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Figure 3: Visualization of anatomical features in MRI. (a) Volume rendering of a difference image computed from a pre- and post-gadolinium contrast scan. Brighter areas indicate a concentration of gadolinium, emphasizing the vasculature. (b) Time-lapse imaging of a subpopulation with Alzheimer’s disease showing loss of cortical gray matter density at 0, 6, 12 and 18 months80. Blue, no significant difference in cortical thickness from elderly control subjects; red and white, significant differences in cortical thickness (image courtesy P. Thompson). (c) Cardiac MRI analysis using anatomical scans and DTI of an ex vivo rat heart81. Color encoding of the DTI indicates the direction of the primary eigenvector: x direction, green; y, red; z, blue. (d) Visualization of a human brain DTI field during a fluid deformation process for image registration82. Orientation and shape of each ellipsoid indicate the pattern of diffusion at that location. Color encoding: low diffusion, green, to high diffusion, red. (e) Interactive visualization of high angular resolution diffusion imaging (HARDI) data using spherical harmonics27. Each shape represents the orientation distribution function measured at that point, which indicates the probability of diffusion in each angular direction. Colors indicate direction of maximum probability: red, lateral; blue, inferior–superior; green, anterior–posterior. Visible in this frame are portions of corpus callosum (central red area) and corticospinal tracts (blue vertical areas near edges). (f) White matter tracts computed from diffusion spectral imaging (DSI) data using Diffusion Toolkit (http://www.trackvis.org/dtk/). The tracts were then clustered automatically into bundles based on shape similarity measures and finally rendered using BrainSuite27. Each color indicates a different bundle. (g) 3D orthogonal views of an MRI volume, displayed with an automatically extracted surface mesh model of the surface of the cerebral cortex (BrainSuite27). (h) 3D surface reconstructions (Amira) from micro-MRI data: left hindlimb of a mouse with peroneal muscular atrophy. (i) Surgical planning visualization for assessment of white matter integrity: tumor model (green mass), ventricles (blue), local diffusion for one slice plane (ellipsoid scale and orientation indicating local diffusion tensor: red, low anisotropy; blue, high) and white matter fiber tracts shaded red to blue with increasing local anisotropy (thin lines, peri-tumoral; thick lines, corticospinal tracts). 3D Slicer: http://wiki.na-mic.org/Wiki/index.php/IGT:ToolKit/Neurosurgical-Planning.
Figure 3: Visualization of anatomical features in MRI. (a) Volume rendering of a difference image computed from a pre- and post-gadolinium contrast scan. Brighter areas indicate a concentration of gadolinium, emphasizing the vasculature. (b) Time-lapse imaging of a subpopulation with Alzheimer’s disease showing loss of cortical gray matter density at 0, 6, 12 and 18 months80. Blue, no significant difference in cortical thickness from elderly control subjects; red and white, significant differences in cortical thickness (image courtesy P. Thompson). (c) Cardiac MRI analysis using anatomical scans and DTI of an ex vivo rat heart81. Color encoding of the DTI indicates the direction of the primary eigenvector: x direction, green; y, red; z, blue. (d) Visualization of a human brain DTI field during a fluid deformation process for image registration82. Orientation and shape of each ellipsoid indicate the pattern of diffusion at that location. Color encoding: low diffusion, green, to high diffusion, red. (e) Interactive visualization of high angular resolution diffusion imaging (HARDI) data using spherical harmonics27. Each shape represents the orientation distribution function measured at that point, which indicates the probability of diffusion in each angular direction. Colors indicate direction of maximum probability: red, lateral; blue, inferior–superior; green, anterior–posterior. Visible in this frame are portions of corpus callosum (central red area) and corticospinal tracts (blue vertical areas near edges). (f) White matter tracts computed from diffusion spectral imaging (DSI) data using Diffusion Toolkit (http://www.trackvis.org/dtk/). The tracts were then clustered automatically into bundles based on shape similarity measures and finally rendered using BrainSuite27. Each color indicates a different bundle. (g) 3D orthogonal views of an MRI volume, displayed with an automatically extracted surface mesh model of the surface of the cerebral cortex (BrainSuite27). (h) 3D surface reconstructions (Amira) from micro-MRI data: left hindlimb of a mouse with peroneal muscular atrophy. (i) Surgical planning visualization for assessment of white matter integrity: tumor model (green mass), ventricles (blue), local diffusion for one slice plane (ellipsoid scale and orientation indicating local diffusion tensor: red, low anisotropy; blue, high) and white matter fiber tracts shaded red to blue with increasing local anisotropy (thin lines, peri-tumoral; thick lines, corticospinal tracts). 3D Slicer: http://wiki.na-mic.org/Wiki/index.php/IGT:ToolKit/Neurosurgical-Planning.
Figure 5: Software systems perspective.
Figure 5: Software systems perspective.
Figure 5: The 3D Slicer visualizes the physical relationship among the model of Z-frame, the 2D image intersecting the Z-frame, and the slice of the preoperative 3D image after the Z-frame registration. This helps the users to confirm that the Z-frame registration is performed correctly.
Figure 5: The 3D Slicer visualizes the physical relationship among the model of Z-frame, the 2D image intersecting the Z-frame, and the slice of the preoperative 3D image after the Z-frame registration. This helps the users to confirm that the Z-frame registration is performed correctly.
Figure 2: a,b. In the checkerboard display between the preoperative image (before deformation) and intraoperative image, a black line indicates a contour of the total gland, whereas a white line indicates a contour of the central gland. a: The contours showed discrepancy at white arrows. b: In a checkerboard display between deformed preoperative and intraoperative images, the contours match closely after registration has been performed. A slight discrepancy remained at the posterior part of the prostate (black arrow).
Figure 2: a,b. In the checkerboard display between the preoperative image (before deformation) and intraoperative image, a black line indicates a contour of the total gland, whereas a white line indicates a contour of the central gland. a: The contours showed discrepancy at white arrows. b: In a checkerboard display between deformed preoperative and intraoperative images, the contours match closely after registration has been performed. A slight discrepancy remained at the posterior part of the prostate (black arrow).
Figure 4: Effects of age on signal intensities sampled at different distances from the WM surface. Statistical p maps showing the results from GLMs testing the effect of age on signal intensity mapped at six different distances from the white surface. Cold colors indicate attenuated signal with advancing age while warm color indicate the opposite relation. The statistical maps were thresholded at p b 10− 6 to allow for visual inspection of regional variability over measures. Mean ventricle CSF intensity for each subject was included in the models to account for scanner related noise.
Figure 4: Effects of age on signal intensities sampled at different distances from the WM surface. Statistical p maps showing the results from GLMs testing the effect of age on signal intensity mapped at six different distances from the white surface. Cold colors indicate attenuated signal with advancing age while warm color indicate the opposite relation. The statistical maps were thresholded at p b 10− 6 to allow for visual inspection of regional variability over measures. Mean ventricle CSF intensity for each subject was included in the models to account for scanner related noise.
Figure: New Expectation Maximization Segmentation Pipeline in Slicer3. We redesigned the user interface of the EMSegment module in Slicer3 and developed a new module called MRIBiasFieldCorrection. Experiments with the SPL Brain Atlas showed improved segmentation accuracy.
Figure: New Expectation Maximization Segmentation Pipeline in Slicer3. We redesigned the user interface of the EMSegment module in Slicer3 and developed a new module called MRIBiasFieldCorrection. Experiments with the SPL Brain Atlas showed improved segmentation accuracy.
Figure 1: Magnified view of the septal wall with the VIBGYOR color map representing the Global Isotropy Index (GII) at various locations. Red represents points with low GII while blue represents points with high GII.
Figure 1: Magnified view of the septal wall with the VIBGYOR color map representing the Global Isotropy Index (GII) at various locations. Red represents points with low GII while blue represents points with high GII.
Figure 2: (a) Pre-procedure contrast enhanced MR image, (b) CT image for interventional planning, (c) registered and fused image showing liver from MR image overlaid on CT image, (d) CT image showing ablation probes overlaid with segmented tumor from the registered MR image, (e) CT image showing iceball (hypointense area around lesion) overlaid with segmented lesion (green) from registered MR image, and (f) 3D representation of liver (red), tumor (green), iceball (blue) and cryoprobes to evaluate tumor coverage and margins.
Figure 2: (a) Pre-procedure contrast enhanced MR image, (b) CT image for interventional planning, (c) registered and fused image showing liver from MR image overlaid on CT image, (d) CT image showing ablation probes overlaid with segmented tumor from the registered MR image, (e) CT image showing iceball (hypointense area around lesion) overlaid with segmented lesion (green) from registered MR image, and (f) 3D representation of liver (red), tumor (green), iceball (blue) and cryoprobes to evaluate tumor coverage and margins.
Figure 1: Whole-brain segmentation. The figure shows the segmentation results from the average brain of Sample 2. The three-dimensional renderings illustrate the shape, extension, and relative position within the brain of the different neuroanatomical structures.
Figure 1: Whole-brain segmentation. The figure shows the segmentation results from the average brain of Sample 2. The three-dimensional renderings illustrate the shape, extension, and relative position within the brain of the different neuroanatomical structures.
Figure 5: Immediately after ablation therapy, MRI was performed again for evaluation of the ablated area. Red: tumor, green: ablated area.
Figure 5: Immediately after ablation therapy, MRI was performed again for evaluation of the ablated area. Red: tumor, green: ablated area.
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