Abstract: In positron emission tomography (PET) imaging, PET imaging data (22) having TOF localization is reconstructed. TOF image reconstruction (30) is performed on the PET imaging data to produce a TOF reconstructed image (32). The TOF image reconstruction utilizes the TOF localization of the PET imaging data. Non-TOF image reconstruction (40) is also performed on the PET imaging data to produce a non-TOF reconstructed image (42). The non-TOF image reconstruction does not utilize the TOF localization of the PET imaging data. A comparison image (50) is computed which is indicative of differences between the TOF reconstructed image and the non TOF reconstructed image. An adjustment (54) is determined for the TOF image reconstruction based on the comparison image, such as alignment correction of an attenuation map (18), and the TOF image reconstruction is repeated on the PET imaging data with the determined adjustment to produce an adjusted TOF reconstructed image.

Abstract: Imaging data (20) are acquired by a PET scanner (6) or other imaging device. Iterative image reconstruction of the imaging data is performed to generate a reconstructed image (22). The iterative image reconstruction includes performing an update step (24) that includes an edge preserving prior (28) having a spatially varying edge preservation threshold (30) whose value at each image voxel depends on a noise metric (32) in a local neighborhood of the image voxel. The noise metric may be computed as an aggregation of the intensities of neighborhood image voxels of the reconstructed image in the local neighborhood of the image voxel. The edge preserving prior may be a Relative Difference Prior (RDP). For further noise suppression, during the iterative image reconstruction image values of image features of the reconstructed image that have spatial extent smaller than a threshold (38) may be reduced.

Abstract: A positron emission tomography (PET) detector array includes an enclosing radiation detector array (10) comprising radiation detector elements (14, 16) effective for detecting 511 keV radiation emanating from inside the radiation detector array. The radiation detector pixels of the cylindrical radiation detector array include both higher speed radiation detector elements (14) and lower speed radiation detector elements (16). The lower speed radiation detector pixels have a temporal resolution that is coarser than a temporal resolution of the higher speed radiation detector pixels.

Abstract: A time of flight (TOF) positron emission tomography (PET) image (38) is generated from TOF PET imaging data (10) acquired of a subject using a TOF PET imaging data acquisition device (6). Iterative image reconstruction (30) of the TOF PET imaging data is performed with TOF localization of counts along respective lines of response (LORs) to iteratively update a reconstructed image (32). Values for at least one regularization or filtering parameter are assigned to the TOF PET imaging data or to voxels of the reconstructed image based on an estimated TOF localization resolution for the TOF PET imaging data or voxels. Regularization (34) or filtering (36) of the reconstructed image is performed using the assigned values for the at least one regularization or filtering parameter. In some embodiments, the varying TOF localization resolution for the TOF PET imaging data or voxels is estimated based on related N acquisition characteristics such as count rates or operating temperature of the detectors.

Abstract: A nuclear medicine image reconstruction method generates a reconstructed image (44) by performing iterative image in reconstruction (30, 130) on nuclear medicine imaging data (22). The iterative image reconstruction produces a sequence of update images (34, 36, 134, 136). During the iterative image reconstruction, a standardized uptake value (SUV) transform (40) is applied to convert an update image (34, 36) to an update SUV image (42, 46). The SUV transform scales values of voxels of the update image to SUV values using scaling factors including at least a body size metric and a dose metric. During the iterative image reconstruction, at least one parameter used in an image update of the iterative image reconstruction is adjusted using the update SUV image. For example, a parameter of a prior or filter (38) incorporated into an image reconstruction update step (32) or used in filtering of an update image (36) may be adjusted.

Abstract: Iterative reconstruction (20) of imaging data is performed to generate a sequence of update images (22) terminating at a reconstructed image. During the iterative reconstruction, at least one of an update image and a parameter of the iterative reconstruction is adjusted using an adjustment process separate from the iterative reconstruction. In some embodiments using an edge-preserving regularization prior (26), the adjustment process (30) adjusts an edge preservation threshold to reduce gradient steepness above which edge preservation applies for later iterations compared with earlier iterations. In some embodiments, the adjustment process includes determining (36, 38) for each pixel, voxel, or region of a current update image whether its evolution prior to the current update image 22) satisfies an artifact feature criterion. A local noise suppression operation (40) is performed on the pixel, voxel, or region if the evolution satisfies the artifact feature criterion and is not performed otherwise.

Abstract: Image processing performed by a computer (22) includes iterative image reconstruction or refinement (26, 56) that produces a series of update images ending in an iteratively reconstructed or refined image. A difference image (34, 64) is computed between a first update image (30, 60) and a second update image (32, 62) of the series. The difference image is converted to a feature image (40) and is used in the iterative processing (26, 56) or in post-processing (44) performed on the iteratively reconstructed or refined images or images from different reconstruction or refinement techniques. In another embodiment, first and second image reconstructions (81, 83) are performed to generate respective first and second reconstructed images (80, 82). A difference image (84) is computed between two images each selected from the group: the first reconstructed image, an update image of the first reconstruction, the second reconstructed image, and an update image of the second reconstruction.

Abstract: Disclosed technology can provide a process for generating reconstructed muon image resolution to optimize the use of the limited angular range muon track data collected by a muon tomography system. In one aspect, a process for improving reconstructed muon image resolution for a volume of interest (VOI) imaged by a muon tomography system includes: collecting raw muon track data of cosmic ray muon tracks passing through the VOI; grouping the raw muon track data into two or more subsets of tracks based on at least one angular distribution of the muon tracks in the raw muon track data; generating a set of images of the VOI based on the two or more subsets of tracks; and combining information from the set of reconstructed images and a reconstructed image based on the full set of the raw muon track data to obtain a resulting reconstructed image of the VOI.

Abstract: Disclosed technology can provide a process for generating reconstructed muon image resolution to optimize the use of the limited angular range muon track data collected by a muon tomography system. In one aspect, a process for improving reconstructed muon image resolution for a volume of interest (VOI) imaged by a muon tomography system includes: collecting raw muon track data of cosmic ray muon tracks passing through the VOI; grouping the raw muon track data into two or more subsets of tracks based on at least one angular distribution of the muon tracks in the raw muon track data; generating a set of images of the VOI based on the two or more subsets of tracks; and combining information from the set of reconstructed images and a reconstructed image based on the full set of the raw muon track data to obtain a resulting reconstructed image of the VOI.

Abstract: A virtual-reality method for surgical training simulates the task of detecting sentinel lymph nodes using a nuclear uptake probe. The simulator can be used with lymphoscintigraphic clinical imaging data to provide patient-specific training scenarios. In yet another embodiment, the apparatus can use a database representing mathematical phantoms to simulate different patient sizes, node distributions, node uptakes, and combinations thereof.

Abstract: Two different collimation geometries are interleaved. Each collimation geometry samples a transaxial slice through the object being imaged. The even slices are of the same fan-beam geometry as the central head, but the odd slices are of different collimation geometry. Each slice covers an axial range that is the same as the pixel size of the solid-state detector, and aligns with the corresponding pixels in the axial direction.

Abstract: First and second gamma radiation detector heads are oriented to image an area of a subject. The area of said subject is completely within a field of view that is defined between the first and second gamma radiation heads. Focal points of each of the first and second gamma radiation heads are also within an area defined between the first and second gamma radiation heads.

Abstract: Two different collimation geometries are interleaved. Each collimation geometry samples a transaxial slice through the object being imaged. The even slices are of the same fan-beam geometry as the central head, but the odd slices are of different collimation geometry. Each slice covers an axial range that is the same as the pixel size of the solid-state detector, and aligns with the corresponding pixels in the axial direction.

Abstract: A large field of view projection image is obtained and a small field of view projection image is obtained. The two images are normalized, to take into account the difference between the count data between the images, and the way the images represent data. The large field of view image does not include truncation errors that are present in the small field of view image and therefore is stitched together with the smaller field of view image to use the improved data within the small field of view image with the truncation reduction enabled by the larger field of view image.

Abstract: A medical imaging system, e.g., a computed tomography system includes at least one radiation detector that is relatively rotatable with respect to an object of interest. The angular range is divided into discrete continuous acquisition ranges and unsampled angular ranges, wherein the discrete continuous acquisition ranges are separated by unsampled angular ranges.

Abstract: An image from a gamma camera, e.g., from a radiopharmaceutical, is corrected for scatter. The image is approximated by estimating the center of the organ and supposing a Guassian response that is scatter-corrected.

Abstract: A large field of view projection image is obtained and a small field of view projection image is obtained. The two images are normalized, to take into account the difference between the count data between the images, and the way the images represent data. The large field of view image does not include truncation errors that are present in the small field of view image and therefore is stitched together with the smaller field of view image to use the improved data within the small field of view image with the truncation reduction enabled by the larger field of view image.

Abstract: An image from a gamma camera, e.g., from a radiopharmaceutical, is corrected for scatter. The image is approximated by estimating the center of the organ and supposing a Guassian response that is scatter-corrected.

Abstract: A medical imaging system, e.g., a computed tomography system includes at least one radiation detector that is relatively rotatable with respect to an object of interest. The angular range is divided into discrete continuous acquisition ranges and unsampled angular ranges, wherein the discrete continuous acquisition ranges are separated by unsampled angular ranges.

Abstract: Medical imaging information is randomly resampled into the new resampled form. The random resampling can maintain the random nature of the information, and can avoid count loss.