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

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Figure 1: Structure of the liquid crystal phantom. a: Chemical structure of the detergent sodium dioctyl sulfosuccinate, the hydrocarbon isooctane, and water. b: Self‐assembled nanostructure with AOT (white sodium ions, red hydrophilic head groups, yellow hydrophobic tails) in the interface between a continuous matrix of isooctane (green) and hexagonally packed cylindrical rods of water (blue). The structure was generated with the molecular dynamics simulation package GROMACS 122 and rendered with POV‐Ray 123. c: Schematic microstructure with randomly oriented 100‐μm‐scale anisotropic domains color‐coded according to the direction of the six‐fold symmetry axis as [x, y, z] = [red, green, blue]. d: Photo of the phantom comprising the transparent liquid crystal in a 0.5‐L round‐bottom glass flask on a cork stand in front of the wall logo at the reception of the Athinoula A. Martinos Center at Massachusetts General Hospital 97.
Figure 1: Structure of the liquid crystal phantom. a: Chemical structure of the detergent sodium dioctyl sulfosuccinate, the hydrocarbon isooctane, and water. b: Self‐assembled nanostructure with AOT (white sodium ions, red hydrophilic head groups, yellow hydrophobic tails) in the interface between a continuous matrix of isooctane (green) and hexagonally packed cylindrical rods of water (blue). The structure was generated with the molecular dynamics simulation package GROMACS 122 and rendered with POV‐Ray 123. c: Schematic microstructure with randomly oriented 100‐μm‐scale anisotropic domains color‐coded according to the direction of the six‐fold symmetry axis as [x, y, z] = [red, green, blue]. d: Photo of the phantom comprising the transparent liquid crystal in a 0.5‐L round‐bottom glass flask on a cork stand in front of the wall logo at the reception of the Athinoula A. Martinos Center at Massachusetts General Hospital 97.
Figure 1: Widespread significant reductions in FA (A) appear to considerably overlap with significant global increases in FW (B). Only limited reductions in FA-t are observed in first-episode patients (C). No differences between medicated and medication- naïve patients were found for FA, FW, or FA-t.
Figure 1: Widespread significant reductions in FA (A) appear to considerably overlap with significant global increases in FW (B). Only limited reductions in FA-t are observed in first-episode patients (C). No differences between medicated and medication- naïve patients were found for FA, FW, or FA-t.
Figure 1: Overview of synthetic data set. The top row summarizes the phantom generation process. The simulated images are generated from 25 major bundles, which are shown in the bottom part of the figure. These were manually segmented from a whole-brain tractogram of a HCP subject and include the CC, cingulum (Cg), fornix (Fx), anterior commissure (CA), optic radiation (OR), posterior commissure (CP), inferior cerebellar peduncle (ICP), middle cerebellar peduncle (MCP), superior cerebellar peduncle (SCP), parieto-occipital pontine tract (POPT), cortico-spinal tract (CST), frontopontine tracts (FPT), ILF, UF, and SLF. The connectivity plot in the middle shows the phantom design. The segment positions correspond to the involved endpoint region (from top to bottom: frontal lobe, temporal lobe, parietal lobe, occipital lobe, subcortical region, cerebellum, brain stem). The radial segment length and the connection number in the plot are chosen according to the volume of the respective bundle endpoint region. Abbreviations: right (R) and left (L) hemisphere, head (H) and tail (T) of each respective bundle.
Figure 1: Overview of synthetic data set. The top row summarizes the phantom generation process. The simulated images are generated from 25 major bundles, which are shown in the bottom part of the figure. These were manually segmented from a whole-brain tractogram of a HCP subject and include the CC, cingulum (Cg), fornix (Fx), anterior commissure (CA), optic radiation (OR), posterior commissure (CP), inferior cerebellar peduncle (ICP), middle cerebellar peduncle (MCP), superior cerebellar peduncle (SCP), parieto-occipital pontine tract (POPT), cortico-spinal tract (CST), frontopontine tracts (FPT), ILF, UF, and SLF. The connectivity plot in the middle shows the phantom design. The segment positions correspond to the involved endpoint region (from top to bottom: frontal lobe, temporal lobe, parietal lobe, occipital lobe, subcortical region, cerebellum, brain stem). The radial segment length and the connection number in the plot are chosen according to the volume of the respective bundle endpoint region. Abbreviations: right (R) and left (L) hemisphere, head (H) and tail (T) of each respective bundle.
Figure 1: Example visualization of a neurosurgical case using SlicerDMRI, with tracts from the UKF two-tensor tractography method. The patient presented with a history of right side paresthesia and aphasia, leading to the diagnosis of a left insular glioblastoma. A, The corticospinal tract (blue) wraps around the medial and superior aspect of the lesion and the inferior fronto-occipital fasciculus (pastel green) is relatively close to the inferior pole of the tumor. B, The arcuate fasciculus (green) fibers spread along the superior surface of the tumor, lateral to the corticospinal tract, and the uncinate fasciculus (white) is distant from the lesion.
Figure 1: Example visualization of a neurosurgical case using SlicerDMRI, with tracts from the UKF two-tensor tractography method. The patient presented with a history of right side paresthesia and aphasia, leading to the diagnosis of a left insular glioblastoma. A, The corticospinal tract (blue) wraps around the medial and superior aspect of the lesion and the inferior fronto-occipital fasciculus (pastel green) is relatively close to the inferior pole of the tumor. B, The arcuate fasciculus (green) fibers spread along the superior surface of the tumor, lateral to the corticospinal tract, and the uncinate fasciculus (white) is distant from the lesion.
Figure 1: Study-specific data-driven WM parcellation. (a) Tractography alignment using groupwise tractography registration, where color indicates subject. (b) Study-specific groupwise WM parcellation (atlas) (left) and example subject-specific tractography parcellation (right). Fibers from different parcels are colored differently, where similar colors represent white-matter-anatomy-similar fibers. (c) Example atlas WM tract parcels (left) and the corresponding subject-specific WM tract parcels (right). (d) Example neighbors of the red tract parcel include the yellow and the blue parcels but not the more distant green one.
Figure 1: Study-specific data-driven WM parcellation. (a) Tractography alignment using groupwise tractography registration, where color indicates subject. (b) Study-specific groupwise WM parcellation (atlas) (left) and example subject-specific tractography parcellation (right). Fibers from different parcels are colored differently, where similar colors represent white-matter-anatomy-similar fibers. (c) Example atlas WM tract parcels (left) and the corresponding subject-specific WM tract parcels (right). (d) Example neighbors of the red tract parcel include the yellow and the blue parcels but not the more distant green one.
Figure 3: Examples of the brain WMH MRI segmentations. (a) Axial T1-weighted image. (b) FLAIR image. (c-f) FLAIR images with WMH segmented labels: (c) reference (green) WMH. (d) WMH (red) from a domain adapted model (fST (.)) fine-tuned on five target training samples. (e) WMH (yellow) from model trained from scratch on 100 target training samples. (f) WMH (orange) from model trained from scratch (fT (.)) on 5 target training samples.
Figure 3: Examples of the brain WMH MRI segmentations. (a) Axial T1-weighted image. (b) FLAIR image. (c-f) FLAIR images with WMH segmented labels: (c) reference (green) WMH. (d) WMH (red) from a domain adapted model (fST (.)) fine-tuned on five target training samples. (e) WMH (yellow) from model trained from scratch on 100 target training samples. (f) WMH (orange) from model trained from scratch (fT (.)) on 5 target training samples.
Figure 5: Results with the gluteus medius muscle of Visible Korean Human dataset. Estimated from (a) optical image, (b) CT, and (c) CT with 11 interactively provided fibers shown in the left most column.
Figure 5: Results with the gluteus medius muscle of Visible Korean Human dataset. Estimated from (a) optical image, (b) CT, and (c) CT with 11 interactively provided fibers shown in the left most column.
Figure 4: Illustration of the mean-squared displacement in two synthetic tissue structures: (a) and (b) are the synthetic cross-sectional planes of parallel axonal bundles with red areas being the axons, green the myelin, and blue the extra-axonal space. c: The mean-squared displacement of simulated diffusing particles in the extra-axonal space of the two tissue structures.
Figure 4: Illustration of the mean-squared displacement in two synthetic tissue structures: (a) and (b) are the synthetic cross-sectional planes of parallel axonal bundles with red areas being the axons, green the myelin, and blue the extra-axonal space. c: The mean-squared displacement of simulated diffusing particles in the extra-axonal space of the two tissue structures.
Figure 6: Recovery of cylinder diameters (d). Red lines show the 80% confidence interval. The resolution limit is shown as the blue vertical line, at dmin=3.3μm. The data represent a case with parallel cylinders and SDE encoding with δ=Δ=40ms
Figure 6: Recovery of cylinder diameters (d). Red lines show the 80% confidence interval. The resolution limit is shown as the blue vertical line, at dmin=3.3μm. The data represent a case with parallel cylinders and SDE encoding with δ=Δ=40ms
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