Abstract: In an image guidance method, a stream of cone-beam computed tomography (CBCT) images of an anatomical organ is acquired using a CBCT imaging device (34). For each CBCT image (44), the surface of the organ in the CBCT image is segmented to generate a segmented surface (70) of the anatomical organ in the CBCT image. Occluded shape reconstruction (66) using a statistical shape prior (68) may be used to reconstruct a missing or occluded surface portion. A pre-operative medical image (12) of the anatomical organ and the CBCT image are spatially registered by warping one of the images to the other of the images using point set registration of a segmented surface (30) of the organ in the preoperative medical image and the segmented surface of the organ in the CBCT image. A fused image (82) is displayed that combines the warped image and the other image.

Abstract: The described implementations relate to systems, methods, and apparatuses for generating regions of interest (214) from imaging data (212). Specifically, the regions of interest are generated for tracking treatment efficacy in a more consistent and repeatable manner. The regions of interest can be generated from contrast medium and non-contrast medium enhanced scans (102) of a patient. Voxel data derived from the scans can be collected and distributed according to respective intensity values in order to identify mode voxels (116, 118, 120) for particular ranges (128) of intensities. Regions of interest (110, 112, 114) can then be generated for each identified mode voxel, and standard deviations for the regions of interest can be determined. One or more thresholds can be derived from the determined standard deviations in order to further filter the intensity values and identify filtered groups of voxels to be the resulting regions of interest.

Abstract: The described implementations relate to systems, methods, and apparatuses for generating regions of interest (214) from imaging data (212). Specifically, the regions of interest are generated for tracking treatment efficacy in a more consistent and repeatable manner. The regions of interest can be generated from contrast medium and non-contrast medium enhanced scans (102) of a patient. Voxel data derived from the scans can be collected and distributed according to respective intensity values in order to identify mode voxels (116, 118, 120) for particular ranges (128) of intensities. Regions of interest (110, 112, 114) can then be generated for each identified mode voxel, and standard deviations for the regions of interest can be determined. One or more thresholds can be derived from the determined standard deviations in order to further filter the intensity values and identify filtered groups of voxels to be the resulting regions of interest.

Abstract: In an image guidance method, a stream of cone-beam computed tomography (CBCT) images of an anatomical organ is acquire using a CBCT imaging device (34). For each CBCT image (44), the surface of the organ in the CBCT image is segmented to generate a segmented surface (70) of the anatomical organ in the CBCT image. Occluded shape reconstruction (66) using a statistical shape prior (68) may be used to reconstruct a missing or occluded surface portion. A pre-operative medical image (12) of the anatomical organ and the CBCT image are spatially registered by warping one of the images to the other of the images using point set registration of a segmented surface (30) of the organ in the preoperative medical image and the segmented surface of the organ in the CBCT image. A fused image (82) is displayed that combines the warped image and the other image.