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

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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: 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 1: Result of Grow Cut Segmentation of the choroid plexus (purple) and cerebrum (white) of fetal brain at 14 weeks of gestation
Figure 1: Result of Grow Cut Segmentation of the choroid plexus (purple) and cerebrum (white) of fetal brain at 14 weeks of gestation
Figure 4: Visual Comparison of Scan A in Table 2. Tissue classification results from three applications, BRAINSABC, Atropos of ANTS Tools, and FreeSurfer on top of T1- and T2-weighted images, multi-modal repeated scan processing. This subject present relatively normal tissue ratio, reflecting minimal, if exists, atrophy in brain tissues. Red and blue boxes highlight where tissue classification is more differentiated from each other.
Figure 4: Visual Comparison of Scan A in Table 2. Tissue classification results from three applications, BRAINSABC, Atropos of ANTS Tools, and FreeSurfer on top of T1- and T2-weighted images, multi-modal repeated scan processing. This subject present relatively normal tissue ratio, reflecting minimal, if exists, atrophy in brain tissues. Red and blue boxes highlight where tissue classification is more differentiated from each other.
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).
Figure 2: The procedure for data pre-processing and feature extraction.
Figure 2: The procedure for data pre-processing and feature extraction.
Figure 2: Different sources of the error contributing to the MRI-guided robotic system: 1, Z-frame registration error; 2, robot-to-Z-frame registration error; 3, manipulator error; and 4, artifact shift.
Figure 2: Different sources of the error contributing to the MRI-guided robotic system: 1, Z-frame registration error; 2, robot-to-Z-frame registration error; 3, manipulator error; and 4, artifact shift.
Figure 1: Photographic, Baseline (b=0) and fractional anisotropy (FA) images of a spherical phantom, where two fiber bundles cross at 45 degree angle.
Figure 1: Photographic, Baseline (b=0) and fractional anisotropy (FA) images of a spherical phantom, where two fiber bundles cross at 45 degree angle.
Figure 2: Two views of the CST. Red - fibers obtained with dense sampling (N = 120) and white - fibers with sparse sampling (N = 32). Note that, despite the sparse sampling the later method traces similar regions of the brain. Overlap measure B<sub>32</sub> = 0.9.
Figure 2: Two views of the CST. Red - fibers obtained with dense sampling (N = 120) and white - fibers with sparse sampling (N = 32). Note that, despite the sparse sampling the later method traces similar regions of the brain. Overlap measure B32 = 0.9.
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