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

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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 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.
Figure 2: fMRI activation maps and fMRI-guided ensemble tracts for a) HC, b) aMCI and c) AD. From left to right, PANEL 1: second level fMRI group activation map resulting from one sample t-tests performed separately for each group, verbal fluency task, axial views; PANELS 2/4: anterior (A), left lateral (L) and right lateral (R) views of the ensemble tract based on the activation of the first panel. The yellow circles highlight the left CB (anterior view) and the left AF (left view), observable with different portions in all the three groups.
Figure 2: fMRI activation maps and fMRI-guided ensemble tracts for a) HC, b) aMCI and c) AD. From left to right, PANEL 1: second level fMRI group activation map resulting from one sample t-tests performed separately for each group, verbal fluency task, axial views; PANELS 2/4: anterior (A), left lateral (L) and right lateral (R) views of the ensemble tract based on the activation of the first panel. The yellow circles highlight the left CB (anterior view) and the left AF (left view), observable with different portions in all the three groups.
Figure 8: V1 and V2 single slice tumor K trans parametric maps generated by all 12 algorithms for a non-pCR (A) and a pCR (B). The primary tumor was in the left breast of the non-pCR and the right breast of the pCR. The color K trans maps are overlaid on post-contrast or pre-contrast DCE images. Although the color scales for these maps are different between subjects and among algorithms, they are kept the same for V1 and V2 maps generated by the same algorithm for the same subject.
Figure 8: V1 and V2 single slice tumor K trans parametric maps generated by all 12 algorithms for a non-pCR (A) and a pCR (B). The primary tumor was in the left breast of the non-pCR and the right breast of the pCR. The color K trans maps are overlaid on post-contrast or pre-contrast DCE images. Although the color scales for these maps are different between subjects and among algorithms, they are kept the same for V1 and V2 maps generated by the same algorithm for the same subject.
Figure 5: A plot of procedure time showing measured durations of the Smart Template preparation, which usually happens before patient arrives, the patient preparation where patient is located on the intervention setup, the imaging required for localizer, Z-frame scan for registration, T2 scan for intra-procedural prostate image, and other multiparametric scans for further target assurance, and the biopsy that consists of needle placement and confirmation imaging of each target.
Figure 5: A plot of procedure time showing measured durations of the Smart Template preparation, which usually happens before patient arrives, the patient preparation where patient is located on the intervention setup, the imaging required for localizer, Z-frame scan for registration, T2 scan for intra-procedural prostate image, and other multiparametric scans for further target assurance, and the biopsy that consists of needle placement and confirmation imaging of each target.
Figure 1: Assessment of total and regional T1 lesion volume in patients with multiple sclerosis. A. T1 hypointense lesions are shown on a representative grayscale MPRAGE image (white arrows). B. Binary lesion map generated from outlined T1 hypointense lesions visualized in green. C. WM fiber tract atlas registered into native space of the MPRAGE image. D. Labeled lesion map derived from the binary lesion map after parcellation of the lesions with the use of the WM fiber tract atlas. The labeled lesion map was used to measure regional T1 white matter lesion volumes within each individual fiber bundle.
Figure 1: Assessment of total and regional T1 lesion volume in patients with multiple sclerosis. A. T1 hypointense lesions are shown on a representative grayscale MPRAGE image (white arrows). B. Binary lesion map generated from outlined T1 hypointense lesions visualized in green. C. WM fiber tract atlas registered into native space of the MPRAGE image. D. Labeled lesion map derived from the binary lesion map after parcellation of the lesions with the use of the WM fiber tract atlas. The labeled lesion map was used to measure regional T1 white matter lesion volumes within each individual fiber bundle.
Figure 1: Segmentation uncertainty. Left: representative example showing differences in CT/PET manual delineations (top) and 3D-Slicer segmentations (bottom). Right: This variability is quantified with the uncertainty region, defined as the difference between the observers’ agreement and observers’ union (highlighted in green). The smaller the uncertainty region is, the lower the variability among multiple contours.
Figure 1: Segmentation uncertainty. Left: representative example showing differences in CT/PET manual delineations (top) and 3D-Slicer segmentations (bottom). Right: This variability is quantified with the uncertainty region, defined as the difference between the observers’ agreement and observers’ union (highlighted in green). The smaller the uncertainty region is, the lower the variability among multiple contours.
Figure 7: Targeting accuracy evaluation setup in the 3T clinical MRI scanner, showing the motorized needle guide template.
Figure 7: Targeting accuracy evaluation setup in the 3T clinical MRI scanner, showing the motorized needle guide template.
Figure 2: Result of ‘Grow Cut Segmentation’ of the fetal brain using the 3D Slicer. The blue structure represents the choroid plexus, and the yellow structure the cerebrum at 14 weeks of gestation (axial and coronal views).
Figure 2: Result of ‘Grow Cut Segmentation’ of the fetal brain using the 3D Slicer. The blue structure represents the choroid plexus, and the yellow structure the cerebrum at 14 weeks of gestation (axial and coronal views).
Figure 1: In one popular approach to fiber clustering, a spectral embedding is used to color (left) and to cluster fibers (right). These images are example results from a multiple subject clustering method for atlas creation (O'Donnell and Westin, 2007).
Figure 1: In one popular approach to fiber clustering, a spectral embedding is used to color (left) and to cluster fibers (right). These images are example results from a multiple subject clustering method for atlas creation (O'Donnell and Westin, 2007).
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