Projects:TumorModeling - Revision history

Polina: /* Processing magnetic resonance spectroscopic images */

2011-04-02T04:16:12Z

Processing magnetic resonance spectroscopic images

2011-04-02T04:15:56Z

Tumor segmentation in large multimodal data sets

2011-04-02T04:15:38Z

Image-based modeling of tumor growth

2011-04-02T04:15:08Z

Image-based modeling of tumor growth

2011-04-02T04:14:35Z

Modeling tumor growth in patients with glioma

2011-04-02T04:13:58Z

Tumor segmentation in large multimodal data sets

2011-04-02T04:13:41Z

Modeling tumor growth in patients with glioma

2011-04-02T04:13:20Z

Modeling tumor growth in patients with glioma

2011-04-02T04:13:03Z

Tumor segmentation in large multimodal data sets

2011-04-02T04:12:38Z

Tumor segmentation in large multimodal data sets

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-[[Image:Sivic_slicer_slc_mets.jpg|thumb|left|300px| Figure 6: MRSI metabolite maps generated in Slicer-SIVIC module referenced to anatomical (FLAIR) image.]]
+[[Image:Sivic_slicer_slc_mets.jpg|thumb|center|300px| Figure 6: MRSI metabolite maps generated in Slicer-SIVIC module referenced to anatomical (FLAIR) image.]]
 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.
 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.
 = Key Investigators =

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 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.
-[[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.]]
+[[Image:Tumor_segmentation_lesion_atlas.png|thumb|center|300px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]
 = Image-based modeling of tumor growth =

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 |[[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.]]
 | [[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.)]]
 |[[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.) ]]
 |}

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 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.
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 {|
 | [[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.)]]
 |[[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.) ]]
 |}

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 = Modeling tumor growth in patients with glioma =
-[[Image:Multimodal_glioma.png|thumb|left|250px| 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.]]
 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.
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 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.
 = Tumor segmentation in large multimodal data sets =

← Older revision		Revision as of 04:12, 2 April 2011
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−	~~[[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.]]~~

−	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.

		+	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.

		+	[[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.]]

	= Image-based modeling of tumor growth =		= Image-based modeling of tumor growth =