NAMIC Wiki - User contributions [en]

Projects:DTI DWI QualityControl

2013-01-02T18:53:03Z

MahshidLightMoon:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

[[Image:DTIPRep GUI.png|400px|thumb|right| DWI-based QC results using DTIPrep through three steps: 1) converting dicom to nrrd format of DWI image, 2) loading the protocol and running the software and 3) potential needed of visual checking and final saving. In this example, gradient #11 suffers from intensity artifact and is excluded. The sphere shows 3D view of gradients distribution before and after running DTIPrep in blue and green colors respectively. ]]

[[Image:GradientsDistribution.png|400px|thumb|right|3D view of gradients distribution with different b-values. ]]

As theoretical work characterizing DTI grows, it is essential to increase its practical usability from a clinical environment perspective. Inherently, DWI images suffer from a vast variety of artifacts and the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts and reduced signal-to-noise ratio (SNR). Therefore, in a clinical environment, this imaging technique needs additional processes such as appropriate QC assessment methods to increase its practical usability. We are developing a framework, called DTIPrep, for assessing and correcting DWIs and DTI.

= Description =

== Current framework for DWI QC ==
DTIPrep is the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) Dicom to NRRD converting, 2) image info checking, 3) diffusion information checking, 4) rician LMMSE noise filter, 5) slice-wise intensity checking, 6) interlace-wise intensity checking, 7) Averaging baseline images, 8) Eddy current and motion correction, 9) gradient-wise checking of residual motion/deformations, 10) joint rician LMMSE noise filter, 11) brain masking, 12) DTI computing, 13) dominant direction artifact (vibration artifact) checking, 14) optional visual checking and 15) simulation-based bias analysis.

== Entropy Based Diffusion Imaging Quality Control ==
We have proposed new QC step for detecting drop-out signal intensities which can be caused by mechanical vibration artifacts. This step detects and potentially removes these residual artifacts that are not commonly detected in the individual DWIs. The artifacts appear in color-FA images in either widespread or local dominant direction. In order to detect such artifacts, we proposed a new approach via the entropy of the Principal Direction (PD) histogram computed over the major region of the image (e.g. the full brain). Given a prior knowledge of expected entropy values for acceptable scans, the quality of the DTI image is categorized into acceptable, suspicious and highly suspicious/rejection categories using calculated the standard scores.

[[Image:Vibration artifact.png|800px|thumb|center| Vibration artifacts. a) Artifact free scan. Top-left, a representative axial slice of the color-FA map. Top-right, spherical histogram of the PD distribution within the entire brain. Bottom-right, tractography of genu and splenium of the corpus callosum. Bottom-left, genu tract in more detail. (b) Vibration artifacts may manifest as localized (prefrontal region for this example) signal-loss in the DWI image or as dominant L-R (red) direction. (c) Vibration artifact in the absence of localize DWI signal loss. Spherical viewpoints chosen to show locations of highest histogram frequency.]]

For our correction step, we employed a simple, iterative leave-one-out-strategy over all individual DWI images by recomputing DTI images and correspond- ing entropies. At each iteration, the DWI with maximal improvement is removed and all leave-one-out entropies are recomputed. This process is continued until either the z-score is in acceptable range or a maximum threshold for exclusion is reached.

[[Image:Correction.png|700px|thumb|center| The correction results show visible improvement in contrast within the cingulum and fornix tracts (left) and fiber tractography of splenium (right). ]]

== Ongoing Research about Simulation-based Bias Analysis==

It is important to note that there are some pitfalls associated with these QC approaches in the result of QC after correcting these artifacts. The correction processes modify a configuration of gradient sampling from a scan either by changing the gradients directions or excluding individual DWI’s along a subset of the gradients due to artifacts. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we propose a simulation-based automatic DTI QC to assess the resulting tensor properties from DWI-QC techniques. We define two error metrics based on the directional distribution of bias for FA and PD. These metrics can provide a promising benchmark for post-QC assessment of the remaining DWIs. Based on our simulation results, we introduce rejection metrics (automatic thresholds) with respect to magnitude and directional distribution of bias for FA and PD.

In experimental results, we applied our method on acquisition schemes and also individual scans post-QC. These results show that the proposed rejection metrics provide an effective assessment of post-QC individual scan and also acquisition protocols. Furthermore, our results confirm that higher degrees of uniformity in the sampling gradients results in lower overall bias. Thus, determination of diffusion properties with minimal error requires an evenly distributed gradient directions before and after QC. This method will be incorporated in DTIPrep, used for QC of DWI/DTI data.

[[Image:Mc schemes.png|500px|thumb|center| Bias analysis of different DWI schemes via MC simulation. (a-c) Gradient direction distribution for 3 acquisition schemes: 42-direction quasi-uniform, Phillips 32-direction non-uniform, and 6-direction uniform. (d- f) Estimated error distribution in PD computation. (g-i) Estimated error distribution in FA computation as a percentage of true FA. Number of iteration 200,000, true FA value of 0.4 and SNR = 10. ]]

[[Image:Mc scans.png|600px|thumb|center| Scan-specific bias analysis via MC simulation. (a-b) The gradient direction distribution after excluding 20% of the total number of gradients due to artifacts, in two configurations: non-clustered and clustered exclusion (blue: excluded gradients, green: included gradients). (c) Estimated error distribution of PD computation given the gradient sampling schemes in (a) and (b). (d) Estimated error distribution in FA computation, shown as percentage of true FA. MC simulation was performed for 200,000 iterations for this experiment, with a true FA value of 0.4 and SNR = 10.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2013-01-02T18:20:25Z

MahshidLightMoon:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

[[Image:DTIPRep GUI.png|400px|thumb|right| DWI-based QC results using DTIPrep through three steps: 1) converting dicom to nrrd format of DWI image, 2) loading the protocol and running the software and 3) potential needed of visual checking and final saving. In this example, gradient #11 suffers from intensity artifact and is excluded. The sphere shows 3D view of gradients distribution before and after running DTIPrep in blue and green colors respectively. ]]

[[Image:GradientsDistribution.png|400px|thumb|right|3D view of gradients distribution with different b-values. ]]

As theoretical work characterizing DTI grows, it is essential to increase its practical usability from a clinical environment perspective. Inherently, DWI images suffer from a vast variety of artifacts and the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts and reduced signal-to-noise ratio (SNR). Therefore, in a clinical environment, this imaging technique needs additional processes such as appropriate QC assessment methods to increase its practical usability. We are developing a framework, called DTIPrep, for assessing and correcting DWIs and DTI.

= Description =

== Current framework for DWI QC ==
DTIPrep is the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) Dicom to NRRD converting, 2) image info checking, 3) diffusion information checking, 4) rician LMMSE noise filter, 5) slice-wise intensity checking, 6) interlace-wise intensity checking, 7) Averaging baseline images, 8) Eddy current and motion correction, 9) gradient-wise checking of residual motion/deformations, 10) joint rician LMMSE noise filter, 11) brain masking, 12) DTI computing, 13) dominant direction artifact (vibration artifact) checking, 14) optional visual checking and 15) simulation-based bias analysis.

== Entropy Based Diffusion Imaging Quality Control ==
We have proposed new QC step for detecting drop-out signal intensities which can be caused by mechanical vibration artifacts. This step detects and potentially removes these residual artifacts that are not commonly detected in the individual DWIs. The artifacts appear in color-FA images in either widespread or local dominant direction ( see Fig?? ). In order to detect such artifacts, we proposed a new approach via the entropy of the Principal Direction (PD) histogram computed over the major region of the image (e.g. the full brain). Given a prior knowledge of expected entropy values for acceptable scans, the quality of the DTI image is categorized into acceptable, suspicious and highly suspicious/rejection categories using calculated the standard scores.

[[Image:Vibration artifact.png|800px|thumb|center| Vibration artifacts. a) Artifact free scan. Top-left, a representative axial slice of the color-FA map. Top-right, spherical histogram of the PD distribution within the entire brain. Bottom-right, tractography of genu and splenium of the corpus callosum. Bottom-left, genu tract in more detail. (b) Vibration artifacts may manifest as localized (prefrontal region for this example) signal-loss in the DWI image or as dominant L-R (red) direction. (c) Vibration artifact in the absence of localize DWI signal loss. Spherical viewpoints chosen to show locations of highest histogram frequency.]]

For our correction step, we employed a simple, iterative leave-one-out-strategy over all individual DWI images by recomputing DTI images and correspond- ing entropies. At each iteration, the DWI with maximal improvement is removed and all leave-one-out entropies are recomputed. This process is continued until either the z-score is in acceptable range or a maximum threshold for exclusion is reached.

[[Image:Correction.png|700px|thumb|center| The correction results show visible improvement in contrast within the cingulum and fornix tracts (left) and fiber tractography of splenium (right). ]]

== Ongoing Research about Simulation-based Bias Analysis==

It is important to note that there are some pitfalls associated with these QC approaches in the result of QC after correcting these artifacts. The correction processes modify a configuration of gradient sampling from a scan either by changing the gradients directions or excluding individual DWI’s along a subset of the gradients due to artifacts. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we propose a simulation-based automatic DTI QC to assess the resulting tensor properties from DWI-QC techniques. We define two error metrics based on the directional distribution of bias for FA and PD. These metrics can provide a promising benchmark for post-QC assessment of the remaining DWIs. Based on our simulation results, we introduce rejection metrics (automatic thresholds) with respect to magnitude and directional distribution of bias for FA and PD.

In experimental results, we applied our method on acquisition schemes and also individual scans post-QC. These results show that the proposed rejection metrics provide an effective assessment of post-QC individual scan and also acquisition protocols. Furthermore, our results confirm that higher degrees of uniformity in the sampling gradients results in lower overall bias. Thus, determination of diffusion properties with minimal error requires an evenly distributed gradient directions before and after QC. This method will be incorporated in DTIPrep, used for QC of DWI/DTI data.

[[Image:Mc schemes.png|500px|thumb|center| Bias analysis of different DWI schemes via MC simulation. (a-c) Gradient direction distribution for 3 acquisition schemes: 42-direction quasi-uniform, Phillips 32-direction non-uniform, and 6-direction uniform. (d- f) Estimated error distribution in PD computation. (g-i) Estimated error distribution in FA computation as a percentage of true FA. Number of iteration 200,000, true FA value of 0.4 and SNR = 10. ]]

[[Image:Mc scans.png|600px|thumb|center| Scan-specific bias analysis via MC simulation. (a-b) The gradient direction distribution after excluding 20% of the total number of gradients due to artifacts, in two configurations: non-clustered and clustered exclusion (blue: excluded gradients, green: included gradients). (c) Estimated error distribution of PD computation given the gradient sampling schemes in (a) and (b). (d) Estimated error distribution in FA computation, shown as percentage of true FA. MC simulation was performed for 200,000 iterations for this experiment, with a true FA value of 0.4 and SNR = 10.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2013-01-02T18:19:55Z

MahshidLightMoon:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

[[Image:DTIPRep GUI.png|400px|thumb|right| DWI-based QC results using DTIPrep through three steps: 1) converting dicom to nrrd format of DWI image, 2) loading the protocol and running the software and 3) potential needed of visual checking and final saving. In this example, gradient #11 suffers from intensity artifact and is excluded. The sphere shows 3D view of gradients distribution before and after running DTIPrep in blue and green colors respectively. ]]

[[Image:GradientsDistribution.png|400px|thumb|right|3D view of gradients distribution with different b-values. ]]

As theoretical work characterizing DTI grows, it is essential to increase its practical usability from a clinical environment perspective. Inherently, DWI images suffer from a vast variety of artifacts and the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts and reduced signal-to-noise ratio (SNR). Therefore, in a clinical environment, this imaging technique needs additional processes such as appropriate QC assessment methods to increase its practical usability. We are developing a framework, called DTIPrep, for assessing and correcting DWIs and DTI.

= Description =

== Current framework for DWI QC ==
DTIPrep is the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) Dicom to NRRD converting, 2) image info checking, 3) diffusion information checking, 4) rician LMMSE noise filter, 5) slice-wise intensity checking, 6) interlace-wise intensity checking, 7) Averaging baseline images, 8) Eddy current and motion correction, 9) gradient-wise checking of residual motion/deformations, 10) joint rician LMMSE noise filter, 11) brain masking, 12) DTI computing, 13) dominant direction artifact (vibration artifact) checking, 14) optional visual checking and 15) simulation-based bias analysis.

== Entropy Based Diffusion Imaging Quality Control ==
We have proposed new QC step for detecting drop-out signal intensities which can be caused by mechanical vibration artifacts. This step detects and potentially removes these residual artifacts that are not commonly detected in the individual DWIs. The artifacts appear in color-FA images in either widespread or local dominant direction ( see Fig?? ). In order to detect such artifacts, we proposed a new approach via the entropy of the Principal Direction (PD) histogram computed over the major region of the image (e.g. the full brain). Given a prior knowledge of expected entropy values for acceptable scans, the quality of the DTI image is categorized into acceptable, suspicious and highly suspicious/rejection categories using calculated the standard scores.

[[Image:Vibration artifact.png|800px|thumb|center| Vibration artifacts. a) Artifact free scan. Top-left, a representative axial slice of the color-FA map. Top-right, spherical histogram of the PD distribution within the entire brain. Bottom-right, tractography of genu and splenium of the corpus callosum. Bottom-left, genu tract in more detail. (b) Vibration artifacts may manifest as localized (prefrontal region for this example) signal-loss in the DWI image or as dominant L-R (red) direction. (c) Vibration artifact in the absence of localize DWI signal loss. Spherical viewpoints chosen to show locations of highest histogram frequency.]]

For our correction step, we employed a simple, iterative leave-one-out-strategy over all individual DWI images by recomputing DTI images and correspond- ing entropies. At each iteration, the DWI with maximal improvement is removed and all leave-one-out entropies are recomputed. This process is continued until either the z-score is in acceptable range or a maximum threshold for exclusion is reached.

[[Image:Correction.png|700px|thumb|center| The correction results show visible improvement in contrast within the cingulum and fornix tracts (left) and fiber tractography of splenium (right). ]]

== Ongoing Research about Simulation-based Bias Analysis==

It is important to note that there are some pitfalls associated with these QC approaches in the result of QC after correcting these artifacts. The correction processes modify a configuration of gradient sampling from a scan either by changing the gradients directions or excluding individual DWI’s along a subset of the gradients due to artifacts. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we propose a simulation-based automatic DTI QC to assess the resulting tensor properties from DWI-QC techniques. We define two error metrics based on the directional distribution of bias for FA and PD. These metrics can provide a promising benchmark for post-QC assessment of the remaining DWIs. Based on our simulation results, we introduce rejection metrics (automatic thresholds) with respect to magnitude and directional distribution of bias for FA and PD.

In experimental results, we applied our method on acquisition schemes and also individual scans post-QC. These results show that the proposed rejection metrics provide an effective assessment of post-QC individual scan and also acquisition protocols. Furthermore, our results confirm that higher degrees of uniformity in the sampling gradients results in lower overall bias. Thus, determination of diffusion properties with minimal error requires an evenly distributed gradient directions before and after QC. This method will be incorporated in DTIPrep, used for QC of DWI/DTI data.

[[Image:Mc schemes.png|700px|thumb|center| Bias analysis of different DWI schemes via MC simulation. (a-c) Gradient direction distribution for 3 acquisition schemes: 42-direction quasi-uniform, Phillips 32-direction non-uniform, and 6-direction uniform. (d- f) Estimated error distribution in PD computation. (g-i) Estimated error distribution in FA computation as a percentage of true FA. Number of iteration 200,000, true FA value of 0.4 and SNR = 10. ]]

[[Image:Mc scans.png|700px|thumb|center| Scan-specific bias analysis via MC simulation. (a-b) The gradient direction distribution after excluding 20% of the total number of gradients due to artifacts, in two configurations: non-clustered and clustered exclusion (blue: excluded gradients, green: included gradients). (c) Estimated error distribution of PD computation given the gradient sampling schemes in (a) and (b). (d) Estimated error distribution in FA computation, shown as percentage of true FA. MC simulation was performed for 200,000 iterations for this experiment, with a true FA value of 0.4 and SNR = 10.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2013-01-02T18:18:50Z

MahshidLightMoon:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

[[Image:DTIPRep GUI.png|400px|thumb|right| DWI-based QC results using DTIPrep through three steps: 1) converting dicom to nrrd format of DWI image, 2) loading the protocol and running the software and 3) potential needed of visual checking and final saving. In this example, gradient #11 suffers from intensity artifact and is excluded. The sphere shows 3D view of gradients distribution before and after running DTIPrep in blue and green colors respectively. ]]

[[Image:GradientsDistribution.png|400px|thumb|right|3D view of gradients distribution with different b-values. ]]

As theoretical work characterizing DTI grows, it is essential to increase its practical usability from a clinical environment perspective. Inherently, DWI images suffer from a vast variety of artifacts and the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts and reduced signal-to-noise ratio (SNR). Therefore, in a clinical environment, this imaging technique needs additional processes such as appropriate QC assessment methods to increase its practical usability. We are developing a framework, called DTIPrep, for assessing and correcting DWIs and DTI.

= Description =

== Current framework for DWI QC ==
DTIPrep is the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) Dicom to NRRD converting, 2) image info checking, 3) diffusion information checking, 4) rician LMMSE noise filter, 5) slice-wise intensity checking, 6) interlace-wise intensity checking, 7) Averaging baseline images, 8) Eddy current and motion correction, 9) gradient-wise checking of residual motion/deformations, 10) joint rician LMMSE noise filter, 11) brain masking, 12) DTI computing, 13) dominant direction artifact (vibration artifact) checking, 14) optional visual checking and 15) simulation-based bias analysis.

== Entropy Based Diffusion Imaging Quality Control ==
We have proposed new QC step for detecting drop-out signal intensities which can be caused by mechanical vibration artifacts. This step detects and potentially removes these residual artifacts that are not commonly detected in the individual DWIs. The artifacts appear in color-FA images in either widespread or local dominant direction ( see Fig?? ). In order to detect such artifacts, we proposed a new approach via the entropy of the Principal Direction (PD) histogram computed over the major region of the image (e.g. the full brain). Given a prior knowledge of expected entropy values for acceptable scans, the quality of the DTI image is categorized into acceptable, suspicious and highly suspicious/rejection categories using calculated the standard scores.

[[Image:Vibration artifact.png|800px|thumb|center| Vibration artifacts. a) Artifact free scan. Top-left, a representative axial slice of the color-FA map. Top-right, spherical histogram of the PD distribution within the entire brain. Bottom-right, tractography of genu and splenium of the corpus callosum. Bottom-left, genu tract in more detail. (b) Vibration artifacts may manifest as localized (prefrontal region for this example) signal-loss in the DWI image or as dominant L-R (red) direction. (c) Vibration artifact in the absence of localize DWI signal loss. Spherical viewpoints chosen to show locations of highest histogram frequency.]]

For our correction step, we employed a simple, iterative leave-one-out-strategy over all individual DWI images by recomputing DTI images and correspond- ing entropies. At each iteration, the DWI with maximal improvement is removed and all leave-one-out entropies are recomputed. This process is continued until either the z-score is in acceptable range or a maximum threshold for exclusion is reached.

[[Image:Correction.png|700px|thumb|center| The correction results show visible improvement in contrast within the cingulum and fornix tracts (left) and fiber tractography of splenium (right). ]]

== Ongoing Research about Simulation-based Bias Analysis==

It is important to note that there are some pitfalls associated with these QC approaches in the result of QC after correcting these artifacts. The correction processes modify a configuration of gradient sampling from a scan either by changing the gradients directions or excluding individual DWI’s along a subset of the gradients due to artifacts. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we propose a simulation-based automatic DTI QC to assess the resulting tensor properties from DWI-QC techniques. We define two error metrics based on the directional distribution of bias for FA and PD. These metrics can provide a promising benchmark for post-QC assessment of the remaining DWIs. Based on our simulation results, we introduce rejection metrics (automatic thresholds) with respect to magnitude and directional distribution of bias for FA and PD.

In experimental results, we applied our method on acquisition schemes and also individual scans post-QC. These results show that the proposed rejection metrics provide an effective assessment of post-QC individual scan and also acquisition protocols. Furthermore, our results confirm that higher degrees of uniformity in the sampling gradients results in lower overall bias. Thus, determination of diffusion properties with minimal error requires an evenly distributed gradient directions before and after QC. This method will be incorporated in DTIPrep, used for QC of DWI/DTI data.

[[Image:Mc schemes.png|700px|thumb|center| ]]

[[Image:Mc scans.png|700px|thumb|center| ]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

File:Mc schemes.png

2013-01-02T18:17:44Z

MahshidLightMoon:

File:Mc scans.png

2013-01-02T18:17:28Z

MahshidLightMoon:

Projects:DTI DWI QualityControl

2013-01-02T18:07:43Z

MahshidLightMoon:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

[[Image:DTIPRep GUI.png|400px|thumb|right| DWI-based QC results using DTIPrep through three steps: 1) converting dicom to nrrd format of DWI image, 2) loading the protocol and running the software and 3) potential needed of visual checking and final saving. In this example, gradient #11 suffers from intensity artifact and is excluded. The sphere shows 3D view of gradients distribution before and after running DTIPrep in blue and green colors respectively. ]]

[[Image:GradientsDistribution.png|400px|thumb|right|3D view of gradients distribution with different b-values. ]]

As theoretical work characterizing DTI grows, it is essential to increase its practical usability from a clinical environment perspective. Inherently, DWI images suffer from a vast variety of artifacts and the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts and reduced signal-to-noise ratio (SNR). Therefore, in a clinical environment, this imaging technique needs additional processes such as appropriate QC assessment methods to increase its practical usability. We are developing a framework, called DTIPrep, for assessing and correcting DWIs and DTI.

= Description =

== Current framework for DWI QC ==
DTIPrep is the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) Dicom to NRRD converting, 2) image info checking, 3) diffusion information checking, 4) rician LMMSE noise filter, 5) slice-wise intensity checking, 6) interlace-wise intensity checking, 7) Averaging baseline images, 8) Eddy current and motion correction, 9) gradient-wise checking of residual motion/deformations, 10) joint rician LMMSE noise filter, 11) brain masking, 12) DTI computing, 13) dominant direction artifact (vibration artifact) checking, 14) optional visual checking and 15) simulation-based bias analysis.

== Entropy Based Diffusion Imaging Quality Control ==
We have proposed new QC step for detecting drop-out signal intensities which can be caused by mechanical vibration artifacts. This step detects and potentially removes these residual artifacts that are not commonly detected in the individual DWIs. The artifacts appear in color-FA images in either widespread or local dominant direction ( see Fig?? ). In order to detect such artifacts, we proposed a new approach via the entropy of the Principal Direction (PD) histogram computed over the major region of the image (e.g. the full brain). Given a prior knowledge of expected entropy values for acceptable scans, the quality of the DTI image is categorized into acceptable, suspicious and highly suspicious/rejection categories using calculated the standard scores.

[[Image:Vibration artifact.png|800px|thumb|center| Vibration artifacts. a) Artifact free scan. Top-left, a representative axial slice of the color-FA map. Top-right, spherical histogram of the PD distribution within the entire brain. Bottom-right, tractography of genu and splenium of the corpus callosum. Bottom-left, genu tract in more detail. (b) Vibration artifacts may manifest as localized (prefrontal region for this example) signal-loss in the DWI image or as dominant L-R (red) direction. (c) Vibration artifact in the absence of localize DWI signal loss. Spherical viewpoints chosen to show locations of highest histogram frequency.]]

For our correction step, we employed a simple, iterative leave-one-out-strategy over all individual DWI images by recomputing DTI images and correspond- ing entropies. At each iteration, the DWI with maximal improvement is removed and all leave-one-out entropies are recomputed. This process is continued until either the z-score is in acceptable range or a maximum threshold for exclusion is reached.

[[Image:Correction.png|700px|thumb|center| The correction results show visible improvement in contrast within the cingulum and fornix tracts (left) and fiber tractography of splenium (right). ]]

== Ongoing Research about Simulation-based Bias Analysis==

It is important to note that there are some pitfalls associated with these QC approaches in the result of QC after correcting these artifacts. The correction processes modify a configuration of gradient sampling from a scan either by changing the gradients directions or excluding individual DWI’s along a subset of the gradients due to artifacts. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we propose a simulation-based automatic DTI QC to assess the resulting tensor properties from DWI-QC techniques. We define two error metrics based on the directional distribution of bias for FA and PD. These metrics can provide a promising benchmark for post-QC assessment of the remaining DWIs.

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2013-01-02T17:42:16Z

MahshidLightMoon:

Projects:DTI DWI QualityControl

2013-01-02T17:41:06Z

MahshidLightMoon:

Projects:DTI DWI QualityControl

2013-01-02T17:40:44Z

MahshidLightMoon:

File:Correction.png

2013-01-02T17:38:49Z

MahshidLightMoon:

Projects:DTI DWI QualityControl

2013-01-02T17:32:31Z

MahshidLightMoon:

File:Vibration artifact.png

2013-01-02T17:30:47Z

MahshidLightMoon:

Projects:DTI DWI QualityControl

2013-01-02T17:26:26Z

MahshidLightMoon:

Projects:DTI DWI QualityControl

2013-01-02T16:55:33Z

MahshidLightMoon:

Projects:DTI DWI QualityControl

2013-01-02T16:45:54Z

MahshidLightMoon:

File:GradientsDistribution.png

2013-01-02T16:44:02Z

MahshidLightMoon:

Projects:DTI DWI QualityControl

2013-01-02T16:35:25Z

MahshidLightMoon:

Projects:DTI DWI QualityControl

2013-01-02T16:33:43Z

MahshidLightMoon:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

[[Image:Screenshot.png|400px|thumb|right| DWI-based QC results using DTIPrep through three steps: 1) converting dicom to nrrd format of DWI image, 2) loading the protocol and running the software and 3) potential needed of visual checking and final saving. In this example, gradient #11 suffers from intensity artifact and is excluded. The sphere shows 3D view of gradients distribution before and after running DTIPrep in blue and green colors respectively. ]]

[[Image:753361684_3.png|400px|thumb|right|3D view of gradients before and after Quality Control procedures]]

As theoretical work characterizing DTI grows, it is essential to increase its practical usability from a clinical environment perspective. Inherently, DWI images suffer from a vast variety of artifacts and the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts and reduced signal-to-noise ratio (SNR). Therefore, in a clinical environment, this imaging technique needs additional processes such as appropriate QC assessment methods to increase its practical usability. We are developing a framework, called DTIPrep, for assessing and correcting DWIs and DTI.

= Description =

== Current framework for DWI QC ==
DTIPrep is the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baseline images, 6) Eddy current and motion correction, 7) gradient-wise checking of residual motion/deformations, 8) Computing DTI measurements and saving and 9) Optional visual QC.

== Ongoing extension using DTI QC ==

In our ongoing research, we are extending our QC procedures to include a QC step based on the derived DTI data. This step detects and potentially removes residual artifacts that are not commonly detected in the individual DWIs. In our large scale population studies, we observed several such artifacts, most specifically an artifact of "dominating direction" (see figure to the right). In order to detect such artifacts, we propose a new approach via the entropy of the Principal Direction (PD) histogram computed over the major region of the image (e.g. the full brain). Given a prior knowledge of expected entropy values for acceptable scans, the quality of the DTI image is categorized into acceptable, suspicious and highly suspicious/rejection categories using calculated the standard scores.

[[Image:Diagram2_copy.png|400px|thumb|right| Visualization of dominating direction artifact using the color FA DTI image (left column) and the corresponding spherical histogram of the principal directions within the brain. Top: Example of an acceptable DTI dataset. Middle: Example of a "green" direction (anterior-posterior) dominating artifact. Bottom: "Red" direction (left-right) artifact.]]

This approach can also be employed to correct data: Using a leave-one-out scheme, we iteratively determine the best candidate DWI image for exclusion as the one with optimal improvement in the corresponding DTI's entropy measure. This removal of DWI's continues until either DTI image is not longer classified in the rejection class.

[[Image:Correction_Figure_copy.png|400px|thumb|right| The correction result (top right image) shows visible improvement in contrast within the cingulum and fornix tracts. The FA profiles of the genu and splenium tracts (bottom, red: uncorrected, blue: corrected image) show higher FA profiles for the corrected image.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

File:DTIPRep GUI.png

2013-01-02T16:31:50Z

MahshidLightMoon:

Projects:DTI DWI QualityControl

2013-01-02T16:11:42Z

MahshidLightMoon:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

[[Image:Screenshot.png|400px|thumb|right| Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|3D view of gradients before and after Quality Control procedures]]

As theoretical work characterizing DTI grows, it is essential to increase its practical usability from a clinical environment perspective. Inherently, DWI images suffer from a vast variety of artifacts and the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts and reduced signal-to-noise ratio (SNR). Therefore, in a clinical environment, this imaging technique needs additional processes such as appropriate QC assessment methods to increase its practical usability. We are developing a framework, called DTIPrep, for assessing and correcting DWIs and DTI.

= Description =

== Current framework for DWI QC ==
DTIPrep is the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baseline images, 6) Eddy current and motion correction, 7) gradient-wise checking of residual motion/deformations, 8) Computing DTI measurements and saving and 9) Optional visual QC.

== Ongoing extension using DTI QC ==

In our ongoing research, we are extending our QC procedures to include a QC step based on the derived DTI data. This step detects and potentially removes residual artifacts that are not commonly detected in the individual DWIs. In our large scale population studies, we observed several such artifacts, most specifically an artifact of "dominating direction" (see figure to the right). In order to detect such artifacts, we propose a new approach via the entropy of the Principal Direction (PD) histogram computed over the major region of the image (e.g. the full brain). Given a prior knowledge of expected entropy values for acceptable scans, the quality of the DTI image is categorized into acceptable, suspicious and highly suspicious/rejection categories using calculated the standard scores.

[[Image:Diagram2_copy.png|400px|thumb|right| Visualization of dominating direction artifact using the color FA DTI image (left column) and the corresponding spherical histogram of the principal directions within the brain. Top: Example of an acceptable DTI dataset. Middle: Example of a "green" direction (anterior-posterior) dominating artifact. Bottom: "Red" direction (left-right) artifact.]]

This approach can also be employed to correct data: Using a leave-one-out scheme, we iteratively determine the best candidate DWI image for exclusion as the one with optimal improvement in the corresponding DTI's entropy measure. This removal of DWI's continues until either DTI image is not longer classified in the rejection class.

[[Image:Correction_Figure_copy.png|400px|thumb|right| The correction result (top right image) shows visible improvement in contrast within the cingulum and fornix tracts. The FA profiles of the genu and splenium tracts (bottom, red: uncorrected, blue: corrected image) show higher FA profiles for the corrected image.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Algorithm:UNC

2012-01-04T16:19:15Z

MahshidLightMoon: /* DWI and DTI Quality Control */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of UNC Algorithms (PI: Martin Styner) =

At UNC, we are interested in a range of algorithms and solutions for the surface based analysis of brain structures and the cortex. We pioneered the use of spherical harmonics based shape analysis for comparing brain structures across objects. We has also worked on incorporating various data sources for correspondence computation on surfaces of different complexity (ranging from simple brain structures to the highly folded cortical surface). A current topic includes the use of diffusion tensor imaging for connectivity analysis in pathological settings. Finally, investigating quality control, validation and evaluation methodology is another important topic of our NA-MIC research.

= UNC Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" |[[Image:UNC_DTIAnalysisFramework.jpg|200px]]
| style="width:85%" |

== [[Projects:AtlasBasedDTIFiberAnalyzerFramework| Atlas Based DTI Fiber Analysis Framework]] ==
This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

Quality control is performed on diffusion weighted images and DTI images are computed for each individual subject. The data is then either mapped into a prior atlas or an unbiased diffeomorphic DTI atlas is generated from all datasets. This creates a normalized coordinate system for all diffusion images in a study. Fiber tracts of interest are generated on this atlas, and then mapped back to the individual subjects. Diffusion properties along fiber tracts, such as fractional anisotropy (FA), are modeled as multivariate functions of arc length and gathered in spreadsheets for statistical analysis.
[[Projects:AtlasBasedDTIFiberAnalyzerFramework|More...]]

<font color="red">'''New: '''</font> First versions on NITRC for [http://www.nitrc.org/projects/dti_tract_stat/ DTIAtlasFiberAnalyzer] and [http://www.nitrc.org/projects/fvlight/ FiberViewerLight ]

|-

| style="width:15%" |[[Image:UNC_longitudinalAtlasEx1.png|200px]]
| style="width:85%" |

== [[Projects:LongitudinalAtlasBuilding| Longitudinal Atlas Building]] ==
As part of the longitudinal intra- and interpatient analysis theme within NA-MIC, we are working on a deformable, longitudinal DTI atlas method. Our longitudinal framework explicitly accounts for temporal dependencies via iterative subject-specific statistical growth modeling, and cross-sectional atlas-building. To effectively account for measurements sparse in time, a continuous-discrete statistical growth model is proposed incorporating also patient co-variates[[Projects:LongitudinalAtlasBuilding|More...]]

<font color="red">'''New: '''</font> Gabe Hart, Yundi Shi , Hongtu Zhu, Mar Sanchez, Martin Styner, Marc Niethammer. DTI Longitudinal Atlas Construction as an Average of Growth Models. Workshop on Spatio-Temporal Image Analysis for Longitudinal and Time-Series Image Data, MICCAI 2010 Aug.;

|-

| style="width:15%" |[[Image:UNC_dwiatlas.png‎|200px]]
| style="width:85%" |

== [[Projects:DWIAtlas|Diffusion Weighted Atlas Construction via model-based transformation and averaging of signal]] ==
This project investigated a method for model-based averaging of sets of diffusion weighted magnetic resonance images (DW-MRI) under space transformations (resulting for example from registration methods). A robust weighted least squares method is developed. Synthetic validation experiments show the improvement of the proposed estimation method in comparison to standard least squares estimation. The developed method is applied to construct an atlas of {\it diffusion weighted images} for a set of macaques, allowing for a more flexible representation of average diffusion information compared to standard diffusion tensor atlases.
[[Projects:DWIAtlas|More...]]

<font color="red">'''New: '''</font> Y. Shi, S. Benzaid, M. Sanchez, M. Styner, M. Niethammer. Diffusion Weighted Atlas Construction via robust model-based transformation. NeuroImage, in preparation.

<font color="red">'''New: '''</font> M. Niethammer, Y. Shi, S. Benzaid, M. Sanchez, and M. Styner. Robust model-based transformation and averaging of diffusion weighted images applied to diffusion weighted atlas construction. MICCAI, Workshop on Computational Diffusion MRI, 2010.

|-

| style="width:15%" |[[Image:UNC_GraphbasedConnectivity_Ex1.png‎|200px]]
| style="width:85%" |

== [[Projects:DiffusionGraphBasedConnectivity|Diffusion Imaging based Connectivity]] ==

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.
[[Projects:DiffusionGraphBasedConnectivity|More...]]

<font color="red">'''New: '''</font> Alexis Boucharin, Ipek Oguz, Clement Vachet, Yundi Shi, Mar Sanchez, Martin Styner. Efficient, graph-based white matter connectivity from orientation distribution functions via multi-directional graph propagation. Medical Imaging 2011: Image Processing (2011) vol. 7962 (1) pp. 79620S

|-

| style="width:15%" |[[Image:DTIPrep_example1.png|200px]]
| style="width:85%" |

== [[Projects:DTI_DWI_QualityControl|DWI and DTI Quality Control]] ==

DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework for automatic DWI and DTI quality assessment and correction. We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation. [[Projects:DTI_DWI_QualityControl|More...]]

<font color="red">'''New: '''</font> Mahshid Farzinfar, Cheryl Dietrich, Rachel Smith, Yinpeng Li, Adyta Gupta, Zhexing Liu, Martin Styner. ENTROPY BASED DTI QUALITY CONTROL VIA REGIONAL ORIENTATION DISTRIBUTION. Submitted to ISBI 2012.

<font color="red">'''New: '''</font> DTIPrep first full version on [http://www.nitrc.org/projects/dtiprep/ NITRC ]

<font color="red">'''New: '''</font> Zhexing Liu, Casey Goodlett, Guido Gerig, Martin Styner. Evaluation of DTI property maps as basis of DTI atlas building. Medical Imaging 2010: Image Processing (2010) vol. 7623 (1) pp. 762325

<font color="red">'''New: '''</font> Zhexing Liu, Yi Wang, Guido Gerig, Sylvain Gouttard, Ran Tao, Thomas Fletcher, Martin Styner. Quality control of diffusion weighted images. Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications (2010) vol. 7628 (1) pp. 76280J

|-

| style="width:15%" |[[Image:Sulcaldepth.png|200px]]
| style="width:85%" |

== [[Projects:CorticalCorrespondenceWithParticleSystem|Cortical Correspondence using Particle System]] ==

In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. This correspondence method has been included in our NAMIC cortical thickness framework GAMBIT [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

<font color="red">'''New: '''</font> Vachet, C., Hazlett, H., Niethammer, M., Oguz, I., Cates, J., Whitaker, R., Piven, J., Styner, M., “Group-wise automatic mesh-based analysis of cortical thickness“. Medical Imaging 2011: Image Processing (2011) vol. 7962 (1) pp. 796227 1 - 10

<font color="red">'''New: '''</font> Lee J, Ehlers C, Crews F, Niethammer M, Budin F, Paniagua B, Sulik K, Johns J, Styner M, Oguz I. Automatic cortical thickness analysis on rodent brain. Medical Imaging 2011: Image Processing (2011) vol. 7962 (1) pp. 796248

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|UNC-Utah Shape Analysis Framework]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM-PDM) with correspondence and tested via statistical point-wise analysis. Additionally, the SPHARM correspondences can be improved with Entropy-based particle systems, by using an integration module recently added to the pipeline. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

* SPHARM-Particle Shape Analysis Toolkit disseminated on [http://www.nitrc.org/projects/spharm-pdm NITRC SPHARM PDM page]. All tools are Slicer compatible.
* Single Slicer 3 module for whole shape analysis pipeline with automatic generation of Slicer MRML scenes for result visualization

<font color="red">'''New: '''</font> Mark Walterfang, Jeffrey Chee Leong Looi, Martin Styner, Ruth H Walker, Adrian Danek, Marc Neithammer, Andrew Evans, Katya Kotschet, Guilherme R Rodrigues, Andrew Hughes, Dennis Velakoulis. Shape alterations in the striatum in chorea-acanthocytosis. Psychiatry research (2011) vol. 192 (1), pp. 29-36

<font color="red">'''New: '''</font> Beatriz Paniagua, Lucia Cevidanes, David Walker, Hongtu Zhu, Ruixin Guo, Martin Styner. Clinical application of SPHARM-PDM to quantify temporomandibular joint osteoarthritis. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society (2011) vol. 35(5), pp. 345-352

<font color="red">'''New: '''</font> Beatriz Paniagua, Lucia Cevidanes, Hongtu Zhu, Martin Styner. Outcome quantification using SPHARM-PDM toolbox in orthognathic surgery. International journal of computer assisted radiology and surgery (2011) vol. 6 (5) pp. 617-626

<font color="red">'''New: '''</font> Jeffrey Chee Leong Looi, Mark Walterfang, Martin Styner, Leif Svensson, Olof Lindberg, Per Ostberg, Lisa Botes, Eva Orndahl, Phyllis Chua, Rajeev Kumar, Dennis Velakoulis, Lars-Olof Wahlund. Shape analysis of the neostriatum in frontotemporal lobar degeneration, Alzheimer's disease, and controls. Neuroimage (2010) vol. 51 (3) pp. 970-86

<font color="red">'''New: '''</font> Maltbie E, Bhatt K, Paniagua B, Smith RG, Graves MM, Mosconi MW, Peterson S, White S, Blocher J, El-Sayed M, Hazlett HC, Styner M. Asymmetric bias in user guided segmentations of brain structures. NeuroImage 2011 Aug. [Epub ahead of print]

<font color="red">'''New: '''</font> Datar M, Gur Y, Paniagua B, Styner M, Whitaker R. Geometric Correspondence for Ensembles of. MICCAI 2011, Part II 2011 Aug.;6892:368–375.

<font color="red">'''New: '''</font> Looi JCL, Macfarlane MD, Walterfang M, Styner M, Velakoulis D, Lätt J, van Westen D, Nilsson C. Morphometric analysis of subcortical structures in progressive supranuclear palsy: In vivo evidence of neostriatal and mesencephalic atrophy. Psychiatry Research: Neuroimaging 2011 Sep.;:1–13

|-

| | [[Image:UNCShape_ShapeCorrespondence.png|200px]]
| |

== [[Projects:LocalStatisticalAnalysisViaPermutationTests|Local Statistical Analysis via Permutation Tests]] ==

We have further developed a set of statistical testing methods that allow the analysis of local shape differences via group differences tests as well interaction tests. Resulting significance maps (both raw and corrected for multiple comparisons) are easily visualized. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. Additional visualization of the interaction tests include Pearson and Spearman correlation maps. [[Projects:LocalStatisticalAnalysisViaPermutationTests|More...]]

<font color="red">'''New: '''</font> User-friendly GUI interface and statistical result visualization via automatically generated Slicer MRML scenes

<font color="red">'''New: '''</font> Available on NITRC either [http://www.nitrc.org/projects/shape_mancova separately (ShapeAnalysisMANCOVA)] or as part of the [http://www.nitrc.org/projects/spharm-pdm SPHARM-PDM shape analysis package]

* Paniagua B., Styner M., Macenko M., Pantazis D., Niethammer M, Local Shape Analysis using MANCOVA, Insight Journal, 2009 July-December, http://hdl.handle.net/10380/3124

|-
| | [[Image:Cause07Competition.gif|200px]]
| |

== [[Projects:MethodEvaluationValidation|Evaluation and Comparison of Medical Image Analysis Methods]] ==

In this project, we want to focus on the evaluation of medical image analysis methods for specific clinical applications in respect to development of evaluation methodology and the organization of venues promoting such comparison and validation studies.

<font color="red">'''New: '''</font> [[Events:_DTI_Tractography_Challenge_MICCAI_2011 | DTI fiber tractography challenge]] at MICCAI 2011

|-

| | [[Image:UNCShape_CaudatePval_MICCAI06.png|200px]]
| |

== [[Projects:PopulationBasedCorrespondence|Population Based Correspondence]] ==

We are developing methodology to automatically find dense point correspondences between a collection of polygonal genus 0 meshes. The advantage of this method is independence from indivisual templates, as well as enhanced modeling properties. The method is based on minimizing a cost function that describes the goodness of correspondence. Apart from a cost function derived from the description length of the model, we also employ a cost function working with arbitrary local features. We extended the original methods to use surface curvature measurements, which are independent to differences of object aligment. [[Projects:PopulationBasedCorrespondence|More...]]

* Styner M., Oguz I., Heimann T., Gerig G. Minimum description length with local geometry. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 1283-1286
* Software available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev website])

|-

|}

Projects:DTI DWI QualityControl

2011-11-03T20:26:32Z

MahshidLightMoon: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWIs data suffer from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep for assessing and correcting DWIs and DTI.

= Description =

DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

[[Image:Screenshot.png|400px|thumb|right| Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|3D view of gradients before and after Quality Control procedures]]

As further extended-DTIprep, our experiments show that the residual artifacts presence after DTIPrep DWIs QC can be detected and corrected using knowledge of DTI. We introduce new approach in DTI QC including the ''detection'' and the ''corrections'' steps. Our approach use new entropy-based benchmark comes up with Principal Directions (PDs) histogram implemented by multi-level subdivision icosahedron within different regions of brain. Given training of the measurement, the quality of DTI is categorized into acceptable, suspicious and unacceptable groups using calculated the standard scores.

[[Image:Diagram2_copy.png|400px|thumb|right| Comparison between control subject and green and red artifacts in terms of their corresponding PDs histogram on sphere.]]

[[Image:AutismTable3_copy.png|400px|thumb|right| The z-score of orientational entropies for autism sub- jects. The detected images are indexed from 13 and all suffer from color artifac.]]

We employ the correction step by excluding gradients which have the most contribution in the artifacts. We continue ex- cluding gradients till the z-score of whole brain of updated image will be close enough to the trained.

[[Image:Correction_Figure_copy.png|400px|thumb|right| The correction step result (the corrected image in right side) shows much more improvement in observing cc and fx tracts. The FA profiles of the genu and splenium are shown in bottom ( blue color: corrected image)]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2011-11-03T20:22:14Z

MahshidLightMoon: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWIs data suffer from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep for assessing and correcting DWIs and DTI.

= Description =

DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

[[Image:Screenshot.png|400px|thumb|right| Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|3D view of gradients before and after Quality Control procedures]]

As further extended-DTIprep, our experiments show that the residual artifacts presence after DTIPrep DWIs QC can be detected and corrected using knowledge of DTI. We introduce new approach in DTI QC including the ''detection'' and the ''corrections'' steps. Our approach use new entropy-based benchmark comes up with Principal Directions (PDs) histogram implemented by multi-level subdivision icosahedron within different regions of brain. Given training of the measurement, the quality of DTI is categorized into acceptable, suspicious and unacceptable groups using calculated the standard scores.

[[Image:Diagram2_copy.png|400px|thumb|right| Comparison between control subject and green and red artifacts in terms of their corresponding PDs histogram on sphere.]]

[[Image:AutismTable3_copy.png|400px|thumb|right| The z-score of orientational entropies for autism sub- jects. The detected images are indexed from 13 and all suffer from color artifac.]]

We employ the correction step by excluding gradients which have the most contribution in the artifacts. We continue ex- cluding gradients till the z-score of whole brain of updated image will be close enough to the trained.

[[Image:Correction_Figure_copy.png|400px|thumb|right|The z-score of orientational entropies for autism sub- jects. The detected images are indexed from 13 and all suffer from color artifac.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2011-11-03T20:21:24Z

MahshidLightMoon: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWIs data suffer from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep for assessing and correcting DWIs and DTI.

= Description =

DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

[[Image:Screenshot.png|400px|thumb|right|alt Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|alt 3D view of gradients before and after Quality Control procedures]]

As further extended-DTIprep, our experiments show that the residual artifacts presence after DTIPrep DWIs QC can be detected and corrected using knowledge of DTI. We introduce new approach in DTI QC including the ''detection'' and the ''corrections'' steps. Our approach use new entropy-based benchmark comes up with Principal Directions (PDs) histogram implemented by multi-level subdivision icosahedron within different regions of brain. Given training of the measurement, the quality of DTI is categorized into acceptable, suspicious and unacceptable groups using calculated the standard scores.

[[Image:Diagram2_copy.png|400px|thumb|right|alt Comparison between control subject and green and red artifacts in terms of their corresponding PDs histogram on sphere.]]

[[Image:AutismTable3_copy.png|400px|thumb|right|alt The z-score of orientational entropies for autism sub- jects. The detected images are indexed from 13 and all suffer from color artifac.]]

We employ the correction step by excluding gradients which have the most contribution in the artifacts. We continue ex- cluding gradients till the z-score of whole brain of updated image will be close enough to the trained.

[[Image:Correction_Figure_copy.png|400px|thumb|right|alt The z-score of orientational entropies for autism sub- jects. The detected images are indexed from 13 and all suffer from color artifac.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2011-11-03T20:15:13Z

MahshidLightMoon: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWIs data suffer from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep for assessing and correcting DWIs and DTI.

= Description =

DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

[[Image:Screenshot.png|400px|thumb|right|alt Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|alt 3D view of gradients before and after Quality Control procedures]]

As further extended-DTIprep, our experiments show that the residual artifacts presence after DTIPrep DWIs QC can be detected and corrected using knowledge of DTI. We introduce new approach in DTI QC including the ''detection'' and the ''corrections'' steps. Our approach use new entropy-based benchmark comes up with Principal Directions (PDs) histogram implemented by multi-level subdivision icosahedron within different regions of brain. Given training of the measurement, the quality of DTI is categorized into acceptable, suspicious and unacceptable groups using calculated the standard scores.

[[Image:Diagram2_copy.png|400px|thumb|right|alt Comparison between control subject and green and red artifacts in terms of their corresponding PDs histogram on sphere.]]

We employ the correction step by excluding gradients which have the most contribution in the artifacts. We continue ex- cluding gradients till the z-score of whole brain of updated image will be close enough to the trained

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

File:Correction Figure copy.png

2011-11-03T20:14:19Z

MahshidLightMoon:

File:Diagram2 copy.png

2011-11-03T20:10:39Z

MahshidLightMoon:

File:AutismTable3 copy.png

2011-11-03T20:08:50Z

MahshidLightMoon:

File:Correction Figure copy.pdf

2011-11-03T20:08:17Z

MahshidLightMoon:

Projects:DTI DWI QualityControl

2011-11-03T19:58:00Z

MahshidLightMoon: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWIs data suffer from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep for assessing and correcting DWIs and DTI.

= Description =

DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

[[Image:Screenshot.png|400px|thumb|right|alt Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|alt 3D view of gradients before and after Quality Control procedures]]

As further extended-DTIprep, our experiments show that the residual artifacts presence after DTIPrep DWIs QC can be detected and corrected using knowledge of DTI. We introduce new approach in DTI QC including the ''detection'' and the ''corrections'' steps. Our approach use new entropy-based benchmark comes up with Principal Directions (PDs) histogram implemented by multi-level subdivision icosahedron within different regions of brain. Given training of the measurement, the quality of DTI is categorized into acceptable, suspicious and unacceptable groups using calculated the standard scores.

[[Image:Diagram2.pdf|400px|thumb|right|alt Comparison between control subject and green and red artifacts in terms of their corresponding PDs histogram on sphere.]]

We employ the correction step by excluding gradients which have the most contribution in the artifacts. We continue ex- cluding gradients till the z-score of whole brain of updated image will be close enough to the trained

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

File:Diagram2.pdf

2011-11-03T19:49:40Z

MahshidLightMoon:

Projects:DTI DWI QualityControl

2011-11-03T19:48:36Z

MahshidLightMoon: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWIs data suffer from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep for assessing and correcting DWIs and DTI.

= Description =

DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

As further extended-DTIprep, our experiments show that the residual artifacts presence after DTIPrep DWIs QC can be detected and corrected using knowledge of DTI. We introduce new approach in DTI QC including the ''detection'' and the ''corrections'' steps. Our approach use new entropy-based benchmark comes up with Principal Directions (PDs) histogram within different regions of brain. Given training of the measurement, the quality of DTI is categorized into acceptable, suspicious and unacceptable groups using calculated the standard scores.

We employ the correction step by excluding gradients which have the most contribution in the artifacts. We continue ex- cluding gradients till the z-score of whole brain of updated image will be close enough to the trained

[[Image:Screenshot.png|400px|thumb|right|alt Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|alt 3D view of gradients before and after Quality Control procedures]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2011-11-03T19:21:50Z

MahshidLightMoon: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWIs data suffer from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep for assessing and correcting DWIs and DTI.

= Description =

DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

As further extended-DTIprep, our experiments show that the residual artifacts presence after DTIPrep DWIs QC can be detected and corrected using knowledge of DTI. We introduce new entropy-based measurement of the DTI data from Principal Directions histogram of the entire image. After training our measurement, the QC is applied on assessing DTI information within gray-matter regions, white-matter areas or the entire image. Thus, DTI artifacts are detected if an unusually high-degree of PDs clusters or unusually uniform distribution are found.

[[Image:Screenshot.png|400px|thumb|right|alt Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|alt 3D view of gradients before and after Quality Control procedures]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2011-11-03T19:19:07Z

MahshidLightMoon:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWIs data suffer from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep for assessing and correcting DWIs and DTI.

= Description =

DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

As further extended-DTIprep, our experiments show that the residual artifacts presence after DTIPrep QC can be detected and corrected using knowledge of DTI. We introduce new entropy-based measurement of the DTI data from Principal Directions histogram of the entire image. After training our measurement, the QC is applied on assessing DTI information within gray-matter regions, white-matter areas or the entire image. Thus, DTI artifacts are detected if an unusually high-degree of PDs clusters or unusually uniform distribution are found.

[[Image:Screenshot.png|400px|thumb|right|alt Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|alt 3D view of gradients before and after Quality Control procedures]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

2011 Summer Project Week DTIPrep

2011-06-24T13:50:17Z

MahshidLightMoon: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
Image:Screenshot.png|Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep
Image:753361684_3.png|3D view of gradients before and after Quality Control procedures
</gallery>

==Motivations: Automated Diffusion Weighted Imaging Quality Control ==
Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. Unfortunately, the DWIs suffer from a lots of artifacts because of the inherent low SNR and long scanning time of multiple directional encoding. Thus, it necessitates the development of Quality Control (QC) as the automated tool for DWIs. We have developed a tool called DTIPrep which is fully open source. In the DTIPrep framework, the following checking procedures are applied within a pipelining structure and described by a protocoling file. These pipeline procedures include: 1) image info checking, 2) diffusion info checking, 3) slice-wise intensity checking, 4) interlace-wise intensity checking, 5) averaging baselines, 6) eddy-motion correction, 7) gradient-wise checking, 8) computing DTI measurements and saving. As our current study, we are focusing on extending DTIPrep to detect artifacts through DTI data. According to our experiments, there are a number of artifacts which can not be detected from the DWIs using only voxel-wise knowledge. For example, residual artifact presence after DWI QC which shows a reddish appearance for DTIs.

==Key Investigators==
* UIowa: Hans Johnson, Mark Scully, Joy Matsui
* UNC: Martin Styner, Clement Vachet, Mahshid Farzinfar

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
'''1.''' Introducing new automated QC tool called DTIPrep that includes the pipeline checking and correcting procedures against the DWI acquired protocol and generating QCed result.

'''2.''' Integrating the visual checking considering into the automated QC procedures consistently and synchronously.

'''3.''' Developing DTIPrep by introducing new measurement of DTIs properties to detect residual artifacts presence after DWI QC.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

'''1.''' Checking size, origin, spacing of image, checking b-values, measurement frames and diffusion sensitizing directions. Applying statistical analysis on computed intensity-based correlations between successive slices and furthermore against interleaved parts of each gradient. Applying rigid and affine registration to correct and detect the motion artifacts. Generating xml QC report file. More details are found in [1].

'''2.''' Describing each gradient as "included" or "excluded" after not only applying QC but also visual checking analysis and defining further QC when some gradients get "included" in the visual checking step.

'''3.''' Detecting and correcting artifacts based on new entropy-based measurement of Principal Directions (PD) distribution within whole image.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
'''*''' All the pipeline procedures of DWI QC have been implemented successfully. DTIPrep generates QC report in xml format and with facilities of reloading QC result and checking mismatching between the image and corresponding reloaded QC report.

"-" In this week, we focused on the part which helps user to be able change the protocols features in the pipleline and generates proper QCed report.

'''*''' Doing visual checking after QC and then asking user whether further QC is applied for updated gradients or the QCed result is just saved.

"-" In this week, we did testing and checking for saving QCed result after doing visual checking.

'''*''' Training and testing the new entropy-based measurement of PD histogram within gray-matter, white-matter areas and the entire image.

"-" We were applying the new measurement on the new data from Tutorial on the whole image, while matter and gray-matter regions.

</div>
</div>



==References==
'''[1]''' Z Liu, Y Wang, G Gerig, S Gouttard, R Tao, T Fletcher, M Styner: Quality control of diffusion weighted images. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol: 7628, pg: 17, 2010.

2011 Summer Project Week DTIPrep

2011-06-24T13:30:54Z

MahshidLightMoon: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
Image:Screenshot.png|Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep
Image:753361684_3.png|3D view of gradients before and after Quality Control procedures
</gallery>

==Motivations: Automated Diffusion Weighted Imaging Quality Control ==
Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. Unfortunately, the DWIs suffer from a lots of artifacts because of the inherent low SNR and long scanning time of multiple directional encoding. Thus, it necessitates the development of Quality Control (QC) as the automated tool for DWIs. We have developed a tool called DTIPrep which is fully open source. In the DTIPrep framework, the following checking procedures are applied within a pipelining structure and described by a protocoling file. These pipeline procedures include: 1) image info checking, 2) diffusion info checking, 3) slice-wise intensity checking, 4) interlace-wise intensity checking, 5) averaging baselines, 6) eddy-motion correction, 7) gradient-wise checking, 8) computing DTI measurements and saving. As our current study, we are focusing on extending DTIPrep to detect artifacts through DTI data. According to our experiments, there are a number of artifacts which can not be detected from the DWIs using only voxel-wise knowledge. For example, residual artifact presence after DWI QC which shows a reddish appearance for DTIs.

==Key Investigators==
* UIowa: Hans Johnson, Mark Scully, Joy Matsui
* UNC: Martin Styner, Clement Vachet, Mahshid Farzinfar

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
'''1.''' Introducing new automated QC tool called DTIPrep that includes the pipeline checking and correcting procedures against the DWI acquired protocol and generating QCed result.

'''2.''' Integrating the visual checking considering into the automated QC procedures consistently and synchronously.

'''3.''' Developing DTIPrep by introducing new measurement of DTIs properties to detect residual artifacts presence after DWI QC.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

'''1.''' Checking size, origin, spacing of image, checking b-values, measurement frames and diffusion sensitizing directions. Applying statistical analysis on computed intensity-based correlations between successive slices and furthermore against interleaved parts of each gradient. Applying rigid and affine registration to correct and detect the motion artifacts. Generating xml QC report file. More details are found in [1].

'''2.''' Describing each gradient as "included" or "excluded" after not only applying QC but also visual checking analysis and defining further QC when some gradients get "included" in the visual checking step.

'''3.''' Detecting and correcting artifacts based on new entropy-based measurement of Principal Directions (PD) distribution within whole image.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
'''*''' All the pipeline procedures of DWI QC have been implemented successfully. DTIPrep generates QC report in xml format and with facilities of reloading QC result and checking mismatching between the image and corresponding reloaded QC report.

"-" In this week, we focused on the part which helps user to be able change the protocols features in the pipleline and generates proper QCed report.

'''*''' Doing visual checking after QC and then asking user whether further QC is applied for updated gradients or the QCed result is just saved.

"-" In this week, we did testing an checking for saving QCed result after doing visual checking.

'''*''' Training and testing the new entropy-based measurement of PD histogram within gray-matter, white-matter areas and the entire image.

"-" We were applying the new measurement on the new data from Tutorial on the whole image, while matter and gray-matter regions.

</div>
</div>



==References==
'''[1]''' Z Liu, Y Wang, G Gerig, S Gouttard, R Tao, T Fletcher, M Styner: Quality control of diffusion weighted images. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol: 7628, pg: 17, 2010.

2011 Summer Project Week DTIPrep

2011-06-24T13:30:20Z

MahshidLightMoon: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
Image:Screenshot.png|Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep
Image:753361684_3.png|3D view of gradients before and after Quality Control procedures
</gallery>

==Motivations: Automated Diffusion Weighted Imaging Quality Control ==
Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. Unfortunately, the DWIs suffer from a lots of artifacts because of the inherent low SNR and long scanning time of multiple directional encoding. Thus, it necessitates the development of Quality Control (QC) as the automated tool for DWIs. We have developed a tool called DTIPrep which is fully open source. In the DTIPrep framework, the following checking procedures are applied within a pipelining structure and described by a protocoling file. These pipeline procedures include: 1) image info checking, 2) diffusion info checking, 3) slice-wise intensity checking, 4) interlace-wise intensity checking, 5) averaging baselines, 6) eddy-motion correction, 7) gradient-wise checking, 8) computing DTI measurements and saving. As our current study, we are focusing on extending DTIPrep to detect artifacts through DTI data. According to our experiments, there are a number of artifacts which can not be detected from the DWIs using only voxel-wise knowledge. For example, residual artifact presence after DWI QC which shows a reddish appearance for DTIs.

==Key Investigators==
* UIowa: Hans Johnson, Mark Scully, Joy Matsui
* UNC: Martin Styner, Clement Vachet, Mahshid Farzinfar

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
'''1.''' Introducing new automated QC tool called DTIPrep that includes the pipeline checking and correcting procedures against the DWI acquired protocol and generating QCed result.

'''2.''' Integrating the visual checking considering into the automated QC procedures consistently and synchronously.

'''3.''' Developing DTIPrep by introducing new measurement of DTIs properties to detect residual artifacts presence after DWI QC.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

'''1.''' Checking size, origin, spacing of image, checking b-values, measurement frames and diffusion sensitizing directions. Applying statistical analysis on computed intensity-based correlations between successive slices and furthermore against interleaved parts of each gradient. Applying rigid and affine registration to correct and detect the motion artifacts. Generating xml QC report file. More details are found in [1].

'''2.''' Describing each gradient as "included" or "excluded" after not only applying QC but also visual checking analysis and defining further QC when some gradients get "included" in the visual checking step.

'''3.''' Detecting and correcting artifacts based on new entropy-based measurement of Principal Directions (PD) distribution within whole image.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
'''*''' All the pipeline procedures of DWI QC have been implemented successfully. DTIPrep generates QC report in xml format and with facilities of reloading QC result and checking mismatching between the image and corresponding reloaded QC report.

"-" In this week, we focused on the part which helps user to be able change the protocols features in the pipleline and generates proper QCed report.

'''*''' Doing visual checking after QC and then asking user whether further QC is applied for updated gradients or the QCed result is just saved.

"-" In this week, we did testing an checking for saving QCed result after doing visual checking.

'''*''' Training and testing the new entropy-based measurement of PD histogram within gray-matter, white-matter areas and the entire image.

"-" We were applying the new measurement on the new data from Tutorial on the whole image, while matter and gray-matter regions.

</div>
</div>



==References==
'''[1]''' Z Liu, Y Wang, G Gerig, S Gouttard, R Tao, T Fletcher, M Styner: Quality control of diffusion weighted images. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol: 7628, pg: 17, 2010.

2011 Summer Project Week DTIPrep

2011-06-24T13:29:31Z

MahshidLightMoon: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
Image:Screenshot.png|Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep
Image:753361684_3.png|3D view of gradients before and after Quality Control procedures
</gallery>

==Motivations: Automated Diffusion Weighted Imaging Quality Control ==
Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. Unfortunately, the DWIs suffer from a lots of artifacts because of the inherent low SNR and long scanning time of multiple directional encoding. Thus, it necessitates the development of Quality Control (QC) as the automated tool for DWIs. We have developed a tool called DTIPrep which is fully open source. In the DTIPrep framework, the following checking procedures are applied within a pipelining structure and described by a protocoling file. These pipeline procedures include: 1) image info checking, 2) diffusion info checking, 3) slice-wise intensity checking, 4) interlace-wise intensity checking, 5) averaging baselines, 6) eddy-motion correction, 7) gradient-wise checking, 8) computing DTI measurements and saving. As our current study, we are focusing on extending DTIPrep to detect artifacts through DTI data. According to our experiments, there are a number of artifacts which can not be detected from the DWIs using only voxel-wise knowledge. For example, residual artifact presence after DWI QC which shows a reddish appearance for DTIs.

==Key Investigators==
* UIowa: Hans Johnson, Mark Scully, Joy Matsui
* UNC: Martin Styner, Clement Vachet, Mahshid Farzinfar

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
'''1.''' Introducing new automated QC tool called DTIPrep that includes the pipeline checking and correcting procedures against the DWI acquired protocol and generating QCed result.

'''2.''' Integrating the visual checking considering into the automated QC procedures consistently and synchronously.

'''3.''' Developing DTIPrep by introducing new measurement of DTIs properties to detect residual artifacts presence after DWI QC.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

'''1.''' Checking size, origin, spacing of image, checking b-values, measurement frames and diffusion sensitizing directions. Applying statistical analysis on computed intensity-based correlations between successive slices and furthermore against interleaved parts of each gradient. Applying rigid and affine registration to correct and detect the motion artifacts. Generating xml QC report file. More details are found in [1].

'''2.''' Describing each gradient as "included" or "excluded" after not only applying QC but also visual checking analysis and defining further QC when some gradients get "included" in the visual checking step.

'''3.''' Detecting and correcting artifacts based on new entropy-based measurement of Principal Directions (PD) distribution within whole image.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
'''*''' All the pipeline procedures of DWI QC have been implemented successfully. DTIPrep generates QC report in xml format and with facilities of reloading QC result and checking mismatching between the image and corresponding reloaded QC report.
"-" In this week, we focused on the part which helps user to be able change the protocols features in the pipleline and generates proper QCed report.

'''*''' Doing visual checking after QC and then asking user whether further QC is applied for updated gradients or the QCed result is just saved.
"-" In this week, we did testing an checking for saving QCed result after doing visual checking.

'''*''' Training and testing the new entropy-based measurement of PD histogram within gray-matter, white-matter areas and the entire image.
"-" We were applying the new measurement on the new data from Tutorial on the whole image, while matter and gray-matter regions.

</div>
</div>



==References==
'''[1]''' Z Liu, Y Wang, G Gerig, S Gouttard, R Tao, T Fletcher, M Styner: Quality control of diffusion weighted images. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol: 7628, pg: 17, 2010.

2011 Summer Project Week DTIPrep

2011-06-15T19:50:58Z

MahshidLightMoon: Created page with '__NOTOC__ <gallery> Image:PW-SLC2011.png|Projects List Image:Screenshot.png|Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality …'

__NOTOC__
<gallery>
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
Image:Screenshot.png|Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep
Image:753361684_3.png|3D view of gradients before and after Quality Control procedures
</gallery>

==Motivations: Automated Diffusion Weighted Imaging Quality Control ==
Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. Unfortunately, the DWIs suffer from a lots of artifacts because of the inherent low SNR and long scanning time of multiple directional encoding. Thus, it necessitates the development of Quality Control (QC) as the automated tool for DWIs. We have developed a tool called DTIPrep which is fully open source. In the DTIPrep framework, the following checking procedures are applied within a pipelining structure and described by a protocoling file. These pipeline procedures include: 1) image info checking, 2) diffusion info checking, 3) slice-wise intensity checking, 4) interlace-wise intensity checking, 5) averaging baselines, 6) eddy-motion correction, 7) gradient-wise checking, 8) computing DTI measurements and saving. As our current study, we are focusing on extending DTIPrep to detect artifacts through DTI data. According to our experiments, there are a number of artifacts which can not be detected from the DWIs using only voxel-wise knowledge. For example, residual artifact presence after DWI QC which shows a reddish appearance for DTIs.

==Key Investigators==
* UIowa: Hans Johnson, Mark Scully, Joy Matsui
* UNC: Martin Styner, Clement Vachet, Mahshid Farzinfar

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
'''1.''' Introducing new automated QC tool called DTIPrep that includes the pipeline checking and correcting procedures against the DWI acquired protocol and generating QCed result.

'''2.''' Integrating the visual checking considering into the automated QC procedures consistently and synchronously.

'''3.''' Developing DTIPrep by introducing new measurement of DTIs properties to detect residual artifacts presence after DWI QC.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

'''1.''' Checking size, origin, spacing of image, checking b-values, measurement frames and diffusion sensitizing directions. Applying statistical analysis on computed intensity-based correlations between successive slices and furthermore against interleaved parts of each gradient. Applying rigid and affine registration to correct and detect the motion artifacts. Generating xml QC report file. More details are found in [1].

'''2.''' Describing each gradient as "included" or "excluded" after not only applying QC but also visual checking analysis and defining further QC when some gradients get "included" in the visual checking step.

'''3.''' Detecting and correcting artifacts based on new entropy-based measurement of Principal Directions (PD) distribution within whole image.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
'''*''' All the pipeline procedures of DWI QC have been implemented successfully. DTIPrep generates QC report in xml format and with facilities of reloading QC result and checking mismatching between the image and corresponding reloaded QC report.

'''*''' Doing visual checking after QC and then asking user whether further QC is applied for updated gradients or the QCed result is just saved.

'''*''' Training and testing the new entropy-based measurement of PD histogram within gray-matter, white-matter areas and the entire image.

</div>
</div>



==References==
'''[1]''' Z Liu, Y Wang, G Gerig, S Gouttard, R Tao, T Fletcher, M Styner: Quality control of diffusion weighted images. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol: 7628, pg: 17, 2010.

2011 Summer Project Week

2011-06-15T19:33:03Z

MahshidLightMoon: /* Projects */

Back to [[Events]]

[[image:PW-MIT2011.png|right|200px]]

*'''Dates:''' June 20-24, 2011
*'''Location:''' MIT

==Preliminary Agenda==
==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, June 20
!style="width:18%" |Tuesday, June 21
!style="width:18%" |Wednesday, June 22
!style="width:18%" |Thursday, June 23
!style="width:18%" |Friday, June 24
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"|'''NA-MIC Update Day'''
|
|bgcolor="#88aaae"|'''IGT Day'''
|bgcolor="#faedb6"|'''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9am-12pm'''
|
|'''9am-11am:''' [[2011 Project Week Breakout Session: Slicer4|Slicer 4 Core Modules Usability Review]]''' [[MIT_Project_Week_Rooms#Star|Star Room]]
<br>----------------------------------------<br>
'''11-12pm: <font color="#4020ff">Breakout Session:'''</font> [[2011 Summer Project Week Breakout Session Slicer4 Annotation|Slicer4 Annotations]] (Nicole Aucoin)<br>
[[MIT_Project_Week_Rooms#Star|Star Room]]
|'''9am-12pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2011 Project Week Breakout Session: ITK|ITK]] (Luis Ibanez)
<br>[[MIT_Project_Week_Rooms#Kiva_32-G449|Kiva Room]]
|'''9am-4pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2011 Summer Project Week Breakout Session:OpenIGTLink|OpenIGTLink]]
<br>[[MIT_Project_Week_Rooms#Kiva_32-G449|Kiva Room]]
|'''10am-12pm:''' [[#Projects|Project Progress Updates]]
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|-
|bgcolor="#ffffdd"|'''12pm-1pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1pm-5:30pm'''
|'''1-1:05pm: <font color="#503020">Ron Kikinis: Welcome</font>'''
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''3:30-5:00pm: NA-MIC Kit Update''' Slicer4 Developers Guided Tour (Pieper) ([[media:2011 Summer-Slicer4.ppt|Draft Slides]]), Slicer4 Extension Writing Tutorial (Finet, Fillion-Robin)
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|'''1-3pm: <font color="#4020ff">Breakout Session:'''</font><br> [http://wiki.slicer.org/slicerWiki/index.php/Slicer4:MultiVolumeContainer#Summer_2011_Project_Week_Breakout_Session Slicer4 MultiVolume Containers] (Nicole Aucoin)
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Star Room]]
<br>----------------------------------------<br>
'''3-4pm:''' [[Summer_2011_Tutorial_Contest|Tutorial Contest Presentations]]
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''4-5pm:''' [[2011 Summer Project Week Breakout Session VTKCharts|VTK Charts]] (Marcus Hanwell)
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Star Room]]
|'''12:45-1pm:''' [[Events:TutorialContestJune2011|Tutorial Contest Winner Announcement]]
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''3-4pm: <font color="#4020ff">Breakout Session:'''</font><br>[[2011_Summer_Project_Week_Breakout_Session_EMRegistration|Inter-subject Registration for EM segmenter]]
<br>[[MIT_Project_Week_Rooms#Kiva_32-G449|Kiva Room]]
|'''1-4pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2011 Summer Project Week Breakout Session:OpenIGTLink|OpenIGTLink]]
<br>[[MIT_Project_Week_Rooms#Kiva_32-G449|Kiva Room]]
|
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|
|}

==Projects==

* Please make a link for your project to a new page based on the [[2011_Summer_Project_Week_Template| 2011 Summer Project Page Template]]

#valuate the applicability of DICOM RT I/O facility in Slicer (via Plastimatch Extension) for Brachytherapy Planning (Tina Kapur, Greg Sharp, Robert Cormack?)
#Visualization of b-spline and vector fields (Steve, Danielle, Dominik)
#[[2011_Summer_Project_Week_Annotation_Module|Annotation Module in Slicer4]] (Nicole Aucoin, Daniel Haehn)
#[[2011_Summer_Project_Week_RECIST|RECIST Slicer4 module]] (Nicole Aucoin)
#DicomToNrrdConverter refactoring ( Xiaodong Tao, Mark Scully)
#[[2011_Summer_Project_Week_normal_consistency_particles|Normal consistency in particle correspondence computation using great circles in principal spheres - Huntington's Disease]], (Beatriz Paniagua, Martin Styner, Sungkyu Jung, Mark Scully)
#Group-wise Automatic Mesh-Based analysis of CortIcal Thickness (GAMBIT) - TBI (Clement Vachet, Martin Styner, Randi Gollub?)
#[[2011_Summer_Project__Week_Shape_Analysis_UNC |SPHARM & particles shape analysis - Huntington's Disease]] - Lucile Bompard, Clement Vachet, Beatriz Paniagua, Martin Styner
#Non-rigid, inter-patient registration of bone masks derived from CT for Head and Neck Cancer Radiation Therapy (Ivan Kolesov, Yi Gao, Gregory Sharp, and Allen Tannenbaum)
#[[2011_Summer_Project_Week_RSS_for_AFib_Ablation|Robust Statistical Segmentation (RSS) for the Atrial Fibrillation Ablation Therapy]] (Yi Gao, Kedar Patwardhan, Wassim Haddad, and Allen Tannenbaum, Rob MacLeod, Josh Blauer, and Josh Cates)
#Mass Spectrometry for Brain Tumor Therapy (Behnood Gholami, Nathalie Agar)
#[[Multimodality Image Registration for TBI]] (Yifei Lou, Danielle Pace, Jack Van Horn?, Marcel Prastawa?)
#[[2011_Summer_Project_Week_Segmentation_TBI|Segmentation of Longitudinal TBI data]] (Bo Wang, Jack Van Horn, Andrei Irimia, Marcel Prastawa, Guido Gerig)
#Longitudinal Shape Regression - Huntington's Disease (James Fishbaugh, Guido Gerig)
#[[2011_Summer_Project__Week_DVH|Dose volume histograms in Slicer]] (Greg Sharp, Nadya Shusharina, Steve Pieper, Csaba Pinter, Tina Kapur)
#[[2011_Summer_Project__Week_DICOM_RT|Synthetic images, vector fields, RT structures and RT doses in Slicer and ITK]]. (Nadya Shusharina, Greg Sharp, Luis Ibanez, Steve Pieper)
#[[2011_Summer_Project_Week_Watersheds|Interactive Watersheds Segmentation Module for Slicer for Atrial Fibrillation and HN Cancer]] (Josh Cates, Ross Whitaker, Steve Pieper, Jim Miller, Nadya)
#Segmentation of Nerve and Nerve Ganglia in the Spine (Adrian Dalca, Giovanna Danagoulian, Ron Kikinis, Ehud Schmidt, Polina Golland)
#Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps (Ramesh Sridharan, Polina Golland)
#Shapeworks Shape Analysis for Atrial Fibrilliation and HD (Manasi Datar, Beatriz UNC, Mark Scully)
#Explore the applicability of RSS and Shapeworks for Ventricular Segmentation(Chiara Carminati, ?, ?)
#[[2011_Summer_Project_Week_Integrate_BRAINSCut_into_Slicer3]](Regina Kim, ... )
#The Vascular Modeling Toolkit in 3D Slicer 4 (Daniel Haehn, Luca Antiga, Steve Pieper)
#[[2011_Summer_Project_Week__LANDWARP_integration|Integration of LANDWARP into interactive registration module]] (Andrey Fedorov, Greg Sharp, Nadya Shusharina)
#[[2011_Summer_Project_Week_Registration_of_mouse_brains|Registration of mouse brains]] (Francois Budin)
#ShapeWorks Applications (Manasi Datar, Beatriz Paniagua, Martin Styner, Ross Whitaker, ?)
#[[2011_Summer_Project__Week_Wireless_Joystick|Wireless joystick controlling medical devices and software (e.g. Slicer4) in the operating room]] (Szymon Kostrzewski)
#[[2011_Summer_Project__Week_Live_Tracked_Ultrasound|Live Tracked Ultrasound with Slicer4 (Tamas Heffter)]]
#[http://wiki.na-mic.org/Wiki/index.php/Survey_stealthlink_openigtlink Surveying research teams interested in Open IGT Link support of Stealth Station (Nobuhiko Hata, Ron Kikinis)]
#[[2011_Summer_Project_Week_DTIPrep|DTIPrep - "Study-specific Protocol" based automatic DWI/DTI quality control and preparation]] - Huntington's Disease (Mashid Farzinfar, Clement Vachet, Joy Matsui, Martin Styner)
#DTIProcessing - Huntington's Disease (Clement Vachet, Joy Matsui, Martin Styner)
#Volumetric DTI into Slicer for HD for Tract based roi segmentation (Steve Callahan, Mark Scully, Jim Miller)
#Nifti Support for Diffusion Tensor Images (Demian)
#Finishing details on the workflows: DICOM->Full brain tractography / peritumoral (Demian)
#Refactoring of the tractography display widget (Isaiah)
#Laterality (Lauren)
#Selection for models and bundles post-clustering (Lauren)
#ROI-based / picking selection of fiber bundles (Maybe one of Sylvain's interns)
#Adding streamlined tractography to the Finsler front propagation tractography toolkit (Antonio)
#Add ODF estimation / visualization (Antonio)
#[[Summer_project_week_2011_Workflows_SOA|Workflows and Service Oriented Architecture Modules for Slicer4 as Extensions]] (Alexander Zaitsev, Wendy Plesniak, Ron Kikinis)
#[[2011_Summer_Project__Week_DICOM_Networking|DICOM Networking interface for Slicer4]] (Steve Pieper, Nicole Aucoin, Noby Hata)
#Stenosis Detector in 3D Slicer 4 (Suares Tamekue, Daniel Haehn, Luca Antiga)
#[[2011_Summer_Project_Week_Spine_Segmentation_And_Osteoporosis_Screening_CT|Spine Segmentation & Osteoporosis Screening In CT Imaging Studies]] (Anthony Blumfield)
#Slicer module for building an average population HARDI Atlas (Ryan Eckbo)
#4D Ultrasound (Laurent Chauvin, Noby Hata, Atsushi Yamada)
#EM Segmentation in 3D Slicer 4 (Daniel Haehn, Dominique Belhachemi, Kilian Pohl)
#[[NonRigidRegistrationThatAccommodatesResection|Demons Based Non-Rigid Registration that Accommodates Resection in 3D Slicer]] (Petter Risholm, Sandy Wells)
#[[2011_Summer_Project_Week_re-parameterize_fiber|Re-parameterize fiber tracts for fiber statistics analysis]]
#[[2011_Summer_Project_Week_Automated_GUI_Testing| Automated GUI Testing (Sonia Pujol, Steve Pieper, Dave Partyka, Jean-Christophe Fillion-Robin, Xiaodong Tao)]]
#[[2011_Summer_Project_Week_Plastimatch_for_EMSegmenter | Integrating Plastimatch into the EMSegmenter]] (Dominique Belhachemi, Kilian Pohl, Greg Sharp)
#[[2011_Summer_Project_Week_Customizing_EMSegmenter_pipelines_for_brain_lesions | Customizing EMSegmenter pipelines for brain lesions]] (Dominique Belhachemi, Alexander Zaitsev, Kilian Pohl)
#[[2011_Summer_Project_Week_Slicer_Extension_for_GLISTR | Slicer extension for GLiome Image SegmenTation and Registration (GLISTR)]] (Andreas Schuh, Daniel Haehn, Kilian Pohl)
#[[2011_Summer_Project_Week_WMGeometry_Slicer4 | White matter geometry measures in Slicer 4]] (Peter Savadjiev)
#Internationalization of Slicer (Luping Fang, Steve Pieper, Daniel Haehn, Suares Tamekue, Jean-Christophe Fillion-Robin, Jean-Christophe, Julien Finet, Yiming Ge, Ping Cao)
#[[2011_Summer_Project_Week__BRAINSFit_new_features_integration|Integrate new features into BRAINSFit]] (Andrey Fedorov, Hans Johnson, Mark Scully)
#[[2011_Summer_Project_Week_FetchMI:_Slicer_integration_with_XNAT |FetchMI: Slicer integration with XNAT 1.5]] (Misha Milchenko, Wendy Plesniak)
#[[2011_Summer_Project_Week_ODF_though_Fiber_Counting | ODF computation through fiber counting]] (Yinpeng Li, Ipek Oguz, Martin Styner)
#[[2011_Summer_Project_Week_Intraoperative_Brain_Shift_Monitor|Intraoperative Brain Shift Monitor]] (Jason White, Alex Golby, Steve Pieper)
#[[2011_Summer_Project_Week_DTI_Volumetric_Segmentation_for_Group_studies | DTI Volumetric Segmentation for Group studies]] (Gopal Veni, Ross Whitaker)

==Background==

We are pleased to announce the 13th 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. If you would like to learn more about this event, please [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week click here to join our mailing list].

Active preparation begins on Thursday, April 28th 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.

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 40-50 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.

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.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== Logistics ==
*'''Dates:''' June 20-24, 2011
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].
*'''REGISTRATION:''' Please register [http://guest.cvent.com/d/sdqy0l/4W here]. Payment must be made by credit card.
*'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week).
*'''Hotel:''' Boston Marriott Cambridge, Two Cambridge Center, 50 Broadway, Cambridge, MA 02142. Group rate is $199/night plus tax. Book [http://www.marriott.com/hotels/travel/boscb?groupCode=jrbjrba&app=resvlink&fromDate=6/19/11&toDate=6/24/11 here] or call 1-617-494-6600 and mention that you are booking in the MIT Room Block. '''All reservations must be made by May 29, 2011 to receive the discounted rate.'''

== Preparation ==

# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list
# The NA-MIC engineering team will be discussing infrastructure projects in a kickoff TCON on April 28, 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!
# By 3pm ET on Thursday May 12, all participants to add a one line title of their project to #Projects
#By 3pm ET on Thursday June 9, all project leads to complete [[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.
# By 3pm on June 16: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
## 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)
## 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.)
## 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)
# 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...
# People doing Slicer related projects should come to project week with slicer built on your laptop.
## 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).
## Projects to modify core behavior of slicer should be done on the [http://viewvc.slicer.org/viewcvs.cgi/trunk/ trunk].

==Registrants==

Do not add your name to this list- it is maintaining by the organizers based on your paid registration (see Logistics for link)
# Nicole Aucoin , Brigham and Women's Hospital
# Dominique Belhachemi , University of Pennsylvania
# Anthony Blumfiled , Radnostics
# Lucile Bompard , UNC NIRAL
# Sylvain Bouix , Brigham and Women's Hospital
# Francois Budin , UNC NIRAL
# Steve Callahan , University of Utah
# Joshua Cates , University of Utah
# Laurent Chauvin , Brigham and Women's Hospital
# Adrian Dalca , MIT CSAIL
# Manasi Datar , University of Utah
# Colin Davey ,
# Ryan Eckbo , Brigham and Women's Hospital
# Jan Egger , University Hospital of Marburg
# Luping Fang , Zhejiang University of Technology, China
# mahshid farzinfar , UNC
# Andriy Fedorov , Brigham and Women's Hospital
# Julien Finet , Kitware Inc.
# James Fishbaugh , University of Utah
# Greg Gardner , University of Utah
# Ronen Globinsky , Yale University
# Maged Goubran , Robarts Research Institute
# Daniel Haehn , University of Pennsylvania
# Mike Halle , Brigham and Women's Hospital
# Noby Hata , Brigham and Women's Hospital
# Tamas Heffter , Queen's University
# Andrei Irimia , University of California, Los Angeles
# Hans Johnson , University of Iowa
# Ilknur Kabul , Kitware
# Tina Kapur , Brigham and Women's Hospital
# Ron Kikinis , Brigham and Women's Hospital; Harvard Medical School
# Regina Kim , University of Iowa
# Szymon Kostrzewski , Ecole Polytechnique Federale de Lausanne EPFL
# Dillon Lee , University of Utah
# Yinpeng Li , UNC-NIRAL
# Yifei Lou , Georgia Institute of Technology
# mohsen mahvash , Harvard Medical School (BWH and VA)
# Katie Mastrogiacomo , Brigham and Women's Hospital
# Joy Matsui , University of Iowa
# Dominik Meier , BWH
# Mikhail Milchenko , Washington University in St. Louis
# James Miller , GE Research
# Isaiah Norton , Brigham and Women's Hospital
# Danielle Pace , Kitware
# Beatriz Paniagua , University of North Carolina at Chapel Hill
# Xenophon Papademetris , Yale University
# Kedar Patwardhan , GE Global Research
# Steve Pieper , Isomics, Inc.
# Csaba Pinter , Queen's University
# Wendy Plesniak , Brigham and Women's Hospital
# Kilian Pohl , UPenn
# Marcel Prastawa , University of Utah
# Sonia Pujol , Brigham and Women's Hospital
# Martin Rajchl , Robarts Research Institute
# Petter Risholm , Brigham and Women's Hospital
# Peter Savadjiev , Brigham and Women's Hospital
# Andreas Schuh , University of Pennsylvania
# Mark Scully , University of Iowa
# Gregory Sharp , MGH
# Yundi Shi , UNC-Chapel Hill
# Nadya Shusharina , MGH
# Ramesh Sridharan , MIT CSAIL
# Hao Su , WPI
# Suarez Tamekue , Brigham and Women's Hospital
# Xiaodong Tao , GE Research
# Clement Vachet , UNC Chapel Hill
# Antonio Vega , Brigham and Women's Hospital
# Gopal Veni , University of Utah
# Bo Wang , University of Utah
# Demian Wasserman , Brigham and Women's Hospital
# Sandy Wells , Brigham and Women's Hospital
# Jason White , Brigham and Women's Hospital
# Atsushi Yamada , Brigham and Women's Hospital
# Alexander Yarmarkovich , Isomics
# Alexander Zaitsev , Brigham and Women's Hospital

2011 Winter Project Week:DTIPrepDocumentation

2011-06-04T23:40:52Z

MahshidLightMoon: /* Motivations: Automated Diffusion Weighted Imaging Quality Control */

__NOTOC__
<gallery>
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
Image:Screenshot.png|Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep
Image:753361684_3.png|3D view of gradients before and after Quality Control procedures
</gallery>

==Motivations: Automated Diffusion Weighted Imaging Quality Control ==
Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. Unfortunately, the DWIs suffer from a lots of artifacts because of the inherent low SNR and long scanning time of multiple directional encoding. Thus, it necessitates the development of Quality Control (QC) as the automated tool for DWIs. We have developed a tool called DTIPrep which is fully open source. In the DTIPrep framework, the following checking procedures are applied within a pipelining structure and described by a protocoling file. These pipeline procedures include: 1) image info checking, 2) diffusion info checking, 3) slice-wise intensity checking, 4) interlace-wise intensity checking, 5) averaging baselines, 6) eddy-motion correction, 7) gradient-wise checking, 8) computing DTI measurements and saving. As our current study, we are focusing on extending DTIPrep to detect artifacts through DTI data. According to our experiments, there are a number of artifacts which can not be detected from the DWIs using only voxel-wise knowledge. For example, residual artifact presence after DWI QC which shows a reddish appearance for DTIs.

==Key Investigators==
* UIowa: Hans Johnson, Mark Scully, Joy Matsui
* UNC: Martin Styner, Clement Vachet, Mahshid Farzinfar, Cheryl Dietrich

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
'''1.''' Introducing new automated QC tool called DTIPrep that includes the pipeline checking and correcting procedures against the DWI acquired protocol and generating QCed result.

'''2.''' Integrating the visual checking considering into the automated QC procedures consistently and synchronously.

'''3.''' Developing DTIPrep by introducing new measurement of DTIs properties to detect residual artifacts presence after DWI QC.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

'''1.''' Checking size, origin, spacing of image, checking b-values, measurement frames and diffusion sensitizing directions. Applying statistical analysis on computed intensity-based correlations between successive slices and furthermore against interleaved parts of each gradient. Applying rigid and affine registration to correct and detect the motion artifacts. Generating xml QC report file. More details are found in [1].

'''2.''' Describing each gradient as "included" or "excluded" after not only applying QC but also visual checking analysis and defining further QC when some gradients get "included" in the visual checking step.

'''3.''' Detecting and correcting artifacts based on new entropy-based measurement of Principal Directions (PD) distribution within whole image.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
'''*''' All the pipeline procedures of DWI QC have been implemented successfully. DTIPrep generates QC report in xml format and with facilities of reloading QC result and checking mismatching between the image and corresponding reloaded QC report.

'''*''' Doing visual checking after QC and then asking user whether further QC is applied for updated gradients or the QCed result is just saved.

'''*''' Training and testing the new entropy-based measurement of PD histogram within gray-matter, white-matter areas and the entire image.

</div>
</div>



==References==
'''[1]''' Z Liu, Y Wang, G Gerig, S Gouttard, R Tao, T Fletcher, M Styner: Quality control of diffusion weighted images. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol: 7628, pg: 17, 2010.

Projects:DTI DWI QualityControl

2011-05-30T20:09:34Z

MahshidLightMoon: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep for assessing and correcting DWIs and DTI.

= Description =

DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

As further extended-DTIprep, our experiments show that the residual artifacts presence after DTIPrep QC can be detected and corrected using knowledge of DTI. We introduce new entropy-based measurement of the DTI data from Principal Directions histogram of the entire image. After training our measurement, the QC is applied on assessing DTI information within gray-matter regions, white-matter areas or the entire image. Thus, DTI artifacts are detected if an unusually high-degree of PDs clusters or unusually uniform distribution are found.

[[Image:Screenshot.png|400px|thumb|right|alt Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|alt 3D view of gradients before and after Quality Control procedures]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2011-05-30T19:56:04Z

MahshidLightMoon: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep for assessing and correcting DWIs and DTI.

= Description =

DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

As further extended-DTIprep, our experiments show that the residual artifacts presence after DTIPrep QC can be detected and corrected using knowledge of DTI. We introduce new entropy-based measurement of the DTI data from Principal Directions histogram of the entire image. After training our measurement, the QC is applied on assessing DTI information within gray-matter regions, white-matter areas or the entire image.

[[Image:Screenshot.png|400px|thumb|right|alt Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|alt 3D view of gradients before and after Quality Control procedures]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2011-05-30T19:54:31Z

MahshidLightMoon: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep for assessing and correcting DWIs and DTI.

= Description =

DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

As further extended DTIprep, our experiments show that the residual artifacts presence after DTIPrep QC can be detected and corrected using knowledge of DTI. We introduce new entropy-based measurement of the DTI data from Principal Directions histogram of the entire image. After training our measurement, the QC is applied on assessing DTI information within gray-matter regions, white-matter areas or the entire image.

[[Image:Screenshot.png|400px|thumb|right|alt Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|alt 3D view of gradients before and after Quality Control procedures]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2011-05-30T19:53:21Z

MahshidLightMoon: /* Diffusion Tensor and Diffusion Weighted Imaging Quality Control */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep for assessing and correcting DWIs and DTI.

= Description =

DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipelines steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

As further extended DTIprep, our experiments show that the residual artifacts presence after DTIPrep QC can be detected and corrected using knowledge of DTI. We introduce new entropy-based measurement of the DTI data from Principal Directions histogram of the entire image. After training our measurement, the QC is applied on assessing DTI information within gray-matter regions, white-matter areas or the entire image.

[[Image:Screenshot.png|400px|thumb|right|alt Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|alt 3D view of gradients before and after Quality Control procedures]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2011-05-30T19:52:10Z

MahshidLightMoon:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep.

= Description =

DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipelines steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

As further extended DTIprep, our experiments show that the residual artifacts presence after DTIPrep QC can be detected and corrected using knowledge of DTI. We introduce new entropy-based measurement of the DTI data from Principal Directions histogram of the entire image. After training our measurement, the QC is applied on assessing DTI information within gray-matter regions, white-matter areas or the entire image.

[[Image:Screenshot.png|400px|thumb|right|alt Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|alt 3D view of gradients before and after Quality Control procedures]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2011-05-30T19:47:43Z

MahshidLightMoon:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Diffusion Tensor and Diffusion Weighted Imaging Quality Control =

DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep. DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipelines steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.

As further extended DTIprep, our experiments show that the residual artifacts presence after DTIPrep QC can be detected and corrected using knowledge of DTI. We introduce new entropy-based measurement of the DTI data from Principal Directions histogram of the entire image. After training our measurement, the QC is applied on assessing DTI information within gray-matter regions, white-matter areas or the entire image.

= Description =

[[Image:Screenshot.png|400px|thumb|right|alt Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]]

[[Image:753361684_3.png|400px|thumb|right|alt 3D view of gradients before and after Quality Control procedures]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADTIQualityControl NA-MIC Publication Database on DTI/DWI QC]

= Key Investigators =
* UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
* Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

= Links =
* DTIPrep on [http://www.nitrc.org/projects/dtiprep/ NITRC]

[[Category: Diffusion MRI]]

Projects:DTI DWI QualityControl

2011-05-30T19:26:17Z

MahshidLightMoon:

Projects:DTI DWI QualityControl

2011-05-30T19:22:56Z

MahshidLightMoon: