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Figure 3: This figure illustrates how the National Alliance for Medical Image Computing (NA-MIC) kit, which contains a modular set of interoperable free open source software packages, is managed under a collaborative, high quality software engineering methodology. The kit itself consists of the 3D slicer application software, a number of tools and toolkits such as Visualization Tool kit (VTK) and Insight Segmentation and Registration Tool kit (ITK), and a software engineering methodology that enables multiplatform implementations. DBP, driving biological projects.
Figure 3: This figure illustrates how the National Alliance for Medical Image Computing (NA-MIC) kit, which contains a modular set of interoperable free open source software packages, is managed under a collaborative, high quality software engineering methodology. The kit itself consists of the 3D slicer application software, a number of tools and toolkits such as Visualization Tool kit (VTK) and Insight Segmentation and Registration Tool kit (ITK), and a software engineering methodology that enables multiplatform implementations. DBP, driving biological projects.
Improving Patient Care with AMIGO, theAdvanced Multimodality Image Guided Operating Suite.
Improving Patient Care with AMIGO, theAdvanced Multimodality Image Guided Operating Suite.
Picture of an elastic polymer tail.
Picture of an elastic polymer tail.
Figure 2: RT planning data. In the two leftmost images, critical structures (Blue=PTV70, Red=Pharyngeal constrictors, Orange=Left submandibular gland, Green=Left parotid gland, Purple=Spinal cord) used in planning of the radiation dose are overlaid on axial and sagittal slices of the planning CT. In the two rightmost images, corresponding structures are overlaid on axial and sagittal slices of the planned dose r. Notice the steep dose gradients between the spinal cord, pharyngeal constrictor and PTV70.
Figure 2: RT planning data. In the two leftmost images, critical structures (Blue=PTV70, Red=Pharyngeal constrictors, Orange=Left submandibular gland, Green=Left parotid gland, Purple=Spinal cord) used in planning of the radiation dose are overlaid on axial and sagittal slices of the planning CT. In the two rightmost images, corresponding structures are overlaid on axial and sagittal slices of the planned dose r. Notice the steep dose gradients between the spinal cord, pharyngeal constrictor and PTV70.
Figure 1: Tract density map for the fornix and inferio-longitudinal fasiculus. The density scales from blue (maximum density) to red (minimum density).
Figure 1: Tract density map for the fornix and inferio-longitudinal fasiculus. The density scales from blue (maximum density) to red (minimum density).
Figure 3: Fibrotic tissue. The elasticity and uncertainty maps are color coded using the rainbow colormap in Fig. 1 (red is soft, purple is stiff) (c) and the window level in parentheses. (a) A coronal slice of the inspiration dataset where the upper and lower part is identified as fibrotic by a physician. (b) Notice how the fibrotic areas in the lower and upper lung appear stiffer than the surrounding tissue, and also that the cyst in the lower left of the image is estimated as “soft” which could mean that it is ventilated and therefore collapses during expiration. (c) The IQR of the estimated elasticity map. The uncertainty is somewhat larger in the stiffer fibrotic area.
Figure 3: Fibrotic tissue. The elasticity and uncertainty maps are color coded using the rainbow colormap in Fig. 1 (red is soft, purple is stiff) (c) and the window level in parentheses. (a) A coronal slice of the inspiration dataset where the upper and lower part is identified as fibrotic by a physician. (b) Notice how the fibrotic areas in the lower and upper lung appear stiffer than the surrounding tissue, and also that the cyst in the lower left of the image is estimated as “soft” which could mean that it is ventilated and therefore collapses during expiration. (c) The IQR of the estimated elasticity map. The uncertainty is somewhat larger in the stiffer fibrotic area.
Figure 4: Most consistent cluster in the Signal space and the Atlas shown in the anatomical space. For each method, we show the group-wise (left) and subject-specific (right) assignment. Also shown is the average and standard eviation of the cluster fMRI signal. Results for subject 2.
Figure 4: Most consistent cluster in the Signal space and the Atlas shown in the anatomical space. For each method, we show the group-wise (left) and subject-specific (right) assignment. Also shown is the average and standard eviation of the cluster fMRI signal. Results for subject 2.
Figure 1: Volumetric brain tumor mapping of FLAIR, Necrosis, and Contrast Enhancement. A 55 year old male patient with a right temporal GBM. (a) Axial FLAIR image demonstrates segmentation (in blue) of the region of FLAIR hyperintensity corresponding to the area of edema/ tumor infiltration. Notice the segmented enhancement (yellow) and necrosis (orange) that has been segmented on the T1WI post- contrast. (b) The segmented edema/tumor infiltration (blue), enhancement (yellow) and necrosis (orange) are seen overlaid on a base post- contrast T1WI. (c) Axial post-contrast enhanced T1WI demonstrates the segmentation of the enhancement (yellow) and necrosis (orange).
Figure 1: Volumetric brain tumor mapping of FLAIR, Necrosis, and Contrast Enhancement. A 55 year old male patient with a right temporal GBM. (a) Axial FLAIR image demonstrates segmentation (in blue) of the region of FLAIR hyperintensity corresponding to the area of edema/ tumor infiltration. Notice the segmented enhancement (yellow) and necrosis (orange) that has been segmented on the T1WI post- contrast. (b) The segmented edema/tumor infiltration (blue), enhancement (yellow) and necrosis (orange) are seen overlaid on a base post- contrast T1WI. (c) Axial post-contrast enhanced T1WI demonstrates the segmentation of the enhancement (yellow) and necrosis (orange).
Representative maps of Ktrans, ve and kep generated from the original and motion-corrected DCE-MRI from one patient. The χ2 maps and T1-weighted image are also shown. The contours of the reliable pixel areas are indicated by the black line on the χ2 maps. The χ2 map generated from the motion-corrected DCE-MRI shows a broadened acceptable pixel area covering the greater area of the SPN, when compared to the map from the original DCE-MRI. The Ktrans, ve and kep maps from the motion-corrected DCE-MRI show better reflections of the boundary of the nodule than those from the original DCE-MRI.
Representative maps of Ktrans, ve and kep generated from the original and motion-corrected DCE-MRI from one patient. The χ2 maps and T1-weighted image are also shown. The contours of the reliable pixel areas are indicated by the black line on the χ2 maps. The χ2 map generated from the motion-corrected DCE-MRI shows a broadened acceptable pixel area covering the greater area of the SPN, when compared to the map from the original DCE-MRI. The Ktrans, ve and kep maps from the motion-corrected DCE-MRI show better reflections of the boundary of the nodule than those from the original DCE-MRI.
Figure 2: Ways a phased-array transducer can be used include producing multiple focal spots to increase the ablated volume per sonication, steering the focal point to different locations, and correcting for aberration caused by tissue structures in the ultrasound beam path.
Figure 2: Ways a phased-array transducer can be used include producing multiple focal spots to increase the ablated volume per sonication, steering the focal point to different locations, and correcting for aberration caused by tissue structures in the ultrasound beam path.
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