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: 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 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 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: 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 1: Illustrating inter-slice correspondences via scale-invariant feature (green circles) from a new US slice (upper right) to multiple US slices in a prior sequence (left). A graph of 3D ISC error vs. ISC inlier count (lower right) reveals low error for inlier counts greater than 4, with the exception of a small number of 3D outliers.
Figure 1: Illustrating inter-slice correspondences via scale-invariant feature (green circles) from a new US slice (upper right) to multiple US slices in a prior sequence (left). A graph of 3D ISC error vs. ISC inlier count (lower right) reveals low error for inlier counts greater than 4, with the exception of a small number of 3D outliers.
Figure 2: The procedure for data pre-processing and feature extraction.
Figure 2: The procedure for data pre-processing and feature extraction.
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: 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.
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