with SLICER through a better use of the MRML architecture.<br /> <br /> * The new functionality is now available through SIVIC and the SLICER Module prototype.<br /> <br /> &lt;/div&gt;<br /> <br /> <br /> &lt;div style=&quot;width: 97%; float: left;&quot;&gt;<br /> <br /> ==Delivery Mechanism==<br /> <br /> This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)<br /> <br /> #ITK Module<br /> #Slicer Module<br /> ##Built-in<br /> ##Extension -- commandline<br /> ##Extension -- loadable YES <br /> #Other (Please specify) YES: part of the 'vtk style' svk library of the SIVIC framework<br /> <br /> ==References==<br /> * Jason C. Crane, Marram P. Olson, Sarah J. Nelson. SIVIC: An Extensible Open-Source DICOM MR Spectroscopy Software Framework and Application Suite. Proc ISMRM 2010. 3354. <br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface&diff=63727 2011-01-14T16:02:58Z

<p>Bmenze: /* Project */</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Figure 1 - Fitting metabolite models to the MRS signal of tumorous brain tissue.<br /> Image:Mrsi parametric-map-NAA 3d.jpg|Figure 2 - MRSI metabolite maps visualized in Slicer<br /> Image:Mrsi_slicer_sivic.jpg| Figure 3 - Screenshot of the SIVIC MRSI module integrated into Slicer. <br /> Image:Sivic_slicer_slc_mets.jpg| Figure 4 - MRSI metabolite maps generated in SIVIC module referenced to anatomical (FLAIR) image.<br /> Image:SIVICSlicerMultitrace.png| Figure 5 - Multiple trace functionality added to svk (SIVIC kit) during project week and then made accessible through the SIVIC MRSI Module. <br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Mangpo Phothilimthana, Polina Golland<br /> * UCSF: Beck Olson, Jason Crane, Sarah Nelson ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> * BWH: Nicole Aucoin<br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> More specifically we want to integrate the previously developed signal processing routines (Fig. 1) into the svk library of the SIVIC framework, and realize the joint display of metabolic maps (Fig. 2) and spectral information (Fig. 3) within the current prototype of the Slicer module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> &lt;/div&gt;<br /> * We implemented a basic quantification algorithm (Fig. 4) integrating over predefined spectral regions and made progress towards implementing the other fitting routines. <br /> <br /> * Metabolic maps generated in SLICER can now be displayed (Fig. 4).<br /> <br /> * The display of individual spectra in SLICER now also supports multiple traces from the spectral fitting (Fig. 5).<br /> <br /> * We improved the integration of SIVIC with SLICER through a better use of the MRML architecture.<br /> <br /> <br /> &lt;/div&gt;<br /> <br /> <br /> &lt;div style=&quot;width: 97%; float: left;&quot;&gt;<br /> <br /> ==Delivery Mechanism==<br /> <br /> This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)<br /> <br /> #ITK Module<br /> #Slicer Module<br /> ##Built-in<br /> ##Extension -- commandline<br /> ##Extension -- loadable YES <br /> #Other (Please specify) YES: part of the 'vtk style' svk library of the SIVIC framework<br /> <br /> ==References==<br /> * Jason C. Crane, Marram P. Olson, Sarah J. Nelson. SIVIC: An Extensible Open-Source DICOM MR Spectroscopy Software Framework and Application Suite. Proc ISMRM 2010. 3354. <br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface&diff=63725 2011-01-14T16:02:11Z

<p>Bmenze: /* Key Investigators */</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Figure 1 - Fitting metabolite models to the MRS signal of tumorous brain tissue.<br /> Image:Mrsi parametric-map-NAA 3d.jpg|Figure 2 - MRSI metabolite maps visualized in Slicer<br /> Image:Mrsi_slicer_sivic.jpg| Figure 3 - Screenshot of the SIVIC MRSI module integrated into Slicer. <br /> Image:Sivic_slicer_slc_mets.jpg| Figure 4 - MRSI metabolite maps generated in SIVIC module referenced to anatomical (FLAIR) image.<br /> Image:SIVICSlicerMultitrace.png| Figure 5 - Multiple trace functionality added to svk (SIVIC kit) during project week and then made accessible through the SIVIC MRSI Module. <br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Mangpo Phothilimthana, Polina Golland<br /> * UCSF: Beck Olson, Jason Crane, Sarah Nelson ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> * BWH: Nicole Aucoin<br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> More specifically we want to integrate the previously developed signal processing routines (Fig. 1) into the svk library of the SIVIC framework, and realize the joint display of metabolic maps (Fig. 2) and spectral information (Fig. 3) within the current prototype of the Slicer module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> &lt;/div&gt;<br /> * We implemented a basic quantification algorithm (Fig. 4) integrating over predefined spectral regions and made progress towards implementing the other fitting routines. <br /> <br /> * Metabolic maps generated in SLICER can now be displayed (Fig. 4).<br /> <br /> * The display of individual spectra in SLICER now also supports multiple traces from the spectral fitting (Fig. 5).<br /> <br /> * Improved integration of SIVIC with SLICER through better use of MRML architecture.<br /> <br /> <br /> &lt;/div&gt;<br /> <br /> <br /> &lt;div style=&quot;width: 97%; float: left;&quot;&gt;<br /> <br /> ==Delivery Mechanism==<br /> <br /> This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)<br /> <br /> #ITK Module<br /> #Slicer Module<br /> ##Built-in<br /> ##Extension -- commandline<br /> ##Extension -- loadable YES <br /> #Other (Please specify) YES: part of the 'vtk style' svk library of the SIVIC framework<br /> <br /> ==References==<br /> * Jason C. Crane, Marram P. Olson, Sarah J. Nelson. SIVIC: An Extensible Open-Source DICOM MR Spectroscopy Software Framework and Application Suite. Proc ISMRM 2010. 3354. <br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface&diff=63723 2011-01-14T15:58:32Z

<p>Bmenze: /* Project */</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Figure 1 - Fitting metabolite models to the MRS signal of tumorous brain tissue.<br /> Image:Mrsi parametric-map-NAA 3d.jpg|Figure 2 - MRSI metabolite maps visualized in Slicer<br /> Image:Mrsi_slicer_sivic.jpg| Figure 3 - Screenshot of the SIVIC MRSI module integrated into Slicer. <br /> Image:Sivic_slicer_slc_mets.jpg| Figure 4 - MRSI metabolite maps generated in SIVIC module referenced to anatomical (FLAIR) image.<br /> Image:SIVICSlicerMultitrace.png| Figure 5 - Multiple trace functionality added to svk (SIVIC kit) during project week and then made accessible through the SIVIC MRSI Module. <br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Mangpo Phothilimthana, Polina Golland<br /> * UCSF: Beck Olson, Jason Crane, Sarah Nelson ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> More specifically we want to integrate the previously developed signal processing routines (Fig. 1) into the svk library of the SIVIC framework, and realize the joint display of metabolic maps (Fig. 2) and spectral information (Fig. 3) within the current prototype of the Slicer module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> &lt;/div&gt;<br /> * We implemented a basic quantification algorithm (Fig. 4) integrating over predefined spectral regions and made progress towards implementing the other fitting routines. <br /> <br /> * Metabolic maps generated in SLICER can now be displayed (Fig. 4).<br /> <br /> * The display of individual spectra in SLICER now also supports multiple traces from the spectral fitting (Fig. 5).<br /> <br /> * Improved integration of SIVIC with SLICER through better use of MRML architecture.<br /> <br /> <br /> &lt;/div&gt;<br /> <br /> <br /> &lt;div style=&quot;width: 97%; float: left;&quot;&gt;<br /> <br /> ==Delivery Mechanism==<br /> <br /> This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)<br /> <br /> #ITK Module<br /> #Slicer Module<br /> ##Built-in<br /> ##Extension -- commandline<br /> ##Extension -- loadable YES <br /> #Other (Please specify) YES: part of the 'vtk style' svk library of the SIVIC framework<br /> <br /> ==References==<br /> * Jason C. Crane, Marram P. Olson, Sarah J. Nelson. SIVIC: An Extensible Open-Source DICOM MR Spectroscopy Software Framework and Application Suite. Proc ISMRM 2010. 3354. <br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface&diff=63705 2011-01-14T15:35:54Z

<p>Bmenze: /* Key Investigators */</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Figure 1 - Fitting metabolite models to the MRS signal of tumorous brain tissue.<br /> Image:Mrsi parametric-map-NAA 3d.jpg|Figure 2 - MRSI metabolite maps visualized in Slicer<br /> Image:Mrsi_slicer_sivic.jpg| Figure 3 - Screenshot of the SIVIC MRSI module integrated into Slicer. <br /> Image:Sivic_slicer_slc_mets.jpg| Figure 4 - MRSI metabolite maps generated in SIVIC module referenced to anatomical (FLAIR) image. <br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Mangpo Phothilimthana, Polina Golland<br /> * UCSF: Beck Olson, Jason Crane, Sarah Nelson ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> More specifically we want to integrate the previously developed signal processing routines (Fig. 1) into the svk library of the SIVIC framework, and realize the joint display of metabolic maps (Fig. 2) and spectral information (Fig. 3) within the current prototype of the Slicer module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> &lt;/div&gt;<br /> <br /> &lt;/div&gt;<br /> <br /> <br /> &lt;div style=&quot;width: 97%; float: left;&quot;&gt;<br /> <br /> ==Delivery Mechanism==<br /> <br /> This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)<br /> <br /> #ITK Module<br /> #Slicer Module<br /> ##Built-in<br /> ##Extension -- commandline<br /> ##Extension -- loadable YES <br /> #Other (Please specify) YES: part of the 'vtk style' svk library of the SIVIC framework<br /> <br /> ==References==<br /> * Jason C. Crane, Marram P. Olson, Sarah J. Nelson. SIVIC: An Extensible Open-Source DICOM MR Spectroscopy Software Framework and Application Suite. Proc ISMRM 2010. 3354. <br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface&diff=62514 2010-12-17T22:08:30Z

<p>Bmenze: /* Delivery Mechanism */</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Figure 1 - Fitting metabolite models to the MRS signal of tumorous brain tissue.<br /> Image:Mrsi parametric-map-NAA 3d.jpg|Figure 2 - MRSI metabolite maps visualized in Slicer<br /> Image:Mrsi_slicer_sivic.jpg| Figure 3 - Screenshot of the SIVIC MRSI module integrated into Slicer. <br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Mangpo Phothilimthana, Polina Golland<br /> * UCSF: Olson Beck, Jason Crane ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> More specifically we want to integrate the previously developed signal processing routines (Fig. 1) into the svk library of the SIVIC framework, and realize the joint display of metabolic maps (Fig. 2) and spectral information (Fig. 3) within the current prototype of the Slicer module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> &lt;/div&gt;<br /> <br /> &lt;/div&gt;<br /> <br /> <br /> &lt;div style=&quot;width: 97%; float: left;&quot;&gt;<br /> <br /> ==Delivery Mechanism==<br /> <br /> This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)<br /> <br /> #ITK Module<br /> #Slicer Module<br /> ##Built-in<br /> ##Extension -- commandline<br /> ##Extension -- loadable YES <br /> #Other (Please specify) YES: part of the 'vtk style' svk library of the SIVIC framework<br /> <br /> ==References==<br /> * Jason C. Crane, Marram P. Olson, Sarah J. Nelson. SIVIC: An Extensible Open-Source DICOM MR Spectroscopy Software Framework and Application Suite. Proc ISMRM 2010. 3354. <br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface&diff=62513 2010-12-17T22:06:42Z

<p>Bmenze: /* Project */</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Figure 1 - Fitting metabolite models to the MRS signal of tumorous brain tissue.<br /> Image:Mrsi parametric-map-NAA 3d.jpg|Figure 2 - MRSI metabolite maps visualized in Slicer<br /> Image:Mrsi_slicer_sivic.jpg| Figure 3 - Screenshot of the SIVIC MRSI module integrated into Slicer. <br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Mangpo Phothilimthana, Polina Golland<br /> * UCSF: Olson Beck, Jason Crane ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> More specifically we want to integrate the previously developed signal processing routines (Fig. 1) into the svk library of the SIVIC framework, and realize the joint display of metabolic maps (Fig. 2) and spectral information (Fig. 3) within the current prototype of the Slicer module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> &lt;/div&gt;<br /> <br /> &lt;/div&gt;<br /> <br /> <br /> &lt;div style=&quot;width: 97%; float: left;&quot;&gt;<br /> <br /> ==Delivery Mechanism==<br /> <br /> This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)<br /> <br /> #ITK Module<br /> #Slicer Module<br /> ##Built-in<br /> ##Extension -- commandline<br /> ##Extension -- loadable YES<br /> #Other (Please specify)<br /> <br /> ==References==<br /> * Jason C. Crane, Marram P. Olson, Sarah J. Nelson. SIVIC: An Extensible Open-Source DICOM MR Spectroscopy Software Framework and Application Suite. Proc ISMRM 2010. 3354. <br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface&diff=62512 2010-12-17T22:04:54Z

<p>Bmenze: /* Key Investigators */</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Figure 1 - Fitting metabolite models to the MRS signal of tumorous brain tissue.<br /> Image:Mrsi parametric-map-NAA 3d.jpg|Figure 2 - MRSI metabolite maps visualized in Slicer<br /> Image:Mrsi_slicer_sivic.jpg| Figure 3 - Screenshot of the SIVIC MRSI module integrated into Slicer. <br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Mangpo Phothilimthana, Polina Golland<br /> * UCSF: Olson Beck, Jason Crane ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> More specifically we want to realize the integration of the previously developed signal processing routines (Fig. 1) into the svk library of the SIVIC framework, and the joint display of metabolic maps (Fig. 2) and spectral information (Fig. 3) within the Slicer module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> &lt;/div&gt;<br /> <br /> &lt;/div&gt;<br /> <br /> <br /> &lt;div style=&quot;width: 97%; float: left;&quot;&gt;<br /> <br /> ==Delivery Mechanism==<br /> <br /> This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)<br /> <br /> #ITK Module<br /> #Slicer Module<br /> ##Built-in<br /> ##Extension -- commandline<br /> ##Extension -- loadable YES<br /> #Other (Please specify)<br /> <br /> ==References==<br /> * Jason C. Crane, Marram P. Olson, Sarah J. Nelson. SIVIC: An Extensible Open-Source DICOM MR Spectroscopy Software Framework and Application Suite. Proc ISMRM 2010. 3354. <br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface&diff=62511 2010-12-17T21:58:46Z

<p>Bmenze: Created page with '__NOTOC__ &lt;gallery&gt; Image:PW-SLC2011.png|Projects List Image:Spectral fitting mrs.png|Figure 1 - Fitting metabolite models to the MRS signal…'</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Figure 1 - Fitting metabolite models to the MRS signal of tumorous brain tissue.<br /> Image:Mrsi parametric-map-NAA 3d.jpg|Figure 2 - MRSI metabolite maps visualized in Slicer<br /> Image:Mrsi_slicer_sivic.jpg| Figure 3 - Screenshot of the SIVIC MRSI module integrated into Slicer. <br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Polina Golland<br /> * UCSF: Olson Beck, Jason Crane ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> <br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> More specifically we want to realize the integration of the previously developed signal processing routines (Fig. 1) into the svk library of the SIVIC framework, and the joint display of metabolic maps (Fig. 2) and spectral information (Fig. 3) within the Slicer module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> &lt;/div&gt;<br /> <br /> &lt;/div&gt;<br /> <br /> <br /> &lt;div style=&quot;width: 97%; float: left;&quot;&gt;<br /> <br /> ==Delivery Mechanism==<br /> <br /> This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)<br /> <br /> #ITK Module<br /> #Slicer Module<br /> ##Built-in<br /> ##Extension -- commandline<br /> ##Extension -- loadable YES<br /> #Other (Please specify)<br /> <br /> ==References==<br /> * Jason C. Crane, Marram P. Olson, Sarah J. Nelson. SIVIC: An Extensible Open-Source DICOM MR Spectroscopy Software Framework and Application Suite. Proc ISMRM 2010. 3354. <br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2011_Winter_Project_Week&diff=62510 2010-12-17T21:39:05Z

<p>Bmenze: /* Analysis */</p> <hr /> <div> Back to [[Project Events]], [[Events]]<br /> Back to [[Project Events]], [[AHM_2011]], [[Events]]<br /> <br /> __NOTOC__<br /> [[image:PW-SLC2011.png|300px]]<br /> <br /> == Dates.Venue.Registration ==<br /> <br /> Please [[AHM_2011#Dates_Venue_Registration|click here for Dates, Venue, and Registration]] for this event.<br /> <br /> == Agenda==<br /> <br /> Please [[AHM_2011#Agenda|click here for the agenda for AHM 2011 and Project Week]].<br /> <br /> ==Background==<br /> <br /> From January 10-14, 2011, the twelfth project week for hands-on research and development activity in Image-Guided Therapy and Neuroscience applications will be hosted in Salt Lake City, Utah. Participant engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithms, medical imaging sequence development, tracking experiments, and clinical applications. The main goal of this event is to further the translational research deliverables of the sponsoring centers ([http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]) and their collaborators by identifying and solving programming problems during planned and ad hoc break-out sessions. <br /> <br /> Active preparation for this conference begins with a kick-off teleconference. Invitations to this call are sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties expressing an interest in working with these centers. The main goal of the initial teleconference is to gather information about which groups/projects would be active at the upcoming event to ensure that there were sufficient resources available to meet everyone's needs. Focused discussions about individual projects are conducted during several subsequent teleconferences and permits the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in break-out sessions. In the final days leading up to the meeting, all project teams are asked to complete a template page on the wiki describing the objectives and research plan for each project. <br /> <br /> On the first day of the conference, each project team leader delivers a short presentation to introduce their topic and individual members of their team. These brief presentations serve to both familiarize other teams doing similar work about common problems or practical solutions, and to identify potential subsets of individuals who might benefit from collaborative work. For the remainder of the conference, about 50% time is devoted to break-out discussions on topics of common interest to particular subsets and 50% to hands-on project work. For hands-on project work, attendees are organized into 30-50 small teams comprised of 2-4 individuals with a mix of multi-disciplinary expertise. To facilitate this work, a large room is setup with ample work tables, internet connection, and power access. This enables each computer software development-based team to gather on a table with their individual laptops, connect to the internet, download their software and data, and work on specific projects. On the final day of the event, each project team summarizes their accomplishments in a closing presentation.<br /> <br /> A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].<br /> <br /> <br /> <br /> <br /> <br /> ==Projects==<br /> <br /> === Segmentation ===<br /> * [[2011_Winter_Project_Week:Extension of ABC to detect pathology categories|Extension of ABC (Atlas-Based Classification) to detect pathology categories, with tests on TBI images]] (Bo Wang, Jack Van Horn, Marcel Prastawa, Guido Gerig).<br /> * [[2011_Winter_Project_Week:Atrial_Fibrillation|Segmentation of the left atrial wall for atrial fibrillation ablation therapy]] (Behnood Gholami, Yi Gao, and Allen Tannenbaum)<br /> * [[2011_Winter_Project_Week:The_Vascular_Modeling_Toolkit_in_3D_Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Luca Antiga, Kilian Pohl, Steve Pieper, Ron Kikinis)<br /> *TubeTK for vascular image segmentation and analysis (Stephen Aylward, Danielle Pace, Steve Pieper)<br /> * [[2011_Winter_Project_Week:StenosisDetector|A stenosis detector in Slicer4 using VMTK ]](Suares Tamekue, Daniel Haehn, Luca Antiga, Ron Kikinis)<br /> * [[2011_Winter_Project_Week:MeshCurvolver|Surface Region Segmentation for Surgical Planning and Mapping ]] (Peter Karasev, Karol Chudy, Allen Tannenbaum)<br /> * [[2011_Winter_Project_Week:SPECTRE_Integration|Integration of SPECTRE into Slicer]] (Nicole Aucoin, Min Chen)<br /> * [[2011_Winter_Project_Week:Segmentation of Nerve and Nerve Ganglia in the Spine]] (Adrian Dalca, Giovanna Danagoulian, Ehud Schmidt, Polina Golland)<br /> <br /> === Registration ===<br /> *[[2011_Winter_Project_Week:RegistrationCaseLibrary|The 3DSlicer Registration Case Library]] (Dominik Meier,Ron Kikinis)<br /> *[[2011_Winter_Project_Week:RegistrationAnisotropy|Voxel Anisotropy and Bias Field Effects on Slicer Image Registration]] (Dominik Meier, Andriy Fedorov) (tentative)<br /> * [[2011_Winter_Project_Week:Efficient co-registration of multiple MR modalities using the ABC|Efficient co-registration of multiple MR modalities using the ABC (atlas-based classification) framework, joint visualization of multiple co-registered modalities]] (Bo Wang, Jack Van Horn, Marcel Prastawa, Guido Gerig).<br /> * [[2011_Winter_Project_Week:DTI_MRI_Registration|DTI MRI Registration- Evaluation of registration schemes]] (Anuja Sharma, Guido Gerig)<br /> * Registration of CT and MRI volumes for Adaptive Radiotherapy (Ivan Kolesov, Gregory Sharp, and Allen Tannenbaum)<br /> * [[2011_Winter_Project_Week:Atlas_Registration_in_Slicer3|Atlas Registration in Slicer3]] (Daniel Haehn, Dominik Meier, Kilian Pohl, Ryan Eckbo)<br /> * Registration in the presence of anatomic variation (aka. Sliding organ registration) (Danielle Pace, Marc Niethammer, Petter Risholm, Tina Kapur, Sandy Wells, Stephen Aylward)<br /> * [[2011_Winter_Project_Week:UncertaintyVisualization|Visualizing registration uncertainty in Slicer3]] (Petter Risholm, William Wells)<br /> * [[2011_Winter_Project_Week:LandmarkRegularization|Landmark-based registration with analytic regularization]] (Nadya Shusharina, Gregory Sharp)<br /> * [[2011_Winter_Project_Week:DTIPipeline|DTI registration/processing pipeline in Slicer3]] (Francois Budin, Clement Vachet)<br /> <br /> === IGT ===<br /> *Open IGT Link 2.0 (Junichi Tokuda, Nobuhiko Hata) <br /> *[[2011_Winter_Project_Week:Osteomark|Osteormark, navigation tool for Osteotomy]] (Laurent Chauvin, Nobuhiko Hata)<br /> *[[2011_Winter_Project_Week:Intra-ProceduralProstateMotion|Detection and compensation for prostate motion during MR-guided prostate biopsy]] (A.Fedorov, Andras Lasso)<br /> *[[2011_Winter_Project_Week:ThinClientQtInterfaceForIGT|Thin Client QT Interface for IGT]] (Nicholas Herlambang, Steve Pieper, Julien Finet, JC)<br /> *[[2011_Winter_Project_Week:TransformRecorderAndProcedureAnnotation|Transform recorder and (surgical) procedure annotation module]] (Tamas Ungi, Junichi Tokuda)<br /> <br /> === Radiotherapy ===<br /> * User controlled segmentation of head and neck structures for Adaptive Radiotherapy (Ivan Kolesov, Gregory Sharp, and Allen Tannenbaum)<br /> * [[2011_Winter_Project_Week:DicomRtExport|DICOM-RT export]] (Greg Sharp, Steve Pieper)<br /> <br /> === Analysis ===<br /> * [[2011_Winter_Project_Week:GAMBITCorticalThicknessAnalysis |GAMBIT - Cortical thickness analysis]] - Clement Vachet, Martin Styner<br /> * [[2011_Winter_Project_Week:ParticleShapeAnalysis|Particle shape analysis incorporating surface normals ]] - Beatriz Paniagua, Martin Styner<br /> * [[2011_Winter_Project_Week:NAMICShapeAnalysis |NAMIC shape analysis pipeline in Slicer 3]] - Lucile Bompard, Martin Styner, Clement Vachet, Chris Gloschat<br /> * Particle Systems for Shape Analysis - Josh Cates, Manasi Datar, Ross Whitaker<br /> * [[2011_Winter_Project_Week:MRSI_module_and_SIVIC_interface| MRSI module and SIVIC interface]] - Bjoern Menze, Jason Crane, Beck Olson, Polina Golland<br /> <br /> === Informatics ===<br /> * [[2011_Winter_Project_Week:UIowaTHPDTIData|Share all UIowa Traveling Human Phantom DTI data with NAMIC]] - Mark Scully, Hans Johnson, Zack M.<br /> * Ontology-augmented MRI brain atlas - Michael Halle, Jim Miller, Samira Farough<br /> * Functional brain atlas (version 2) - Michael Halle, Jim Miller<br /> <br /> === Diffusion ===<br /> * [[2011_Winter_Project_Week:DicomToNrrdTestSuite |Test suite for DicomToNrrdConverter]] - Mark Scully, Zach Mullen, Xiaodong Tao, Hans Johnson <br /> * [[2011_Winter_Project_Week:DicomToNrrdRefactoring |Requirements gathering for refactoring DicomToNrrdConverter]] - Mark Scully, Xiaodong Tao, Hans Johnson <br /> * [[2011_Winter_Project_Week:DTIPrepDocumentation |Documentation and 1st Draft Tutorial for DTIPrep]] - Clement Vachet, Mark Scully, Hans Johnson<br /> * Voxelwise fiber distribution from tractography - Yinpeng Li, Martin Styner<br /> * [[2011_Winter_Project_Week:TwoTensorTracts |Two-tensor full brain tractography pipeline]] - Lauren O'Donnell, Yogesh Rathi, C-F Westin<br /> * [[2011_Winter_Project_Week:FreeWaterElimination |Free-water elimination]] - Ofer Pasternak, Demian Wassermann, C-F Westin<br /> * Finsler tractography in ITK - Antonio Tristan-Vega, C-F Westin<br /> * Statistical analysis of Cingulum extracted using Volumetric framework - Gopal Veni, Ross Whitaker<br /> <br /> === NA-MIC Kit Internals ===<br /> <br /> * Command line module logic redesign (passing paramenters, tie into workflows) - Jim, Steve<br /> * 64bit Windows Builds - Dave P<br /> * Dashboards: Factory machine, subprojects, and CDash@Home - Dave P, Zack M, Steve, and Stephen<br /> * MIDAS for data hosting - Zach M and Hans<br /> * vtkWidgets - JC and Will, Nicole Aucoin<br /> * [[2011_Winter_Project_Week:Annotation_module_in_Slicer4_Display_widget_intersections|Annotation module in Slicer4: Display widget intersections]] (Daniel Haehn, Nicole Aucoin, Steve Pieper)<br /> * [[2011_Winter_Project_Week: Python and Slicer4| Python and Slicer4]]: Workflows, Scripting, and Porting - JC, Jim, Steve, and Danielle<br /> * [[2011_Winter_Project_Week: Slice View Performance| Improve Performance of Slice Rendering in slicer3 and slicer4]] (Steve, Will, Jc, Luca)<br /> * [[2011_NAMIC_Project_week:_Real-Time_Volume_Rendering_for_Virtual_Colonoscopy| Real-Time Volume Rendering for Virtual Colonoscopy]] (Steve, Alex)<br /> <br /> === Execution Model ===<br /> * Extract SlicerExecutionModel (SEM) into separate entity. SEM is the only component needed to build modules compatible with Slicer3D, so it should be easy incorporate into external applications without all of Slicer3D. Jim, Hans<br /> * [[2011_Winter_Project_Week:ExtendSEMXml|Extend SEM xml]] to include sections for explicit grant acknowledgements, pointers to documentation, and pointers to examples. - Hans, Andriy<br /> * [[2011_Winter_Project_Week:SEMXMLSchema|Create a formal schema for the SEM xml so that eternal tools (i.e. nipype) can validate the xml.]] - Hans Johnson, Jim Miller, Tim Olsen<br /> * [[2011_Winter_Project_Week:XMLToMediaWiki|Improve documentation extractor script that converts XML to MediaWiki format so that it can directly push this information into the Slicer3D MediaWiki.]] - (Wiki Systems Admin), Hans Johnson<br /> * [[2011_Winter_Project_Week:ExternalToolsMergingStrategies | Improve merging strategies between software that is part of externals tools and part of Slicer.]] - Mark Scully, Hans Johnson<br /> <br /> === Workflows and Integration ===<br /> * Workflows and Service Oriented Architecture Solutions for Slicer3 Modules. - Alexander Zaitsev, Wendy Plesniak, Charles Guttmann, Ron Kikinis<br /> <br /> === Preparation ===<br /> <br /> #Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list] <br /> #Starting Thursday, October 28th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:<br /> #*October 28: Engineering Infrastructure Projects<br /> #*November 4: Engineering Infrastructure Projects<br /> #*November 11: DPB Projects: Iowa, Outcomes from Alg Core Retreat <br /> #*November 18: DPB Projects: MGH <br /> #*November 25: DBP Projects, Funded External Collaborations<br /> #*December 2: Funded External Collaborations<br /> #*December 9: Other External Collaborations<br /> #*December 16:Finalize Engineering Projects <br /> #*January 6: Loose Ends<br /> #By December 16, 2010: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.<br /> #By December 16, 2010: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)<br /> ##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]<br /> ##Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)<br /> ##Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)<br /> ##Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)<br /> #Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_Summer_Project_Week_MRSI_module_and_SIVIC_interface&diff=55345 2010-06-25T13:59:37Z

<p>Bmenze: </p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-MIT2010.png|[[2010_Summer_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Figure 1 - Fitting metabolite models to the MRS signal of tumorous brain tissue.<br /> Image:Mrsi parametric-map-NAA 3d.jpg|Figure 2 - MRSI metabolite maps visualized in Slicer<br /> Image:Mrsi_slicer_sivic.jpg| Figure 3 - Screenshot of the SIVIC MRSI module integrated into Slicer. (Also see large screenshot at end of page.)<br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Mangpo Phothilimthana, Polina Golland<br /> * UCSF: Olson Beck, Jason Crane ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> <br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> We approached the integration from two sides:<br /> * We integrated one of the two MRSI signal processing routines (Fig. 1) into a svk/SIVIC library prototype class.<br /> * We integrated the SIVIC visualization routines into Slicer (Fig. 3, screenshot below) as a prototype module.<br /> <br /> Further steps will comprise the full integration of the signal processing routines, and the joint display of metabolic maps (Fig. 2) and spectral information within the Slicer module.<br /> &lt;/div&gt;<br /> &lt;/div&gt;<br /> <br /> == Screenshot ==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> <br /> [[Image:Mrsi_slicer_sivic.jpg|thumpnail|750px]] Figure 3 - Screenshot of the SIVIC MRSI module integrated into Slicer.<br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_Summer_Project_Week_MRSI_module_and_SIVIC_interface&diff=55215 2010-06-25T12:28:50Z

<p>Bmenze: </p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-MIT2010.png|[[2010_Summer_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Figure 1 - Fitting metabolite models to the MRS signal of tumorous brain tissue (top: baseline removal; bottom: resonance line model fits)<br /> Image:Mrsi parametric-map-NAA 3d.jpg|Figure 2 - MRSI metabolite maps visualized in Slicer<br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Mangpo Phothilimthana, Polina Golland<br /> * UCSF: Olson Beck, Jason Crane ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> <br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> We approached the integration from two sides:<br /> * We integrated one of the two MRSI signal processing routines (Fig. 1) into a svk/SIVIC library prototype class.<br /> * We integrated the SIVIC visualization routines into Slicer (Fig. 3, screenshot below) as a prototype module.<br /> <br /> Further steps will comprise the full integration of the signal processing routines, and the joint display of metabolic maps (Fig. 2) and spectral information within the Slicer module.<br /> <br /> &lt;/div&gt;<br /> <br /> <br /> <br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> [[Image:Mrsi_slicer_sivic.jpg|thumpnail|750px]] Figure 3 - Screenshot of the SIVIC MRSI module integrated into Slicer.<br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=File:Mrsi_slicer_sivic.jpg&diff=55214 2010-06-25T12:23:34Z

<p>Bmenze: </p> <hr /> <div></div>

Bmenze https://www.na-mic.org/w/index.php?title=File:Mrsi_slicer_sivic.tif&diff=55213 2010-06-25T12:17:09Z

<p>Bmenze: uploaded a new version of &quot;File:Mrsi slicer sivic.tif&quot;:&amp;#32;Reverted to version as of 12:13, 25 June 2010</p> <hr /> <div></div>

Bmenze https://www.na-mic.org/w/index.php?title=File:Mrsi_slicer_sivic.tif&diff=55212 2010-06-25T12:16:08Z

<p>Bmenze: uploaded a new version of &quot;File:Mrsi slicer sivic.tif&quot;</p> <hr /> <div></div>

Bmenze https://www.na-mic.org/w/index.php?title=File:Mrsi_slicer_sivic.tif&diff=55210 2010-06-25T12:13:56Z

<p>Bmenze: uploaded a new version of &quot;File:Mrsi slicer sivic.tif&quot;</p> <hr /> <div></div>

Bmenze https://www.na-mic.org/w/index.php?title=File:Mrsi_slicer_sivic.tif&diff=55206 2010-06-25T12:06:48Z

<p>Bmenze: </p> <hr /> <div></div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_Summer_Project_Week_MRSI_module_and_SIVIC_interface&diff=55204 2010-06-25T12:02:28Z

<p>Bmenze: /* Project */</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-MIT2010.png|[[2010_Summer_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Fitting metabolite models to the MRS signal of tumorous brain tissue (top: baseline removal; bottom: resonance line model fits)<br /> Image:Mrsi parametric-map-NAA 3d.jpg|MRSI metabolite maps visualized in Slicer<br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, M Phothilimthana, Polina Golland<br /> * UCSF: Olson Beck, Jason Crane ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> <br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> We approached the integration from two sides:<br /> * We integrated one of the two MRSI signal processing routines (Fig. 1, above) into a svk/SIVIC library prototype class.<br /> * We integrated the SIVIC visualization routines into Slicer 3.7 (Fig. 3, below) as a prototype module.<br /> <br /> Further steps will comprise the full integration of the signal processing routines, and the joint display of fitting results (as in Fig. 2) and spectral information within the Slicer module.<br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_Summer_Project_Week_MRSI_module_and_SIVIC_interface&diff=55203 2010-06-25T11:59:11Z

<p>Bmenze: /* Project */</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-MIT2010.png|[[2010_Summer_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Fitting metabolite models to the MRS signal of tumorous brain tissue (top: baseline removal; bottom: resonance line model fits)<br /> Image:Mrsi parametric-map-NAA 3d.jpg|MRSI metabolite maps visualized in Slicer<br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, M Phothilimthana, Polina Golland<br /> * UCSF: Olson Beck, Jason Crane ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> <br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> We approached the integration from two sides:<br /> * We integrated one of the two MRSI signal processing routines (Fig. 1, above) into a svk/SIVIC library prototype class.<br /> * We integrated the SIVIC visualization routines into Slicer 3.7 (Fig. 3, below) as a prototype module.<br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_Summer_Project_Week_MRSI_module_and_SIVIC_interface&diff=53144 2010-05-31T18:42:33Z

<p>Bmenze: Created page with '__NOTOC__ &lt;gallery&gt; Image:PW-MIT2010.png|Projects List Image:Spectral fitting mrs.png|Fitting metabolite models to the MRS signal of tumorou…'</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-MIT2010.png|[[2010_Summer_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Fitting metabolite models to the MRS signal of tumorous brain tissue (top: baseline removal; bottom: resonance line model fits)<br /> Image:Mrsi parametric-map-NAA 3d.jpg|MRSI metabolite maps visualized in Slicer<br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, M Phothilimthana, Polina Golland<br /> * UCSF: Olson Beck, Jason Crane ([http://www.radiology.ucsf.edu/nelsonlab/ Nelson Lab])<br /> <br /> <br /> ==Project==<br /> &lt;div style=&quot;margin: 10px;&quot;&gt;<br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule currently] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ SIVIC] Slicer interface developed at the UCSF.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_Summer_Project_Week&diff=53143 2010-05-31T18:35:50Z

<p>Bmenze: /* Analysis */</p> <hr /> <div>__NOTOC__<br /> <br /> Back to [[Project Events]], [[Events]]<br /> <br /> [[Image:PW-MIT2010.png|300px]]<br /> <br /> <br /> ==Background==<br /> <br /> We are pleased to announce the 11th PROJECT WEEK of hands-on research and development activity for applications in Image-Guided Therapy, Neuroscience, and several additional areas of biomedical research that enable personalized medicine. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. <br /> <br /> Active preparation begins on Thursday, April 15th at 3pm ET, with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects. <br /> <br /> The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 30-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.<br /> <br /> This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January. <br /> <br /> A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].<br /> <br /> == Logistics ==<br /> *'''Dates:''' June 21-25, 2010<br /> *'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A &amp; B: 34-401A &amp; 34-401B]].<br /> *'''REGISTRATION:''' Please click [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 here] to do an on-line registration for the meeting that will allow you to pay by credit card, or send a check.<br /> *'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week). <br /> *'''Hotel:''' We have reserved a block of rooms at the Boston Marriott Cambridge Hotel, Two Cambridge Center, 50 Broadway, Cambridge, MA 02142. (Phone: 617.252.4405, Fax: 617.494.6565) [http://www.marriott.com/hotels/travel/BOSCB?groupCode=NAMNAMA&amp;app=resvlink&amp;fromDate=6/20/10&amp;toDate=6/25/10 Please click here to reserve.] You will be directed to the property's home page with the group code already entered in the appropriate field. All you need to do is enter your arrival date to begin the reservation process. <br /> <br /> ''' All reservations must be made by Tuesday, June 1, 2010 to receive the discounted rate of'''<br /> ''' $189/night/room (plus tax).'''<br /> ''' This rate is good only through June 1.'''<br /> <br /> Please note that if you try to reserve a room outside of the block on the shoulder nights via the link, you will be told that the group rate is not available for the duration of your stay. To reserve those rooms, which might not be at the group rate because it is based upon availability, please call Marriott Central Reservations at 1-800-228-9290. <br /> <br /> *Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.<br /> *For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]<br /> <br /> ==Agenda==<br /> === Monday, June 21, 2010 === <br /> ** noon-1pm lunch <br /> **1pm: Welcome (Ron Kikinis)<br /> ** 1:05-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template Wiki Template]) <br /> ** 3:30-5:30pm Tutorial: [[2010 Summer Project Week Breakout: Getting Started with Qt]] (Adam Weinrich, Nokia)<br /> <br /> === Tuesday, June 22, 2010 ===<br /> ** 8:30am breakfast<br /> **9-9:45am: NA-MIC Kit Update (Jim Miller) - include Module nomenclature (Extensions: cmdline vs loadable, Built-in), QT, Include Superbuild demo by Dave P.<br /> **9:45-10:30am 3D Slicer Update (Steve Pieper)<br /> **10:30-11am OpenIGTLink Update (Junichi Tokuda)<br /> **11-12pm: Slicer Hands-on Workshop (Randy Gollub, Sonia Pujol)<br /> ** noon lunch <br /> ** 1-3pm: Breakout Session: QT/Slicer (Steve, JC, J2) (w/ possible QnA with QT experts)<br /> ** 3pm: [[Summer_2010_Tutorial_Contest|Tutorial Contest Presentations]]<br /> ** 4-5pm [[2010 Summer Project Week Breakout Session: Data Management]] (Dan Marcus, Stephen Aylward)<br /> ** 5:30pm adjourn for day<br /> <br /> === Wednesday, June 23, 2010 ===<br /> ** 8:30am breakfast<br /> ** 9am-12pm Breakout Session: [[2010 Project Week Breakout Session: ITK]] (Luis Ibanez)<br /> ** noon lunch<br /> **12:45pm: [[Events:TutorialContestJune2010|Tutorial Contest Winner Announcement]]<br /> **1-3pm: Breakout Session: [[Microscopy_Image_Analysis]] (Sean Megason)<br /> **3-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:QA Training]] (Luis Ibanesz)<br /> **3-4pm: Breakout Session: [[2010 Summer Project Week Breakout Session:VTK Widget]] (Nicole, Kilian, JC)<br /> ** 5:30pm adjourn for day<br /> <br /> === Thursday, June 24, 2010 ===<br /> ** 8:30am breakfast<br /> ** 9am-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:OpenIGTLink|OpenIGTLink]]<br /> ** noon lunch<br /> ** 1-2pm: [[2010 Summer Project Week Breakout Session:GWE]] (Marco Ruiz)<br /> ** 5:30pm adjourn for day<br /> <br /> === Friday, June 25, 2010 === <br /> ** 8:30am breakfast<br /> ** 10am-noon: [[#Projects|Project Progress Updates]]<br /> *** Noon: Lunch boxes and adjourn by 1:30pm.<br /> ***We need to empty room by 1:30. You are welcome to use wireless in Stata.<br /> ***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]<br /> ***Next Project Week [[AHM_2011|in Utah, Fill in Dates]]<br /> <br /> ==Projects==<br /> <br /> <br /> <br /> === Segmentation ===<br /> *[[2010_Summer_Project_Week_Robust_Statistics_Segmenter_Slicer_Module|Robust Statistics Segmenter Slicer Module]] (Yi Gao, Allen Tannenbaum, Ron Kikinis)<br /> *[[2010_Summer_Project_Week_Multi_scale_Shape_Based_Segmentation_for_the_Hippocampus|Multi-scale Shape Based Segmentation for the Hippocampus]] (Yi Gao, Allen Tannenbaum)<br /> *[[2010_Summer_Project_Week/The Vascular Modeling Toolkit in 3D Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Luca Antiga, Kilian Pohl, Steve Pieper, Ron Kikinis)<br /> *[[2010_Summer_Project_Week_Prostate_MRI_Segmentation|Prostate Segmentation from MRI]] (Andriy Fedorov, Yi Gao)<br /> *[[2010_Summer_Project_Week_SPECTRE|SPECTRE: Skull Stripping integration with Slicer]] (Nicole Aucoin, Min Chen)<br /> <br /> === Registration ===<br /> *[[2010_Summer_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library]] (Dominik Meier)<br /> *[[2010_Summer_Project_Week_Fiducial_Deformable_Registration|Fiducial-based deformable image registration]] (Nadya Shusharina, Greg Sharp)<br /> *[[2010_Summer_Project_Week_HAMMER: Deformable Registration|HAMMER: Deformable Registration]] (Guorong Wu, Xiaodong Tao, Jim Miller, Dinggang Shen)<br /> *[[2010_Summer_Project_Week_Best_Regularization_Term_for_Demons_Registration_Algorithm|Best Regularization Term for Demons Registration Algorithm]] (Rui Li, Greg Sharp)<br /> *[[2010_Summer_Project_Week_RegistrationEvaluation|Evaluation of Registration in Slicer]] (James Fishbaugh, Guido Gerig, Domink Meier)<br /> *[[2010_Summer_Project_Week_MR_to_CT_Registration_for_Prostate_Brachytherapy_Planning|MR to CT Registration for Prostate Brachytherapy Planning]] (Andriy Fedorov, ?)<br /> *[[2010_Summer_Project_Week_MR_to_Ultrasound_Registration_Methodology|MR to Ultrasound Registration Methodology]] (Dieter Hahn, William Wells, Joachim Hornegger, Tina Kapur, Stephen Aylward)<br /> <br /> === IGT ===<br /> *Prostate Intervention(Junichi, Sam Song, Tamas Ungi?)<br /> * Liver Ablation (Haiying Liu)<br /> * BrainLab-Aurora HybridNav (Isaiah Norton)<br /> <br /> === Radiotherapy ===<br /> *[[2010_Summer_Project_Week_DICOM_RT|Dicom RT plugin]] (Greg Sharp)<br /> *[[2010_Summer_Project_Week_HandN_Cancer|Adaptive Radiation Therapy for H&amp;N cancer]] (Marta Peroni,Polina Golland,Greg Sharp)<br /> <br /> === Analysis ===<br /> *[[Microscopy Image Analysis]] (Arnaud Gelas)<br /> *Femoral Fracture Classification Brainstorming Session (Karl F, Vince M, Peter Karasev, Curt Lisle, Ron)<br /> *Cortical thickness analysis (Clement Vachet, Heather Cody Hazlett, Martin Styner)<br /> *[[2010_Summer_Project_Week_MRSI_module_and_SIVIC_interface| MRSI module and SIVIC interface]] (B Menze, M Phothilimthana, J Crane (UCSF), B Olson (UCSF), P Golland)<br /> <br /> === Informatics ===<br /> * Computer Aided Photodynamic Therapy (Pietka, Spinczyk)<br /> <br /> === Diffusion ===<br /> *Fluid Mechanics Based Tractography (Nathan Hageman)<br /> <br /> === Python ===<br /> <br /> === Slicer Internals ===<br /> *Module Inventory (Steve, Jim)<br /> *Viewer Manager Factory (Alex Y., Kilian, Steve, Nicole)<br /> * [[2010 NAMIC Project week: Programmatic use of Volume Rendering module|Programmatic use of Volume Rendering module]] (Andrey Fedorov, Yanling Liu, Alex Yarmarkovich)<br /> <br /> === Execution Model ===<br /> <br /> ===Other NA-MIC Kit Internals===<br /> *VTKWidgets (JC, will, Schroeder, Nicole, Ron)<br /> *Superbuild (Dave Partika, Steve Pieper, Katie Hayes)<br /> <br /> == Preparation ==<br /> <br /> # Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list<br /> # The NA-MIC engineering team will be discussing infrastructure projects in a kickoff TCON on April 15, 3pm ET. In the weeks following, new and old participants from the above mailing list will be invited to join to discuss their projects, so please make sure you are on it!<br /> # By 3pm ET on June 10, 2009: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.<br /> # By 3pm on June 17, 2010: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)<br /> ## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)<br /> ## Gather test images in any of the Data sharing resources we have (e.g. XNAT/MIDAS). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)<br /> ## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)<br /> # Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...<br /> # People doing Slicer related projects should come to project week with slicer built on your laptop.<br /> ## Projects to develop extension modules should work with the [http://viewvc.slicer.org/viewcvs.cgi/branches/Slicer-3-6/#dirlist Slicer-3-6 branch] (new code should not be checked into the branch).<br /> ## Projects to modify core behavior of slicer should be done on the [http://viewvc.slicer.org/viewcvs.cgi/trunk/ trunk].<br /> <br /> ==Attendee List==<br /> <br /> &lt;big&gt;'''NOTE:'''&lt;/big&gt; &lt;font color=&quot;maroon&quot;&gt;THIS IS AN AUTOMATICALLY GENERATED LIST FROM THE REGISTRATION WEBSITE. ATTENDEES SHOULD '''NOT''' EDIT THIS, BUT [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 REGISTER BY CLICKING HERE.]&lt;/font&gt; <br /> <br /> # Aucoin Nicole , BWH<br /> # Audette Michel , Kitware<br /> # Aylward Stephen , Kitware, Inc<br /> # Budin Francois , UNC<br /> # Burdette Everette , Acoustic MedSystems, Inc.<br /> # Chen Min , Johns Hopkins University<br /> # Datar Mansi , SCI Institute<br /> # Fedorov Andriy , Surgical Planning Lab<br /> # Fishbaugh James , SCI Institute<br /> # Gao Yi , Gerogia Tech<br /> # gouaillard alexandre , CoSMo Software<br /> # Gouttard Sylvain , SCI Institute<br /> # Haehn Daniel , University of Pennsylvania<br /> # Hageman Nathan , UCLA<br /> # Hahn Dieter , University Erlangen<br /> # Hata Nobuhiko , Brigham and Women's Hospital<br /> # Hayes Kathryn , Brigham and Women's Hospital<br /> # Holton Leslie , Medtronic Navigation<br /> # Johnson Hans , University of Iowa<br /> # Kapur Tina , Brigham and Women's Hospital<br /> # Kikinis Ron , Brigham and Women's Hospital<br /> # Kim Minjeong , UNC-Chapel Hill<br /> # Kolesov Ivan , Georgia Institute of Technology<br /> # Li Rui , MGH<br /> # Liu Haiying , Brigham and Women's Hospital<br /> # Magnotta Vincent , The University of Iowa<br /> # malaterre mathieu , CoSMo Software<br /> # Mastrogiacomo Katie , Brigham and Women's Hospital<br /> # Matsui Joy , University of Iowa<br /> # Meier Dominik , BWH, Boston MA<br /> # menze bjoern , CSAIL MIT<br /> # Norton Isaiah , BWH Neurosurgery<br /> # Paniagua Beatriz , University of North Caolina at Chapel Hill<br /> # Papademetris Xenophon , Yale University<br /> # Pathak Sudhir , Univeristy Of Pittsburgh<br /> # Peroni Marta , Politecnico di Milano, MGH, MIT<br /> # Pieper Steve , Isomics, Inc.<br /> # Plesniak Wendy , BWH<br /> # Pohl Kilian , IBM<br /> # Pujol Sonia , Brigham and Women's Hospital<br /> # Riklin Raviv Tammy , MIT, CSAIL<br /> # Ruiz Marco , UCSD<br /> # Scully Mark , The Mind Research Network<br /> # Sharp Greg , MGH<br /> # Shusharina Nadya , MGH<br /> # Smith Gareth , Wolfson Medical Imaging Centre (WMIC)<br /> # Spinczyk Dominik , Silesian University of Technology<br /> # Tao Xiaodong , GE Research<br /> # Ungi Tamas , Queen's University<br /> # Vachet Clement , UNC Chapel Hill<br /> # Veni Gopalkrishna , SCI Institute<br /> # Wassermann Demian , SPL/LMI/PNL<br /> # Wells Sandy , BWH<br /> # Wu Guorong , University of North Carolina at Chapel Hill</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_Summer_Project_Week_MRSImoduleAndSIVIC&diff=53142 2010-05-31T16:12:03Z

<p>Bmenze: Created page with '__NOTOC__ &lt;gallery&gt; Image:PW-SLC2010.png|Projects List Image:Spectral fitting mrs.png|Fitting metabolite models to the MRS signal of tumorou…'</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Fitting metabolite models to the MRS signal of tumorous brain tissue (top: baseline removal; bottom: resonance line model fits)<br /> Image:Mrsi parametric-map-NAA 3d.jpg|MRSI metabolite maps visualized in Slicer<br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Polina Golland<br /> * UCSF: Olson Beck, Jason Crane<br /> <br /> <br /> &lt;div style=&quot;margin: 20px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to integrate the [[http://wiki.na-mic.org/Wiki/index.php/2010_WinterProject_Week_MRSIModule | currently]] available MRSI quantitation routines with the [http://sourceforge.net/apps/trac/sivic/ | SIVIC] Slicer interface developed at the UCSF.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_Summer_Project_Week&diff=53141 2010-05-31T16:07:04Z

<p>Bmenze: /* Analysis */</p> <hr /> <div>__NOTOC__<br /> <br /> Back to [[Project Events]], [[Events]]<br /> <br /> [[Image:PW-MIT2010.png|300px]]<br /> <br /> <br /> ==Background==<br /> <br /> We are pleased to announce the 11th PROJECT WEEK of hands-on research and development activity for applications in Image-Guided Therapy, Neuroscience, and several additional areas of biomedical research that enable personalized medicine. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. <br /> <br /> Active preparation begins on Thursday, April 15th at 3pm ET, with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects. <br /> <br /> The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 30-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.<br /> <br /> This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January. <br /> <br /> A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].<br /> <br /> == Logistics ==<br /> *'''Dates:''' June 21-25, 2010<br /> *'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A &amp; B: 34-401A &amp; 34-401B]].<br /> *'''REGISTRATION:''' Please click [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 here] to do an on-line registration for the meeting that will allow you to pay by credit card, or send a check.<br /> *'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week). <br /> *'''Hotel:''' We have reserved a block of rooms at the Boston Marriott Cambridge Hotel, Two Cambridge Center, 50 Broadway, Cambridge, MA 02142. (Phone: 617.252.4405, Fax: 617.494.6565) [http://www.marriott.com/hotels/travel/BOSCB?groupCode=NAMNAMA&amp;app=resvlink&amp;fromDate=6/20/10&amp;toDate=6/25/10 Please click here to reserve.] You will be directed to the property's home page with the group code already entered in the appropriate field. All you need to do is enter your arrival date to begin the reservation process. <br /> <br /> ''' All reservations must be made by Tuesday, June 1, 2010 to receive the discounted rate of'''<br /> ''' $189/night/room (plus tax).'''<br /> ''' This rate is good only through June 1.'''<br /> <br /> Please note that if you try to reserve a room outside of the block on the shoulder nights via the link, you will be told that the group rate is not available for the duration of your stay. To reserve those rooms, which might not be at the group rate because it is based upon availability, please call Marriott Central Reservations at 1-800-228-9290. <br /> <br /> *Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.<br /> *For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]<br /> <br /> ==Agenda==<br /> === Monday, June 21, 2010 === <br /> ** noon-1pm lunch <br /> **1pm: Welcome (Ron Kikinis)<br /> ** 1:05-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template Wiki Template]) <br /> ** 3:30-5:30pm Tutorial: [[2010 Summer Project Week Breakout: Getting Started with Qt]] (Adam Weinrich, Nokia)<br /> <br /> === Tuesday, June 22, 2010 ===<br /> ** 8:30am breakfast<br /> **9-9:45am: NA-MIC Kit Update (Jim Miller) - include Module nomenclature (Extensions: cmdline vs loadable, Built-in), QT, Include Superbuild demo by Dave P.<br /> **9:45-10:30am 3D Slicer Update (Steve Pieper)<br /> **10:30-11am OpenIGTLink Update (Junichi Tokuda)<br /> **11-12pm: Slicer Hands-on Workshop (Randy Gollub, Sonia Pujol)<br /> ** noon lunch <br /> ** 1-3pm: Breakout Session: QT/Slicer (Steve, JC, J2) (w/ possible QnA with QT experts)<br /> ** 3pm: [[Summer_2010_Tutorial_Contest|Tutorial Contest Presentations]]<br /> ** 4-5pm [[2010 Summer Project Week Breakout Session: Data Management]] (Dan Marcus, Stephen Aylward)<br /> ** 5:30pm adjourn for day<br /> <br /> === Wednesday, June 23, 2010 ===<br /> ** 8:30am breakfast<br /> ** 9am-12pm Breakout Session: [[2010 Project Week Breakout Session: ITK]] (Luis Ibanez)<br /> ** noon lunch<br /> **12:45pm: [[Events:TutorialContestJune2010|Tutorial Contest Winner Announcement]]<br /> **1-3pm: Breakout Session: [[Microscopy_Image_Analysis]] (Sean Megason)<br /> **3-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:QA Training]] (Luis Ibanesz)<br /> **3-4pm: Breakout Session: [[2010 Summer Project Week Breakout Session:VTK Widget]] (Nicole, Kilian, JC)<br /> ** 5:30pm adjourn for day<br /> <br /> === Thursday, June 24, 2010 ===<br /> ** 8:30am breakfast<br /> ** 9am-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:OpenIGTLink|OpenIGTLink]]<br /> ** noon lunch<br /> ** 1-2pm: [[2010 Summer Project Week Breakout Session:GWE]] (Marco Ruiz)<br /> ** 5:30pm adjourn for day<br /> <br /> === Friday, June 25, 2010 === <br /> ** 8:30am breakfast<br /> ** 10am-noon: [[#Projects|Project Progress Updates]]<br /> *** Noon: Lunch boxes and adjourn by 1:30pm.<br /> ***We need to empty room by 1:30. You are welcome to use wireless in Stata.<br /> ***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]<br /> ***Next Project Week [[AHM_2011|in Utah, Fill in Dates]]<br /> <br /> ==Projects==<br /> <br /> <br /> <br /> === Segmentation ===<br /> *[[2010_Summer_Project_Week_Robust_Statistics_Segmenter_Slicer_Module|Robust Statistics Segmenter Slicer Module]] (Yi Gao, Allen Tannenbaum, Ron Kikinis)<br /> *[[2010_Summer_Project_Week_Multi_scale_Shape_Based_Segmentation_for_the_Hippocampus|Multi-scale Shape Based Segmentation for the Hippocampus]] (Yi Gao, Allen Tannenbaum)<br /> *[[2010_Summer_Project_Week/The Vascular Modeling Toolkit in 3D Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Luca Antiga, Kilian Pohl, Steve Pieper, Ron Kikinis)<br /> *[[2010_Summer_Project_Week_Prostate_MRI_Segmentation|Prostate Segmentation from MRI]] (Andriy Fedorov, Yi Gao)<br /> *[[2010_Summer_Project_Week_SPECTRE|SPECTRE: Skull Stripping integration with Slicer]] (Nicole Aucoin, Min Chen)<br /> <br /> === Registration ===<br /> *[[2010_Summer_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library]] (Dominik Meier)<br /> *[[2010_Summer_Project_Week_Fiducial_Deformable_Registration|Fiducial-based deformable image registration]] (Nadya Shusharina, Greg Sharp)<br /> *[[2010_Summer_Project_Week_HAMMER: Deformable Registration|HAMMER: Deformable Registration]] (Guorong Wu, Xiaodong Tao, Jim Miller, Dinggang Shen)<br /> *[[2010_Summer_Project_Week_Best_Regularization_Term_for_Demons_Registration_Algorithm|Best Regularization Term for Demons Registration Algorithm]] (Rui Li, Greg Sharp)<br /> *[[2010_Summer_Project_Week_RegistrationEvaluation|Evaluation of Registration in Slicer]] (James Fishbaugh, Guido Gerig, Domink Meier)<br /> *[[2010_Summer_Project_Week_MR_to_CT_Registration_for_Prostate_Brachytherapy_Planning|MR to CT Registration for Prostate Brachytherapy Planning]] (Andriy Fedorov, ?)<br /> *[[2010_Summer_Project_Week_MR_to_Ultrasound_Registration_Methodology|MR to Ultrasound Registration Methodology]] (Dieter Hahn, William Wells, Joachim Hornegger, Tina Kapur, Stephen Aylward)<br /> <br /> === IGT ===<br /> *Prostate Intervention(Junichi, Sam Song, Tamas Ungi?)<br /> * Liver Ablation (Haiying Liu)<br /> * BrainLab-Aurora HybridNav (Isaiah Norton)<br /> <br /> === Radiotherapy ===<br /> *[[2010_Summer_Project_Week_DICOM_RT|Dicom RT plugin]] (Greg Sharp)<br /> *[[2010_Summer_Project_Week_HandN_Cancer|Adaptive Radiation Therapy for H&amp;N cancer]] (Marta Peroni,Polina Golland,Greg Sharp)<br /> <br /> === Analysis ===<br /> *[[Microscopy Image Analysis]] (Arnaud Gelas)<br /> *Femoral Fracture Classification Brainstorming Session (Karl F, Vince M, Peter Karasev, Curt Lisle, Ron)<br /> *Cortical thickness analysis (Clement Vachet, Heather Cody Hazlett, Martin Styner)<br /> *[[2010_Summer_Project_Week_MRSImoduleAndSIVIC| MRSI module and SIVIC interface]] (B Menze, J Crane (UCSF), B Olson (UCSF), M Phothilimthana, P Golland)<br /> <br /> === Informatics ===<br /> * Computer Aided Photodynamic Therapy (Pietka, Spinczyk)<br /> <br /> === Diffusion ===<br /> *Fluid Mechanics Based Tractography (Nathan Hageman)<br /> <br /> === Python ===<br /> <br /> === Slicer Internals ===<br /> *Module Inventory (Steve, Jim)<br /> *Viewer Manager Factory (Alex Y., Kilian, Steve, Nicole)<br /> * [[2010 NAMIC Project week: Programmatic use of Volume Rendering module|Programmatic use of Volume Rendering module]] (Andrey Fedorov, Yanling Liu, Alex Yarmarkovich)<br /> <br /> === Execution Model ===<br /> <br /> ===Other NA-MIC Kit Internals===<br /> *VTKWidgets (JC, will, Schroeder, Nicole, Ron)<br /> *Superbuild (Dave Partika, Steve Pieper, Katie Hayes)<br /> <br /> == Preparation ==<br /> <br /> # Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list<br /> # The NA-MIC engineering team will be discussing infrastructure projects in a kickoff TCON on April 15, 3pm ET. In the weeks following, new and old participants from the above mailing list will be invited to join to discuss their projects, so please make sure you are on it!<br /> # By 3pm ET on June 10, 2009: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.<br /> # By 3pm on June 17, 2010: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)<br /> ## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)<br /> ## Gather test images in any of the Data sharing resources we have (e.g. XNAT/MIDAS). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)<br /> ## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)<br /> # Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...<br /> # People doing Slicer related projects should come to project week with slicer built on your laptop.<br /> ## Projects to develop extension modules should work with the [http://viewvc.slicer.org/viewcvs.cgi/branches/Slicer-3-6/#dirlist Slicer-3-6 branch] (new code should not be checked into the branch).<br /> ## Projects to modify core behavior of slicer should be done on the [http://viewvc.slicer.org/viewcvs.cgi/trunk/ trunk].<br /> <br /> ==Attendee List==<br /> <br /> &lt;big&gt;'''NOTE:'''&lt;/big&gt; &lt;font color=&quot;maroon&quot;&gt;THIS IS AN AUTOMATICALLY GENERATED LIST FROM THE REGISTRATION WEBSITE. ATTENDEES SHOULD '''NOT''' EDIT THIS, BUT [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 REGISTER BY CLICKING HERE.]&lt;/font&gt; <br /> <br /> # Aucoin Nicole , BWH<br /> # Audette Michel , Kitware<br /> # Aylward Stephen , Kitware, Inc<br /> # Budin Francois , UNC<br /> # Burdette Everette , Acoustic MedSystems, Inc.<br /> # Chen Min , Johns Hopkins University<br /> # Datar Mansi , SCI Institute<br /> # Fedorov Andriy , Surgical Planning Lab<br /> # Fishbaugh James , SCI Institute<br /> # Gao Yi , Gerogia Tech<br /> # gouaillard alexandre , CoSMo Software<br /> # Gouttard Sylvain , SCI Institute<br /> # Haehn Daniel , University of Pennsylvania<br /> # Hageman Nathan , UCLA<br /> # Hahn Dieter , University Erlangen<br /> # Hata Nobuhiko , Brigham and Women's Hospital<br /> # Hayes Kathryn , Brigham and Women's Hospital<br /> # Holton Leslie , Medtronic Navigation<br /> # Johnson Hans , University of Iowa<br /> # Kapur Tina , Brigham and Women's Hospital<br /> # Kikinis Ron , Brigham and Women's Hospital<br /> # Kim Minjeong , UNC-Chapel Hill<br /> # Kolesov Ivan , Georgia Institute of Technology<br /> # Li Rui , MGH<br /> # Liu Haiying , Brigham and Women's Hospital<br /> # Magnotta Vincent , The University of Iowa<br /> # malaterre mathieu , CoSMo Software<br /> # Mastrogiacomo Katie , Brigham and Women's Hospital<br /> # Matsui Joy , University of Iowa<br /> # Meier Dominik , BWH, Boston MA<br /> # menze bjoern , CSAIL MIT<br /> # Norton Isaiah , BWH Neurosurgery<br /> # Paniagua Beatriz , University of North Caolina at Chapel Hill<br /> # Papademetris Xenophon , Yale University<br /> # Pathak Sudhir , Univeristy Of Pittsburgh<br /> # Peroni Marta , Politecnico di Milano, MGH, MIT<br /> # Pieper Steve , Isomics, Inc.<br /> # Plesniak Wendy , BWH<br /> # Pohl Kilian , IBM<br /> # Pujol Sonia , Brigham and Women's Hospital<br /> # Riklin Raviv Tammy , MIT, CSAIL<br /> # Ruiz Marco , UCSD<br /> # Scully Mark , The Mind Research Network<br /> # Sharp Greg , MGH<br /> # Shusharina Nadya , MGH<br /> # Smith Gareth , Wolfson Medical Imaging Centre (WMIC)<br /> # Spinczyk Dominik , Silesian University of Technology<br /> # Tao Xiaodong , GE Research<br /> # Ungi Tamas , Queen's University<br /> # Vachet Clement , UNC Chapel Hill<br /> # Veni Gopalkrishna , SCI Institute<br /> # Wassermann Demian , SPL/LMI/PNL<br /> # Wells Sandy , BWH<br /> # Wu Guorong , University of North Carolina at Chapel Hill</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_Summer_Project_Week&diff=53140 2010-05-31T15:58:58Z

<p>Bmenze: /* Analysis */</p> <hr /> <div>__NOTOC__<br /> <br /> Back to [[Project Events]], [[Events]]<br /> <br /> [[Image:PW-MIT2010.png|300px]]<br /> <br /> <br /> ==Background==<br /> <br /> We are pleased to announce the 11th PROJECT WEEK of hands-on research and development activity for applications in Image-Guided Therapy, Neuroscience, and several additional areas of biomedical research that enable personalized medicine. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. <br /> <br /> Active preparation begins on Thursday, April 15th at 3pm ET, with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects. <br /> <br /> The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 30-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.<br /> <br /> This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January. <br /> <br /> A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].<br /> <br /> == Logistics ==<br /> *'''Dates:''' June 21-25, 2010<br /> *'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A &amp; B: 34-401A &amp; 34-401B]].<br /> *'''REGISTRATION:''' Please click [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 here] to do an on-line registration for the meeting that will allow you to pay by credit card, or send a check.<br /> *'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week). <br /> *'''Hotel:''' We have reserved a block of rooms at the Boston Marriott Cambridge Hotel, Two Cambridge Center, 50 Broadway, Cambridge, MA 02142. (Phone: 617.252.4405, Fax: 617.494.6565) [http://www.marriott.com/hotels/travel/BOSCB?groupCode=NAMNAMA&amp;app=resvlink&amp;fromDate=6/20/10&amp;toDate=6/25/10 Please click here to reserve.] You will be directed to the property's home page with the group code already entered in the appropriate field. All you need to do is enter your arrival date to begin the reservation process. <br /> <br /> ''' All reservations must be made by Tuesday, June 1, 2010 to receive the discounted rate of'''<br /> ''' $189/night/room (plus tax).'''<br /> ''' This rate is good only through June 1.'''<br /> <br /> Please note that if you try to reserve a room outside of the block on the shoulder nights via the link, you will be told that the group rate is not available for the duration of your stay. To reserve those rooms, which might not be at the group rate because it is based upon availability, please call Marriott Central Reservations at 1-800-228-9290. <br /> <br /> *Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.<br /> *For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]<br /> <br /> ==Agenda==<br /> === Monday, June 21, 2010 === <br /> ** noon-1pm lunch <br /> **1pm: Welcome (Ron Kikinis)<br /> ** 1:05-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template Wiki Template]) <br /> ** 3:30-5:30pm Tutorial: [[2010 Summer Project Week Breakout: Getting Started with Qt]] (Adam Weinrich, Nokia)<br /> <br /> === Tuesday, June 22, 2010 ===<br /> ** 8:30am breakfast<br /> **9-9:45am: NA-MIC Kit Update (Jim Miller) - include Module nomenclature (Extensions: cmdline vs loadable, Built-in), QT, Include Superbuild demo by Dave P.<br /> **9:45-10:30am 3D Slicer Update (Steve Pieper)<br /> **10:30-11am OpenIGTLink Update (Junichi Tokuda)<br /> **11-12pm: Slicer Hands-on Workshop (Randy Gollub, Sonia Pujol)<br /> ** noon lunch <br /> ** 1-3pm: Breakout Session: QT/Slicer (Steve, JC, J2) (w/ possible QnA with QT experts)<br /> ** 3pm: [[Summer_2010_Tutorial_Contest|Tutorial Contest Presentations]]<br /> ** 4-5pm [[2010 Summer Project Week Breakout Session: Data Management]] (Dan Marcus, Stephen Aylward)<br /> ** 5:30pm adjourn for day<br /> <br /> === Wednesday, June 23, 2010 ===<br /> ** 8:30am breakfast<br /> ** 9am-12pm Breakout Session: [[2010 Project Week Breakout Session: ITK]] (Luis Ibanez)<br /> ** noon lunch<br /> **12:45pm: [[Events:TutorialContestJune2010|Tutorial Contest Winner Announcement]]<br /> **1-3pm: Breakout Session: [[Microscopy_Image_Analysis]] (Sean Megason)<br /> **3-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:QA Training]] (Luis Ibanesz)<br /> **3-4pm: Breakout Session: [[2010 Summer Project Week Breakout Session:VTK Widget]] (Nicole, Kilian, JC)<br /> ** 5:30pm adjourn for day<br /> <br /> === Thursday, June 24, 2010 ===<br /> ** 8:30am breakfast<br /> ** 9am-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:OpenIGTLink|OpenIGTLink]]<br /> ** noon lunch<br /> ** 1-2pm: [[2010 Summer Project Week Breakout Session:GWE]] (Marco Ruiz)<br /> ** 5:30pm adjourn for day<br /> <br /> === Friday, June 25, 2010 === <br /> ** 8:30am breakfast<br /> ** 10am-noon: [[#Projects|Project Progress Updates]]<br /> *** Noon: Lunch boxes and adjourn by 1:30pm.<br /> ***We need to empty room by 1:30. You are welcome to use wireless in Stata.<br /> ***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]<br /> ***Next Project Week [[AHM_2011|in Utah, Fill in Dates]]<br /> <br /> ==Projects==<br /> <br /> <br /> <br /> === Segmentation ===<br /> *[[2010_Summer_Project_Week_Robust_Statistics_Segmenter_Slicer_Module|Robust Statistics Segmenter Slicer Module]] (Yi Gao, Allen Tannenbaum, Ron Kikinis)<br /> *[[2010_Summer_Project_Week_Multi_scale_Shape_Based_Segmentation_for_the_Hippocampus|Multi-scale Shape Based Segmentation for the Hippocampus]] (Yi Gao, Allen Tannenbaum)<br /> *[[2010_Summer_Project_Week/The Vascular Modeling Toolkit in 3D Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Luca Antiga, Kilian Pohl, Steve Pieper, Ron Kikinis)<br /> *[[2010_Summer_Project_Week_Prostate_MRI_Segmentation|Prostate Segmentation from MRI]] (Andriy Fedorov, Yi Gao)<br /> *[[2010_Summer_Project_Week_SPECTRE|SPECTRE: Skull Stripping integration with Slicer]] (Nicole Aucoin, Min Chen)<br /> <br /> === Registration ===<br /> *[[2010_Summer_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library]] (Dominik Meier)<br /> *[[2010_Summer_Project_Week_Fiducial_Deformable_Registration|Fiducial-based deformable image registration]] (Nadya Shusharina, Greg Sharp)<br /> *[[2010_Summer_Project_Week_HAMMER: Deformable Registration|HAMMER: Deformable Registration]] (Guorong Wu, Xiaodong Tao, Jim Miller, Dinggang Shen)<br /> *[[2010_Summer_Project_Week_Best_Regularization_Term_for_Demons_Registration_Algorithm|Best Regularization Term for Demons Registration Algorithm]] (Rui Li, Greg Sharp)<br /> *[[2010_Summer_Project_Week_RegistrationEvaluation|Evaluation of Registration in Slicer]] (James Fishbaugh, Guido Gerig, Domink Meier)<br /> *[[2010_Summer_Project_Week_MR_to_CT_Registration_for_Prostate_Brachytherapy_Planning|MR to CT Registration for Prostate Brachytherapy Planning]] (Andriy Fedorov, ?)<br /> *[[2010_Summer_Project_Week_MR_to_Ultrasound_Registration_Methodology|MR to Ultrasound Registration Methodology]] (Dieter Hahn, William Wells, Joachim Hornegger, Tina Kapur, Stephen Aylward)<br /> <br /> === IGT ===<br /> *Prostate Intervention(Junichi, Sam Song, Tamas Ungi?)<br /> * Liver Ablation (Haiying Liu)<br /> * BrainLab-Aurora HybridNav (Isaiah Norton)<br /> <br /> === Radiotherapy ===<br /> *[[2010_Summer_Project_Week_DICOM_RT|Dicom RT plugin]] (Greg Sharp)<br /> *[[2010_Summer_Project_Week_HandN_Cancer|Adaptive Radiation Therapy for H&amp;N cancer]] (Marta Peroni,Polina Golland,Greg Sharp)<br /> <br /> === Analysis ===<br /> *[[Microscopy Image Analysis]] (Arnaud Gelas)<br /> *Femoral Fracture Classification Brainstorming Session (Karl F, Vince M, Peter Karasev, Curt Lisle, Ron)<br /> *Cortical thickness analysis (Clement Vachet, Heather Cody Hazlett, Martin Styner)<br /> *[[MRSI module and SIVIC interface]] (Bjoern Menze, Polina Golland)<br /> <br /> === Informatics ===<br /> * Computer Aided Photodynamic Therapy (Pietka, Spinczyk)<br /> <br /> === Diffusion ===<br /> *Fluid Mechanics Based Tractography (Nathan Hageman)<br /> <br /> === Python ===<br /> <br /> === Slicer Internals ===<br /> *Module Inventory (Steve, Jim)<br /> *Viewer Manager Factory (Alex Y., Kilian, Steve, Nicole)<br /> * [[2010 NAMIC Project week: Programmatic use of Volume Rendering module|Programmatic use of Volume Rendering module]] (Andrey Fedorov, Yanling Liu, Alex Yarmarkovich)<br /> <br /> === Execution Model ===<br /> <br /> ===Other NA-MIC Kit Internals===<br /> *VTKWidgets (JC, will, Schroeder, Nicole, Ron)<br /> *Superbuild (Dave Partika, Steve Pieper, Katie Hayes)<br /> <br /> == Preparation ==<br /> <br /> # Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list<br /> # The NA-MIC engineering team will be discussing infrastructure projects in a kickoff TCON on April 15, 3pm ET. In the weeks following, new and old participants from the above mailing list will be invited to join to discuss their projects, so please make sure you are on it!<br /> # By 3pm ET on June 10, 2009: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.<br /> # By 3pm on June 17, 2010: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)<br /> ## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)<br /> ## Gather test images in any of the Data sharing resources we have (e.g. XNAT/MIDAS). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)<br /> ## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)<br /> # Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...<br /> # People doing Slicer related projects should come to project week with slicer built on your laptop.<br /> ## Projects to develop extension modules should work with the [http://viewvc.slicer.org/viewcvs.cgi/branches/Slicer-3-6/#dirlist Slicer-3-6 branch] (new code should not be checked into the branch).<br /> ## Projects to modify core behavior of slicer should be done on the [http://viewvc.slicer.org/viewcvs.cgi/trunk/ trunk].<br /> <br /> ==Attendee List==<br /> <br /> &lt;big&gt;'''NOTE:'''&lt;/big&gt; &lt;font color=&quot;maroon&quot;&gt;THIS IS AN AUTOMATICALLY GENERATED LIST FROM THE REGISTRATION WEBSITE. ATTENDEES SHOULD '''NOT''' EDIT THIS, BUT [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 REGISTER BY CLICKING HERE.]&lt;/font&gt; <br /> <br /> # Aucoin Nicole , BWH<br /> # Audette Michel , Kitware<br /> # Aylward Stephen , Kitware, Inc<br /> # Budin Francois , UNC<br /> # Burdette Everette , Acoustic MedSystems, Inc.<br /> # Chen Min , Johns Hopkins University<br /> # Datar Mansi , SCI Institute<br /> # Fedorov Andriy , Surgical Planning Lab<br /> # Fishbaugh James , SCI Institute<br /> # Gao Yi , Gerogia Tech<br /> # gouaillard alexandre , CoSMo Software<br /> # Gouttard Sylvain , SCI Institute<br /> # Haehn Daniel , University of Pennsylvania<br /> # Hageman Nathan , UCLA<br /> # Hahn Dieter , University Erlangen<br /> # Hata Nobuhiko , Brigham and Women's Hospital<br /> # Hayes Kathryn , Brigham and Women's Hospital<br /> # Holton Leslie , Medtronic Navigation<br /> # Johnson Hans , University of Iowa<br /> # Kapur Tina , Brigham and Women's Hospital<br /> # Kikinis Ron , Brigham and Women's Hospital<br /> # Kim Minjeong , UNC-Chapel Hill<br /> # Kolesov Ivan , Georgia Institute of Technology<br /> # Li Rui , MGH<br /> # Liu Haiying , Brigham and Women's Hospital<br /> # Magnotta Vincent , The University of Iowa<br /> # malaterre mathieu , CoSMo Software<br /> # Mastrogiacomo Katie , Brigham and Women's Hospital<br /> # Matsui Joy , University of Iowa<br /> # Meier Dominik , BWH, Boston MA<br /> # menze bjoern , CSAIL MIT<br /> # Norton Isaiah , BWH Neurosurgery<br /> # Paniagua Beatriz , University of North Caolina at Chapel Hill<br /> # Papademetris Xenophon , Yale University<br /> # Pathak Sudhir , Univeristy Of Pittsburgh<br /> # Peroni Marta , Politecnico di Milano, MGH, MIT<br /> # Pieper Steve , Isomics, Inc.<br /> # Plesniak Wendy , BWH<br /> # Pohl Kilian , IBM<br /> # Pujol Sonia , Brigham and Women's Hospital<br /> # Riklin Raviv Tammy , MIT, CSAIL<br /> # Ruiz Marco , UCSD<br /> # Scully Mark , The Mind Research Network<br /> # Sharp Greg , MGH<br /> # Shusharina Nadya , MGH<br /> # Smith Gareth , Wolfson Medical Imaging Centre (WMIC)<br /> # Spinczyk Dominik , Silesian University of Technology<br /> # Tao Xiaodong , GE Research<br /> # Ungi Tamas , Queen's University<br /> # Vachet Clement , UNC Chapel Hill<br /> # Veni Gopalkrishna , SCI Institute<br /> # Wassermann Demian , SPL/LMI/PNL<br /> # Wells Sandy , BWH<br /> # Wu Guorong , University of North Carolina at Chapel Hill</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_Summer_Project_Week&diff=52878 2010-05-22T15:31:02Z

<p>Bmenze: /* Analysis */</p> <hr /> <div>__NOTOC__<br /> <br /> Back to [[Project Events]], [[Events]]<br /> <br /> [[Image:PW-MIT2010.png|300px]]<br /> <br /> <br /> ==Background==<br /> <br /> We are pleased to announce the 11th PROJECT WEEK of hands-on research and development activity for applications in Image-Guided Therapy, Neuroscience, and several additional areas of biomedical research that enable personalized medicine. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. <br /> <br /> Active preparation begins on Thursday, April 15th at 3pm ET, with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects. <br /> <br /> The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 30-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.<br /> <br /> This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January. <br /> <br /> A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].<br /> <br /> == Logistics ==<br /> *'''Dates:''' June 21-25, 2010<br /> *'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A &amp; B: 34-401A &amp; 34-401B]].<br /> *'''REGISTRATION:''' Please click [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 here] to do an on-line registration for the meeting that will allow you to pay by credit card, or send a check.<br /> *'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week). <br /> *'''Hotel:''' We have reserved a block of rooms at the Boston Marriott Cambridge Hotel, Two Cambridge Center, 50 Broadway, Cambridge, MA 02142. (Phone: 617.252.4405, Fax: 617.494.6565) [http://www.marriott.com/hotels/travel/BOSCB?groupCode=NAMNAMA&amp;app=resvlink&amp;fromDate=6/20/10&amp;toDate=6/25/10 Please click here to reserve.] You will be directed to the property's home page with the group code already entered in the appropriate field. All you need to do is enter your arrival date to begin the reservation process. <br /> <br /> ''' All reservations must be made by Tuesday, June 1, 2010 to receive the discounted rate of'''<br /> ''' $189/night/room (plus tax).'''<br /> ''' This rate is good only through June 1.'''<br /> <br /> Please note that if you try to reserve a room outside of the block on the shoulder nights via the link, you will be told that the group rate is not available for the duration of your stay. To reserve those rooms, which might not be at the group rate because it is based upon availability, please call Marriott Central Reservations at 1-800-228-9290. <br /> <br /> *Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.<br /> *For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]<br /> <br /> ==Agenda==<br /> === Monday, June 21, 2010 === <br /> ** noon-1pm lunch <br /> **1pm: Welcome (Ron Kikinis)<br /> ** 1:05-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template Wiki Template]) <br /> ** 3:30-5:30pm Tutorial: [[2010 Summer Project Week Breakout: Getting Started with Qt]] (Adam Weinrich, Nokia)<br /> <br /> === Tuesday, June 22, 2010 ===<br /> ** 8:30am breakfast<br /> **9-9:45am: NA-MIC Kit Update (Jim Miller) - include Module nomenclature (Extensions: cmdline vs loadable, Built-in), QT, Include Superbuild demo by Dave P.<br /> **9:45-10:30am 3D Slicer Update (Steve Pieper)<br /> **10:30-11am OpenIGTLink Update (Junichi Tokuda)<br /> **11-12pm: Slicer Hands-on Workshop (Randy Gollub, Sonia Pujol)<br /> ** noon lunch <br /> ** 1-3pm: Breakout Session: QT/Slicer (Steve, JC, J2) (w/ possible QnA with QT experts)<br /> ** 3pm: [[Summer_2010_Tutorial_Contest|Tutorial Contest Presentations]]<br /> ** 4-5pm [[2010 Summer Project Week Breakout Session: Data Management]] (Dan Marcus, Stephen Aylward)<br /> ** 5:30pm adjourn for day<br /> <br /> === Wednesday, June 23, 2010 ===<br /> ** 8:30am breakfast<br /> ** 9am-12pm Breakout Session: [[2010 Project Week Breakout Session: ITK]] (Luis Ibanez)<br /> ** noon lunch<br /> **12:45pm: [[Events:TutorialContestJune2010|Tutorial Contest Winner Announcement]]<br /> **1-3pm: Breakout Session: [[Microscopy_Image_Analysis]] (Sean Megason)<br /> **3-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:QA Training]] (Luis Ibanesz)<br /> **3-4pm: Breakout Session: [[2010 Summer Project Week Breakout Session:VTK Widget]] (Nicole, Kilian, JC)<br /> ** 5:30pm adjourn for day<br /> <br /> === Thursday, June 24, 2010 ===<br /> ** 8:30am breakfast<br /> ** 9am-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:OpenIGTLink|OpenIGTLink]]<br /> ** noon lunch<br /> ** 1-2pm: [[2010 Summer Project Week Breakout Session:GWE]] (Marco Ruiz)<br /> ** 5:30pm adjourn for day<br /> <br /> === Friday, June 25, 2010 === <br /> ** 8:30am breakfast<br /> ** 10am-noon: [[#Projects|Project Progress Updates]]<br /> *** Noon: Lunch boxes and adjourn by 1:30pm.<br /> ***We need to empty room by 1:30. You are welcome to use wireless in Stata.<br /> ***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]<br /> ***Next Project Week [[AHM_2011|in Utah, Fill in Dates]]<br /> <br /> ==Projects==<br /> <br /> <br /> <br /> === Segmentation ===<br /> *[[2010_Summer_Project_Week_Robust_Statistics_Segmenter_Slicer_Module|Robust Statistics Segmenter Slicer Module]] (Yi Gao, Allen Tannenbaum, Ron Kikinis)<br /> *[[2010_Summer_Project_Week_Multi_scale_Shape_Based_Segmentation_for_the_Hippocampus|Multi-scale Shape Based Segmentation for the Hippocampus]] (Yi Gao, Allen Tannenbaum)<br /> *[[2010_Summer_Project_Week/The Vascular Modeling Toolkit in 3D Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Luca Antiga, Kilian Pohl, Steve Pieper, Ron Kikinis)<br /> *[[2010_Summer_Project_Week_Prostate_MRI_Segmentation|Prostate Segmentation from MRI]] (Andriy Fedorov, Yi Gao)<br /> <br /> === Registration ===<br /> *[[2010_Summer_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library]] (Dominik Meier)<br /> *[[2010_Summer_Project_Week_Fiducial_Deformable_Registration|Fiducial-based deformable image registration]] (Nadya Shusharina, Greg Sharp)<br /> *[[2010_Summer_Project_Week_HAMMER: Deformable Registration|HAMMER: Deformable Registration]] (Guorong Wu, Xiaodong Tao, Jim Miller, Dinggang Shen)<br /> *[[2010_Summer_Project_Week_Best_Regularization_Term_for_Demons_Registration_Algorithm|Best Regularization Term for Demons Registration Algorithm]] (Rui Li, Greg Sharp)<br /> *[[2010_Summer_Project_Week_RegistrationEvaluation|Evaluation of Registration in Slicer]] (James Fishbaugh, Guido Gerig, Domink Meier)<br /> *[[2010_Summer_Project_Week_MR_to_CT_Registration_for_Prostate_Brachytherapy_Planning|MR to CT Registration for Prostate Brachytherapy Planning]] (Andriy Fedorov, ?)<br /> *[[2010_Summer_Project_Week_MR_to_Ultrasound_Registration_Methodology|MR to Ultrasound Registration Methodology]] (Dieter Hahn, William Wells, Joachim Hornegger, Tina Kapur, Stephen Aylward)<br /> <br /> === IGT ===<br /> *Prostate Intervention(Junichi, Sam Song, Tamas Ungi?)<br /> * Liver Ablation (Haiying Liu)<br /> * BrainLab-Aurora HybridNav (Isaiah Norton)<br /> <br /> === Radiotherapy ===<br /> *[[2010_Summer_Project_Week_DICOM_RT|Dicom RT plugin]] (Greg Sharp)<br /> *[[2010_Summer_Project_Week_HandN_Cancer|Adaptive Radiation Therapy for H&amp;N cancer]] (Marta Peroni,Polina Golland,Greg Sharp)<br /> <br /> === Analysis ===<br /> *[[Microscopy Image Analysis]] (Arnaud Gelas)<br /> *Femoral Fracture Classification Brainstorming Session (Karl F, Vince M, Peter Karasev, Curt Lisle, Ron)<br /> *Cortical thickness analysis (Clement Vachet, Heather Cody Hazlett, Martin Styner)<br /> *MRSI module and SIVIC interface (Bjoern Menze, Polina Golland)<br /> <br /> === Informatics ===<br /> * Computer Aided Photodynamic Therapy (Pietka, Spinczyk)<br /> <br /> === Diffusion ===<br /> *Fluid Mechanics Based Tractography (Nathan Hageman)<br /> <br /> === Python ===<br /> <br /> === Slicer Internals ===<br /> *Module Inventory (Steve, Jim)<br /> *Viewer Manager Factory (Alex Y., Kilian, Steve, Nicole)<br /> <br /> === Execution Model ===<br /> <br /> ===Other NA-MIC Kit Internals===<br /> *VTKWidgets (JC, Nicole)<br /> *Superbuild (Dave Partika)<br /> <br /> == Preparation ==<br /> <br /> # Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list<br /> # The NA-MIC engineering team will be discussing infrastructure projects in a kickoff TCON on April 15, 3pm ET. In the weeks following, new and old participants from the above mailing list will be invited to join to discuss their projects, so please make sure you are on it!<br /> # By 3pm ET on June 10, 2009: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.<br /> # By 3pm on June 17, 2010: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)<br /> ## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)<br /> ## Gather test images in any of the Data sharing resources we have (e.g. XNAT/MIDAS). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)<br /> ## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)<br /> # Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...<br /> # People doing Slicer related projects should come to project week with slicer built on your laptop.<br /> ## Projects to develop extension modules should work with the [http://viewvc.slicer.org/viewcvs.cgi/branches/Slicer-3-6/#dirlist Slicer-3-6 branch] (new code should not be checked into the branch).<br /> ## Projects to modify core behavior of slicer should be done on the [http://viewvc.slicer.org/viewcvs.cgi/trunk/ trunk].<br /> <br /> ==Attendee List==<br /> <br /> THIS IS AN AUTOMATICALLY GENERATED LIST FROM THE REGISTRATION WEBSITE. ATTENDEES SHOULD NOT EDIT THIS, BUT [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 REGISTER BY CLICKING HERE.]<br /> <br /> {| class=&quot;wikitable sortable&quot; border=&quot;1&quot;<br /> |+ Registered Attendee List<br /> |-<br /> ! class=&quot;unsortable&quot;| Count !! Name !! Institution <br /> |-<br /> | 1 || Audette Michel || Kitware<br /> |-<br /> | 2 || Aylward Stephen || Kitware, Inc<br /> |-<br /> | 3 || Burdette Everette || Acoustic MedSystems, Inc.<br /> |-<br /> | 4 || Chen Min || Johns Hopkins University<br /> |-<br /> | 5 || Datar Mansi || SCI Institute<br /> |-<br /> | 6 || Haehn Daniel || University of Pennsylvania<br /> |-<br /> | 7 || Johnson Hans || University of Iowa<br /> |-<br /> | 8 || Kapur Tina || Brigham and Women's Hospital<br /> |-<br /> | 9 || Kikinis Ron || Brigham and Women's Hospital<br /> |-<br /> | 10 || Liu Haiying || Brigham and Women's Hospital<br /> |-<br /> | 11 || Mastrogiacomo Katie || Brigham and Women's Hospital<br /> |-<br /> | 12 || Matsui Joy || University of Iowa<br /> |-<br /> | 13 || Meier Dominik || BWH, Boston MA<br /> |-<br /> | 14 || Norton Isaiah || BWH Neurosurgery<br /> |-<br /> | 15 || Paniagua Beatriz || University of North Caolina at Chapel Hill<br /> |-<br /> | 16 || Papademetris Xenophon || Yale University<br /> |-<br /> | 17 || Pathak Sudhir || Univeristy Of Pittsburgh<br /> |-<br /> | 18 || Peroni Marta || Politecnico di Milano<br /> |-<br /> | 19 || Pohl Kilian || IBM<br /> |-<br /> | 20 || Pujol Sonia || Brigham and Women's Hospital<br /> |-<br /> | 21 || Riklin Raviv Tammy || MIT, CSAIL<br /> |-<br /> | 22 || Ruiz Marco || UCSD<br /> |-<br /> | 23 || Scully Mark || The Mind Research Network<br /> |-<br /> | 24 || Spinczyk Dominik || Silesian University of Technology<br /> |-<br /> | 25 || Vachet Clement || UNC Chapel Hill<br /> |-<br /> | 26 || Veni Gopalkrishna || SCI Institute<br /> |-<br /> |}</div>

Bmenze https://www.na-mic.org/w/index.php?title=Projects:TumorModeling&diff=50291 2010-03-13T16:31:38Z

<p>Bmenze: /* Key Investigators */</p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]<br /> __NOTOC__<br /> = Modeling tumor growth in patients with glioma =<br /> We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth.<br /> <br /> <br /> This aims at two directions: First, it aims at making complex information from longitudinal multimodal data set accessible for diagnostic radiology through physiological models. This will allow to estimate features such as degree of infiltration, speed of growth, or mass effect in a quantitative fashion; for therapy it will allow to identify regions at risk for progression. Second, it aims at providing the means to test different mavroscopic tumor models from theoretical biology on real clinical data.<br /> <br /> <br /> To realize these aims, the project comprises a number of ascpects -- automated segmentation of tumors in large multimodal image data sets, making information of different MR image modalities accessible for the tumor model, with a focus on the processing of magnetic resonance spectroscopic images (MRSI), and the development of methods for the image-based estimation of parameters in reaction-diffusion type models of tumor growth.<br /> <br /> <br /> [[Image:Multimodal_glioma.png|thumb|center|600px| Figure 1: Multi-modal image data from a patient with low-grade glioma. A large number of different modalities and derived parameter volumes are acquired during the monitoring of tumor growth.]]<br /> <br /> == Segmenting tumors in large multimodal data sets ==<br /> To segment all MR image volumes available for a patient we developed an approach for learning patient-specific lesion atlases (Figure 2) with limited user interaction. Figure 2 shows the manual segmentation of the tumor from different raters (red, green, blue) and the automatic segmentation using the patient-specific lesion atlas (black) in T1-MRI, T1-MRI and the fractional anisotropy map from DTI. <br /> <br /> <br /> [[Image:Tumor_segmentation_lesion_atlas.png|thumb|center|600px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]<br /> <br /> == Processing magnetic resonance spectroscopic images ==<br /> To make the metabolic information of magnetic resonance spectroscopic images available for modeling the evolution of glioma growth we are implementing an [http://wiki.na-mic.org/Wiki/index.php/2009_Summer_Project_Week_MRSI-Module MRSI processing module] for Slicer.<br /> <br /> <br /> = Key Investigators =<br /> * MIT: [http://people.csail.mit.edu/menze Bjoern Menze], [http://people.csail.mit.edu/tammy Tammy Riklin Raviv], [http://people.csail.mit.edu/koen Koen Van Leemput], [http://people.csail.mit.edu/polina Polina Golland]<br /> * INRIA Sophia-Antipolis, France: Ezequiel Geremia, Olivier Clatz, Nicholas Ayache<br /> * DKFZ Heidelberg, Germany: Bram Stieltjes, Marc-Andre Weber<br /> <br /> = Publications =<br /> *[http://www.na-mic.org/publications/pages/display?search=Projects%3ATumorModeling&amp;submit=Search&amp;words=all&amp;title=checked&amp;keywords=checked&amp;authors=checked&amp;abstract=checked&amp;sponsors=checked&amp;searchbytag=checked| NA-MIC Publications Database]</div>

Bmenze https://www.na-mic.org/w/index.php?title=Projects:TumorModeling&diff=48789 2010-02-16T21:08:44Z

<p>Bmenze: /* Publications */</p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]<br /> __NOTOC__<br /> = Modeling tumor growth in patients with glioma =<br /> We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth.<br /> <br /> <br /> This aims at two directions: First, it aims at making complex information from longitudinal multimodal data set accessible for diagnostic radiology through physiological models. This will allow to estimate features such as degree of infiltration, speed of growth, or mass effect in a quantitative fashion; for therapy it will allow to identify regions at risk for progression. Second, it aims at providing the means to test different mavroscopic tumor models from theoretical biology on real clinical data.<br /> <br /> <br /> To realize these aims, the project comprises a number of ascpects -- automated segmentation of tumors in large multimodal image data sets, making information of different MR image modalities accessible for the tumor model, with a focus on the processing of magnetic resonance spectroscopic images (MRSI), and the development of methods for the image-based estimation of parameters in reaction-diffusion type models of tumor growth.<br /> <br /> <br /> [[Image:Multimodal_glioma.png|thumb|center|600px| Figure 1: Multi-modal image data from a patient with low-grade glioma. A large number of different modalities and derived parameter volumes are acquired during the monitoring of tumor growth.]]<br /> <br /> == Segmenting tumors in large multimodal data sets ==<br /> To segment all MR image volumes available for a patient we developed an approach for learning patient-specific lesion atlases (Figure 2) with limited user interaction. Figure 2 shows the manual segmentation of the tumor from different raters (red, green, blue) and the automatic segmentation using the patient-specific lesion atlas (black) in T1-MRI, T1-MRI and the fractional anisotropy map from DTI. <br /> <br /> <br /> [[Image:Tumor_segmentation_lesion_atlas.png|thumb|center|600px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]<br /> <br /> == Processing magnetic resonance spectroscopic images ==<br /> To make the metabolic information of magnetic resonance spectroscopic images available for modeling the evolution of glioma growth we are implementing an [http://wiki.na-mic.org/Wiki/index.php/2009_Summer_Project_Week_MRSI-Module MRSI processing module] for Slicer.<br /> <br /> <br /> = Key Investigators =<br /> * MIT: [http://people.csail.mit.edu/menze Bjoern Menze], Tammy Riklin Raviv, Koen Van Leemput, Polina Golland<br /> * INRIA Sophia-Antipolis, France: Ezequiel Geremia, Olivier Clatz, Nicholas Ayache<br /> * DKFZ Heidelberg, Germany: Bram Stieltjes, Marc-Andre Weber<br /> <br /> = Publications =<br /> *[http://www.na-mic.org/publications/pages/display?search=Projects%3ATumorModeling&amp;submit=Search&amp;words=all&amp;title=checked&amp;keywords=checked&amp;authors=checked&amp;abstract=checked&amp;sponsors=checked&amp;searchbytag=checked| NA-MIC Publications Database]</div>

Bmenze https://www.na-mic.org/w/index.php?title=Projects:LatentAtlasSegmentation&diff=48788 2010-02-16T21:06:52Z

<p>Bmenze: /* Publications */</p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],<br /> __NOTOC__<br /> = Joint Segmentation of Image Ensembles via Latent Atlases =<br /> <br /> <br /> Spatial priors, such as probabilistic atlases, play an important role<br /> in MRI segmentation. The atlases are typically generated by<br /> averaging manual labels of aligned brain regions across different<br /> subjects. However, the availability of comprehensive, reliable and suitable<br /> manual segmentations is limited. We therefore propose a joint segmentation of<br /> corresponding, aligned structures in the entire population<br /> that does not require a probability atlas.<br /> Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble.<br /> The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs)<br /> and energy minimization criteria.<br /> We evaluate the method by segmenting 50<br /> brain MR volumes. Segmentation accuracy for cortical and subcortical<br /> structures approaches the quality of state-of-the-art atlas-based segmentation results,<br /> suggesting that the ''latent atlas'' method is a reasonable alternative when<br /> existing atlases are not compatible with the data to be processed.<br /> <br /> <br /> = Description =<br /> Here we propose and demonstrate a method that does not use a set of<br /> training images or probabilistic atlases as priors. Instead we extract an ensemble of corresponding structures<br /> simultaneously. The evolving segmentation of the entire image set<br /> supports each of the individual segmentations. In practice, a subset<br /> of the model parameters, called the spatial parameters, is inferred<br /> as part of the joint segmentation processes. These latent spatial<br /> parameters, which can be viewed as a `dynamic atlas', are estimated exclusively<br /> from the data at hand and a single manual segmentation.<br /> The updated estimates of the latent atlas are used iteratively as Markov Random Field (MRF) priors on the tissue labels. The single node potentials term of the MRF model is formulated as a spatial constraint in a level-set functional for segmentation.<br /> The main novelty of the suggested method with respect to other group-wise segmentation methods is the consistent statistically-driven variational framework for MR ensemble segmentation by estimating a latent atlas.<br /> <br /> <br /> <br /> == Results ==<br /> We test the proposed approach on 50 MR<br /> brain scans. Some of the subjects in this set are diagnosed with the first episode schizophrenia or affective disorder.<br /> The MR images (T1, 256X256X128 volume, 0.9375 X 0.9375 X 1.5 mm<br /> voxel size) were acquired by a 1.5-T General Electric Scanner.<br /> In addition to the MR volumes, manual<br /> segmentations of three structures (superior temporal<br /> gyrus, amygdala, and hippocampus) in each hemisphere were provided<br /> for each of the 50 individuals and used to evaluate<br /> the quality of the automatic segmentation results. MR images are preprocessed by skull stripping.<br /> The volumes were aligned using B-spline registration.<br /> <br /> [[Image:LatentAtlasSeg.jpg | center | 700px]]<br /> <br /> ''Three cross-sections of 3D segmentation of Hippocampus, Amygdala and Superior Temporal Gyrus in the left and the right hemispheres. Automatic segmentation is shown in red. Manual segmentation is shown in blue. Fourth column: Coronal views of the resulting atlases for each pair of structures.''<br /> <br /> == Brain Tumor Modeling ==<br /> We have applied the proposed algorithm to a longitudinal multi-modal Patient specific brain scans for brain tumor segmentation and modeling. In this particular application the inferred spatial parameters estimate the patient's latent anatomy. No prior information is assumed but a couple of mouse clicks that define a sphere that initializes the segmentation of the first time point. To learn more please refer to the [[Projects:TumorModeling| brain tumor modeling]] page.<br /> <br /> = Key Investigators =<br /> * MIT: [[http://people.csail.mit.edu/tammy/ Tammy Riklin Raviv]], Polina Golland, Koen Van Leemput<br /> * Harvard: William M. Wells, Ron Kikinis, Martha Shenton, Sylvain Bouix<br /> <br /> <br /> = Publications =<br /> <br /> *[http://www.na-mic.org/publications/pages/display?search=Projects%3ALatentAtlasSegmentation&amp;submit=Search&amp;words=all&amp;title=checked&amp;keywords=checked&amp;authors=checked&amp;abstract=checked&amp;sponsors=checked&amp;searchbytag=checked| NA-MIC Publications Database]</div>

Bmenze https://www.na-mic.org/w/index.php?title=Projects:LatentAtlasSegmentation&diff=48753 2010-02-16T20:30:12Z

<p>Bmenze: /* Publications */</p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],<br /> __NOTOC__<br /> = Joint Segmentation of Image Ensembles via Latent Atlases =<br /> <br /> <br /> Spatial priors, such as probabilistic atlases, play an important role<br /> in MRI segmentation. The atlases are typically generated by<br /> averaging manual labels of aligned brain regions across different<br /> subjects. However, the availability of comprehensive, reliable and suitable<br /> manual segmentations is limited. We therefore propose a joint segmentation of<br /> corresponding, aligned structures in the entire population<br /> that does not require a probability atlas.<br /> Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble.<br /> The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs)<br /> and energy minimization criteria.<br /> We evaluate the method by segmenting 50<br /> brain MR volumes. Segmentation accuracy for cortical and subcortical<br /> structures approaches the quality of state-of-the-art atlas-based segmentation results,<br /> suggesting that the ''latent atlas'' method is a reasonable alternative when<br /> existing atlases are not compatible with the data to be processed.<br /> <br /> <br /> = Description =<br /> Here we propose and demonstrate a method that does not use a set of<br /> training images or probabilistic atlases as priors. Instead we extract an ensemble of corresponding structures<br /> simultaneously. The evolving segmentation of the entire image set<br /> supports each of the individual segmentations. In practice, a subset<br /> of the model parameters, called the spatial parameters, is inferred<br /> as part of the joint segmentation processes. These latent spatial<br /> parameters, which can be viewed as a `dynamic atlas', are estimated exclusively<br /> from the data at hand and a single manual segmentation.<br /> The updated estimates of the latent atlas are used iteratively as Markov Random Field (MRF) priors on the tissue labels. The single node potentials term of the MRF model is formulated as a spatial constraint in a level-set functional for segmentation.<br /> The main novelty of the suggested method with respect to other group-wise segmentation methods is the consistent statistically-driven variational framework for MR ensemble segmentation by estimating a latent atlas.<br /> <br /> <br /> <br /> == Results ==<br /> We test the proposed approach on 50 MR<br /> brain scans. Some of the subjects in this set are diagnosed with the first episode schizophrenia or affective disorder.<br /> The MR images (T1, 256X256X128 volume, 0.9375 X 0.9375 X 1.5 mm<br /> voxel size) were acquired by a 1.5-T General Electric Scanner.<br /> In addition to the MR volumes, manual<br /> segmentations of three structures (superior temporal<br /> gyrus, amygdala, and hippocampus) in each hemisphere were provided<br /> for each of the 50 individuals and used to evaluate<br /> the quality of the automatic segmentation results. MR images are preprocessed by skull stripping.<br /> The volumes were aligned using B-spline registration.<br /> <br /> [[Image:LatentAtlasSeg.jpg | center | 700px]]<br /> <br /> ''Three cross-sections of 3D segmentation of Hippocampus, Amygdala and Superior Temporal Gyrus in the left and the right hemispheres. Automatic segmentation is shown in red. Manual segmentation is shown in blue. Fourth column: Coronal views of the resulting atlases for each pair of structures.''<br /> <br /> == Brain Tumor Modeling ==<br /> We have applied the proposed algorithm to a longitudinal multi-modal Patient specific brain scans for brain tumor segmentation and modeling. In this particular application the inferred spatial parameters estimate the patient's latent anatomy. No prior information is assumed but a couple of mouse clicks that define a sphere that initializes the segmentation of the first time point. To learn more please refer to the [[Projects:TumorModeling| brain tumor modeling]] page.<br /> <br /> = Key Investigators =<br /> * MIT: [[http://people.csail.mit.edu/tammy/ Tammy Riklin Raviv]], Polina Golland, Koen Van Leemput<br /> * Harvard: William M. Wells, Ron Kikinis, Martha Shenton, Sylvain Bouix<br /> <br /> <br /> = Publications =<br /> ''In Press''<br /> <br /> * T. Riklin Raviv, B.H. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W. M. Wells III and P. Golland. Joint Segmentation using Patient specific Latent Anatomy Model. In Proc. MICCAI Workshop on Probabilistic Models for Medical Image Analysis, 244-255, 2009. <br /> <br /> '' In Print ''<br /> <br /> *[http://www.na-mic.org/publications/pages/display?search=Projects%3ALatentAtlasSegmentation&amp;submit=Search&amp;words=all&amp;title=checked&amp;keywords=checked&amp;authors=checked&amp;abstract=checked&amp;sponsors=checked&amp;searchbytag=checked| NA-MIC Publications Database]</div>

Bmenze https://www.na-mic.org/w/index.php?title=Projects:TumorModeling&diff=48750 2010-02-16T20:21:24Z

<p>Bmenze: /* Publications */</p> <hr /> <div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]<br /> __NOTOC__<br /> = Modeling tumor growth in patients with glioma =<br /> We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth.<br /> <br /> <br /> This aims at two directions: First, it aims at making complex information from longitudinal multimodal data set accessible for diagnostic radiology through physiological models. This will allow to estimate features such as degree of infiltration, speed of growth, or mass effect in a quantitative fashion; for therapy it will allow to identify regions at risk for progression. Second, it aims at providing the means to test different mavroscopic tumor models from theoretical biology on real clinical data.<br /> <br /> <br /> To realize these aims, the project comprises a number of ascpects -- automated segmentation of tumors in large multimodal image data sets, making information of different MR image modalities accessible for the tumor model, with a focus on the processing of magnetic resonance spectroscopic images (MRSI), and the development of methods for the image-based estimation of parameters in reaction-diffusion type models of tumor growth.<br /> <br /> <br /> [[Image:Multimodal_glioma.png|thumb|center|600px| Figure 1: Multi-modal image data from a patient with low-grade glioma. A large number of different modalities and derived parameter volumes are acquired during the monitoring of tumor growth.]]<br /> <br /> == Segmenting tumors in large multimodal data sets ==<br /> To segment all MR image volumes available for a patient we developed an approach for learning patient-specific lesion atlases (Figure 2) with limited user interaction. Figure 2 shows the manual segmentation of the tumor from different raters (red, green, blue) and the automatic segmentation using the patient-specific lesion atlas (black) in T1-MRI, T1-MRI and the fractional anisotropy map from DTI. <br /> <br /> <br /> [[Image:Tumor_segmentation_lesion_atlas.png|thumb|center|600px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]<br /> <br /> == Processing magnetic resonance spectroscopic images ==<br /> To make the metabolic information of magnetic resonance spectroscopic images available for modeling the evolution of glioma growth we are implementing an [http://wiki.na-mic.org/Wiki/index.php/2009_Summer_Project_Week_MRSI-Module MRSI processing module] for Slicer.<br /> <br /> <br /> = Key Investigators =<br /> * MIT: [http://people.csail.mit.edu/menze Bjoern Menze], Tammy Riklin Raviv, Koen Van Leemput, Polina Golland<br /> * INRIA Sophia-Antipolis, France: Ezequiel Geremia, Olivier Clatz, Nicholas Ayache<br /> * DKFZ Heidelberg, Germany: Bram Stieltjes, Marc-Andre Weber<br /> <br /> = Publications =<br /> * T. Riklin Raviv, B.H. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W. M. Wells III and P. Golland. ''Joint Segmentation using Patient specific Latent Anatomy Model''. In Proc. MICCAI Workshop on Probabilistic Models for Medical Image Analysis (PMMIA), 244-255, 2009.</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_WinterProject_Week_MRSIModule&diff=47565 2010-01-08T16:12:10Z

<p>Bmenze: </p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]<br /> Image:Spectral fitting mrs.png|Fitting metabolite models to the MRS signal of tumorous brain tissue (top: baseline removal; bottom: resonance line model fits)<br /> Image:Mrsi parametric-map-NAA 3d.jpg|MRSI metabolite maps visualized in Slicer<br /> Image:Workflow_MRSI_signal_processing.png| ''Results'': Workflow for the MRSI signal processing. Each processing step is to be realized as an individual external module.<br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Polina Golland<br /> <br /> <br /> &lt;div style=&quot;margin: 20px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to find directions for either replacing these third-party libraries -- e.g. by using standard optimization routines from Python being available in Slicer -- or for making them easily available to the user of the module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> We focused on the second option and investigated directions for using third party libraries from within Slicer.<br /> <br /> * We adapted the current MRSI processing routine to be separable into several sub-routines, each of which to be usable as external CLI module and depending on few (or single) external libraries only. <br /> <br /> * We worked towards integrating an exemplary subroutine -- together with its external library -- as an external CLI module, potentially to be distributed as a Slicer Extension.<br /> <br /> Separate subroutines can be integrated using a Slicer Wizard in accordance with the signal processing workflow (see ''Results'' figure above).<br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_WinterProject_Week_MRSIModule&diff=47563 2010-01-08T16:11:31Z

<p>Bmenze: /* Key Investigators */</p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]<br /> Image:Mrsi parametric-map-NAA 3d.jpg|MRSI metabolite maps visualized in Slicer<br /> Image:Spectral fitting mrs.png|Fitting metabolite models to the MRS signal of tumorous brain tissue (top: baseline removal; bottom: resonance line model fits)<br /> Image:Workflow_MRSI_signal_processing.png| ''Results'': Workflow for the MRSI signal processing. Each processing step is to be realized as an individual external module.<br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Polina Golland<br /> <br /> <br /> &lt;div style=&quot;margin: 20px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to find directions for either replacing these third-party libraries -- e.g. by using standard optimization routines from Python being available in Slicer -- or for making them easily available to the user of the module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> We focused on the second option and investigated directions for using third party libraries from within Slicer.<br /> <br /> * We adapted the current MRSI processing routine to be separable into several sub-routines, each of which to be usable as external CLI module and depending on few (or single) external libraries only. <br /> <br /> * We worked towards integrating an exemplary subroutine -- together with its external library -- as an external CLI module, potentially to be distributed as a Slicer Extension.<br /> <br /> Separate subroutines can be integrated using a Slicer Wizard in accordance with the signal processing workflow (see ''Results'' figure above).<br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_WinterProject_Week_MRSIModule&diff=47553 2010-01-08T16:00:27Z

<p>Bmenze: </p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]<br /> Image:Mrsi parametric-map-NAA 3d.jpg|MRSI metabolite maps visualized in Slicer<br /> Image:Spectral fitting mrs.png|Fitting metabolite models to the MRS signal of tumorous brain tissue (top: baseline removal; bottom: resonance line model fits)<br /> Image:Workflow_MRSI_signal_processing.png| ''Results'': Workflow for the MRSI signal processing. Each processing step is to be realized as an individual external module.<br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Polina Golland<br /> <br /> <br /> &lt;div style=&quot;margin: 20px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to find directions for either replacing these third-party libraries -- e.g. by using standard optimization routines from Python being available in Slicer -- or for making them easily available to the user of the module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> We focused on the second option and investigated directions for using third party libraries from within Slicer.<br /> <br /> * We adapted the current MRSI processing routine to be separable into several sub-routines, each of which to be usable as external CLI module and depending on few (or single) external libraries only. <br /> <br /> * We worked towards integrating an exemplary subroutine -- together with its external library -- as an external CLI module, and potentially to be distributed as a Slicer Extension.<br /> <br /> Separate subroutines can be integrated using a Slicer Wizard in accordance with the signal processing workflow (see ''Results'' figure above).<br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_WinterProject_Week_MRSIModule&diff=47550 2010-01-08T15:58:13Z

<p>Bmenze: </p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]<br /> Image:Mrsi parametric-map-NAA 3d.jpg|MRSI metabolite maps visualized in Slicer<br /> Image:Spectral fitting mrs.png|Fitting metabolite models to the MRS signal of tumorous brain tissue (top: baseline removal; bottom: resonance line model fits)<br /> Image:Workflow_MRSI_signal_processing.png| Workflow for the MRSI signal processing. Each processing step is to be realized as an individual external module.<br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Polina Golland<br /> <br /> <br /> &lt;div style=&quot;margin: 20px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left; padding-right: 2%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to find directions for either replacing these third-party libraries -- e.g. by using standard optimization routines from Python being available in Slicer -- or for making them easily available to the user of the module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 35%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> We focused on the second option and investigated directions for using third party libraries from within Slicer.<br /> <br /> * We adapted the current MRSI processing routine to be separable into several sub-routines, each of which to be usable as external CLI module and depending on few (or single) external libraries only. <br /> <br /> * We worked towards integrating an exemplary subroutine -- together with its external library -- as an external CLI module, and potentially to be distributed as a Slicer Extension.<br /> <br /> Separate subroutines can be integrated using a Slicer Wizard in accordance with the signal processing workflow (see Figure above).<br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=2010_WinterProject_Week_MRSIModule&diff=47547 2010-01-08T15:53:17Z

<p>Bmenze: </p> <hr /> <div>__NOTOC__<br /> &lt;gallery&gt;<br /> Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]<br /> Image:Mrsi parametric-map-NAA 3d.jpg|MRSI metabolite maps visualized in Slicer<br /> Image:Spectral fitting mrs.png|Fitting metabolite models to the MRS signal of tumorous brain tissue (top: baseline removal; bottom: resonance line model fits)<br /> &lt;/gallery&gt;<br /> <br /> ==Key Investigators==<br /> * MIT: Bjoern Menze, Polina Golland<br /> <br /> <br /> &lt;div style=&quot;margin: 20px;&quot;&gt;<br /> <br /> &lt;div style=&quot;width: 32%; float: left; padding-right: 3%;&quot;&gt;<br /> &lt;h3&gt;Objective&lt;/h3&gt;<br /> Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases -- such as tumors in brain, breast and prostate -- can be can be associated with characteristic changes in the metabolic level. <br /> <br /> The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 32%; float: left; padding-right: 3%;&quot;&gt;<br /> &lt;h3&gt;Approach, Plan&lt;/h3&gt;<br /> Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models. Current fitting routines require, however, several external software libraries not to be distributed, installed and used easily.<br /> <br /> We want to find directions for either replacing these third-party libraries -- e.g. by using standard optimization routines from Python being available in Slicer -- or for making them easily available to the user of the module.<br /> &lt;/div&gt;<br /> <br /> &lt;div style=&quot;width: 30%; float: left;&quot;&gt;<br /> &lt;h3&gt;Progress&lt;/h3&gt;<br /> We focused on the second option and investigated directions for using third party libraries from within Slicer.<br /> <br /> * We adapted the current MRSI processing routine to be separable into several sub-routines, each of which to be usable as external CLI module and depending on few (or single) external libraries only. <br /> <br /> * We worked towards integrating an exemplary subroutine -- together with its external library -- as an external CLI module, and potentially to be distributed as a Slicer Extension.<br /> <br /> Separate subroutines can be integrated using a Slicer Wizard in accordance with the signal processing workflow (see Figure above).<br /> <br /> &lt;/div&gt;</div>

Bmenze https://www.na-mic.org/w/index.php?title=File:Workflow_MRSI_signal_processing.png&diff=47539 2010-01-08T15:43:34Z

<p>Bmenze: </p> <hr /> <div></div>

Bmenze