Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Figure 2: Example of representative lung features ρr . Each column shows a single feature in axial and frontal view.
Figure 2: Example of representative lung features ρr . Each column shows a single feature in axial and frontal view.
Figure 1: Regions of interest (ROI) placement on PET and corresponding magnetic resonance images. The figure shows an example of the ROI placement in gray and white matter regions and in germinal zones on magnetic resonance imaging (upper panel) and coregistration with corresponding PET scans (lower panel). From a total of 24 regions both SUVmean and SUVmax were obtained. SUVs were analyzed longitudinally over time for each patient in all ROI, to include a pre- and postchemotherapy data set. (1,2): right and left cerebellar cortex; (3,4): right and left medial temporal lobe and hippocampus; (5,6): right and left orbitofrontal cortex/olfactory gyrus; (7,8): right and left anterior and posterior subventricular zone; (9,10): right and left caudate nucleus; (11,12): right and left thalamus; (13,14): right and left periventricular white matter; (15): corpus callosum (splenium); (16,17): right and left frontal cortex; (18,19): right and left subcortical white matter; Not shown in this representative image are: right and left parietal cortex, corpus callosum (genu), posterior cingulate, and cerebellar white matter. PET, positron emission tomography; SUV, standardized uptake value.
Figure 1: Regions of interest (ROI) placement on PET and corresponding magnetic resonance images. The figure shows an example of the ROI placement in gray and white matter regions and in germinal zones on magnetic resonance imaging (upper panel) and coregistration with corresponding PET scans (lower panel). From a total of 24 regions both SUVmean and SUVmax were obtained. SUVs were analyzed longitudinally over time for each patient in all ROI, to include a pre- and postchemotherapy data set. (1,2): right and left cerebellar cortex; (3,4): right and left medial temporal lobe and hippocampus; (5,6): right and left orbitofrontal cortex/olfactory gyrus; (7,8): right and left anterior and posterior subventricular zone; (9,10): right and left caudate nucleus; (11,12): right and left thalamus; (13,14): right and left periventricular white matter; (15): corpus callosum (splenium); (16,17): right and left frontal cortex; (18,19): right and left subcortical white matter; Not shown in this representative image are: right and left parietal cortex, corpus callosum (genu), posterior cingulate, and cerebellar white matter. PET, positron emission tomography; SUV, standardized uptake value.
Figure 5: The simple DICOM image viewer in Tab 4 of the client lets users view downloaded studies. Selected studies are organized on top left by patient ID, study UID, series UID, and image ID (4a). The bottom left panel shows a list of studies already downloaded to local machine (4b)
Figure 5: The simple DICOM image viewer in Tab 4 of the client lets users view downloaded studies. Selected studies are organized on top left by patient ID, study UID, series UID, and image ID (4a). The bottom left panel shows a list of studies already downloaded to local machine (4b)
Figure 1: Fixed (red) and moving (blue) images and the overlay of both grids after transformation (middle). The interpolation uncertainty varies across the resampled image due to different distances to neighboring points on the moving grid. Arrows point to two exemplary locations on the fixed grid where neighbors from the moving grid are close and far, respectively (right).
Figure 1: Fixed (red) and moving (blue) images and the overlay of both grids after transformation (middle). The interpolation uncertainty varies across the resampled image due to different distances to neighboring points on the moving grid. Arrows point to two exemplary locations on the fixed grid where neighbors from the moving grid are close and far, respectively (right).
Figure 3: Screenshot shows intraprocedural visualization of imaging for needle position confirmation for transperineal prostate biopsy in a 71-year-old man with no rectal access and increasing PSA (31.09 ng/dL [31.09 μg/L]). Targeted biopsy yielded Gleason 4+3 PCa in right anterior central gland. Image shows 3D Slicer targeting module during biopsy with needle confirmation image loaded. Left: control panel. Top middle: axial T2-weighted MR image. Top right: three-dimensional view, including calculated needle path (arrow). Bottom middle: sagittal T2-weighted MR image shows target and needle tract. Bottom right: coronal T2-weighted MR image, with target and needle track. RGC = right central gland.
Figure 3: Screenshot shows intraprocedural visualization of imaging for needle position confirmation for transperineal prostate biopsy in a 71-year-old man with no rectal access and increasing PSA (31.09 ng/dL [31.09 μg/L]). Targeted biopsy yielded Gleason 4+3 PCa in right anterior central gland. Image shows 3D Slicer targeting module during biopsy with needle confirmation image loaded. Left: control panel. Top middle: axial T2-weighted MR image. Top right: three-dimensional view, including calculated needle path (arrow). Bottom middle: sagittal T2-weighted MR image shows target and needle tract. Bottom right: coronal T2-weighted MR image, with target and needle track. RGC = right central gland.
Figure 1: Comparison between small deformation and diffeomorphic registrations with equal parameter values, the uncertainty is represented by the Frobenius norm of the covariance. The small deformation model has a smaller variance in general at the expense of a possibly invalid deformation field away from the landmarks.
Figure 1: Comparison between small deformation and diffeomorphic registrations with equal parameter values, the uncertainty is represented by the Frobenius norm of the covariance. The small deformation model has a smaller variance in general at the expense of a possibly invalid deformation field away from the landmarks.
Figure 1: Schematic diagram depicting the overview of the analysis. A: First, we performed five manual delineations and six 3D-Slicer segmentations (three observers twice) on twenty lung tumors. B: Second, fifty-six radiomic features quantifying tumor intensity, texture and shape were extracted from these segmentations. C: Third, the resulting feature matrices were compared for robustness of the feature values.
Figure 1: Schematic diagram depicting the overview of the analysis. A: First, we performed five manual delineations and six 3D-Slicer segmentations (three observers twice) on twenty lung tumors. B: Second, fifty-six radiomic features quantifying tumor intensity, texture and shape were extracted from these segmentations. C: Third, the resulting feature matrices were compared for robustness of the feature values.
Figure 2: (A) Aggregate image of glioblastomas generated from the sum of binary tumor masks; (B) Region in where patients were significantly more likely to present without TAS (p<0.05)
Figure 2: (A) Aggregate image of glioblastomas generated from the sum of binary tumor masks; (B) Region in where patients were significantly more likely to present without TAS (p<0.05)
Figure 4: Human data. Left: Airway tree with four generations colored: 1: Red; 2: Yellow; 3: Green; 4: Cyan; 5: Blue. Center: Airways of 10 different subjects affinely registered. Right: The same airways registered with our deformable tensor field approach.
Figure 4: Human data. Left: Airway tree with four generations colored: 1: Red; 2: Yellow; 3: Green; 4: Cyan; 5: Blue. Center: Airways of 10 different subjects affinely registered. Right: The same airways registered with our deformable tensor field approach.
Figure 2: FE model generation; (a) segmented MR slice with thorax and lung shapes highlighted and (b) extruded ‘2.5D’ FE model generated in EIDORS.
Figure 2: FE model generation; (a) segmented MR slice with thorax and lung shapes highlighted and (b) extruded ‘2.5D’ FE model generated in EIDORS.
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