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

Surgical Planning Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Details view | Download Statistics | Help | Import | Log in
Collection:
Items displayed:

Gallery of images uploaded to the Publication Database.
685 images found.

PAGE 4 of 69
Figure 8: The mean localization errors across all 32 subjects for atlas-guided and subject MRI-guided DOI as a function of cortical position. The gray color indicates no assigned value due to a sparsity of simulated activations in those areas.
Figure 8: The mean localization errors across all 32 subjects for atlas-guided and subject MRI-guided DOI as a function of cortical position. The gray color indicates no assigned value due to a sparsity of simulated activations in those areas.
Figure 2: ROIs used for extraction of anterior, as well as posterior limbs of the internal capsule (the pink fiber bundle demonstrates the fronto-thalamic fibers passing through the anterior limb of the internal capsule)
Figure 2: ROIs used for extraction of anterior, as well as posterior limbs of the internal capsule (the pink fiber bundle demonstrates the fronto-thalamic fibers passing through the anterior limb of the internal capsule)
Figure 5-1: The baby brain data set of 22 T1-weighted MRI volumes. Central coronal slices of the input images were obtained at the initial, misaligned position.
Figure 5-1: The baby brain data set of 22 T1-weighted MRI volumes. Central coronal slices of the input images were obtained at the initial, misaligned position.
Figure 8: Multiple object RSS. (a–c) All the seeded labels drawn for this test. (e–g) Segmentation results in axial, sagittal, and coronal views. (d and h) 3D views from superior and left.
Figure 8: Multiple object RSS. (a–c) All the seeded labels drawn for this test. (e–g) Segmentation results in axial, sagittal, and coronal views. (d and h) 3D views from superior and left.
Figure 6: Single slice through the prostate demonstrating tumor (outlined in red) and normal tissue (outlined in yellow) as defined by both T2 WI (top row, left) and ADC map (bottom row, left). Ktrans values were estimated in these outlined areas of tumor and normal tissue, using T1 maps obtained by six-TR VTR (top row, middle), muscle-adjusted VFA (top row, right), three-TR VTR (bottom row, middle) and using a set T1 value of 1597 ms. Ktrans values obtained are outlined in Table 2.
Figure 6: Single slice through the prostate demonstrating tumor (outlined in red) and normal tissue (outlined in yellow) as defined by both T2 WI (top row, left) and ADC map (bottom row, left). Ktrans values were estimated in these outlined areas of tumor and normal tissue, using T1 maps obtained by six-TR VTR (top row, middle), muscle-adjusted VFA (top row, right), three-TR VTR (bottom row, middle) and using a set T1 value of 1597 ms. Ktrans values obtained are outlined in Table 2.
Figure 10: An example biopsy needle position confirmation image during prostate biopsy procedure using the APT robot. Although needle void is found in all images, the artifact study suggests that the true needle tip position is located at the first image plane and needle voids in the last three (from right) are only extended artifacts. Hence, clinicians can confirm whether the needle is placed at the designated target by checking the fourth needle void image from the last (in case of 3 mm image slice thickness).
Figure 10: An example biopsy needle position confirmation image during prostate biopsy procedure using the APT robot. Although needle void is found in all images, the artifact study suggests that the true needle tip position is located at the first image plane and needle voids in the last three (from right) are only extended artifacts. Hence, clinicians can confirm whether the needle is placed at the designated target by checking the fourth needle void image from the last (in case of 3 mm image slice thickness).
Figure 2: Diffeomorphic mapping (φ) of an ontologically labeled atlas (left, I0) onto the ES average template (right, I1) with the region of significant tissue volume expansion colored.
Figure 2: Diffeomorphic mapping (φ) of an ontologically labeled atlas (left, I0) onto the ES average template (right, I1) with the region of significant tissue volume expansion colored.
Figure 7: Diffusion MRI data for neurosurgical planning. The tractography region of interest (ROI) is a box placed around the tumor (in green) in the frontal lobe. The ROI is also visualized with rectangles in the slice views below. Tracts are then created based on the principal diffusion directions, which are color-coded (bottom). Diffusion ellipsoids are shown along the tract to visualize the shape of the local diffusion.
Figure 7: Diffusion MRI data for neurosurgical planning. The tractography region of interest (ROI) is a box placed around the tumor (in green) in the frontal lobe. The ROI is also visualized with rectangles in the slice views below. Tracts are then created based on the principal diffusion directions, which are color-coded (bottom). Diffusion ellipsoids are shown along the tract to visualize the shape of the local diffusion.
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.
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: The configuration of the robot system for MRI-guided prostate interventions.
Figure 2: The configuration of the robot system for MRI-guided prostate interventions.
PAGE 4 of 69 1 2 3 4 5 6 7 ...69