Abstract: A non-transitory computer-readable medium storing instructions readable and executable by a workstation (18) including at least one electronic processor (20) to perform an image reconstruction method (100).

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by a workstation (18) including at least one electronic processor (20) to perform an image reconstruction method (100). The method includes: operating a positron emission tomography (PET) imaging device (12) to acquire imaging data on a frame by frame basis for frames along an axial direction with neighboring frames overlapping along the axial direction wherein the frames include a frame (k), a preceding frame (k?1) overlapping the frame (k), and a succeeding frame (k+1) overlapping the frame (k); reconstructing an image of the frame (k) using imaging data from the frame (k), the preceding frame (k?1), and the succeeding frame (k+1).

Abstract: A non-transitory storage medium stores instructions readable and executable by an electronic processor (20) to perform a method (100) for estimating singles rates for detectors (16) of a detector array (14) of a positron emission tomography (PET) imaging device (12).

Abstract: A non-transitory computer readable medium storing instructions readable and executable by an imaging workstation (14) including at least one electronic processor (16) to perform a dataset generation method (100) operating on emission imaging data acquired of a patient for one or more axial frames at a corresponding one or more bed positions, the method comprising: (a) identifying a frame of interest from the one or more axial frames; (b) generating simulated lesion data by simulating emission imaging data for the frame of interest of at least one simulated lesion placed in the frame of interest; (c) generating simulated frame emission imaging data by simulating emission imaging data for the frame of interest of the patient; (d) determining a normalization factor comprising a ratio of the value of a quantitative metric for the simulated patient data and the value of the quantitative metric for the emission imaging data acquired of the same patient for the frame of interest; and (e) generating a hybrid data set

Abstract: An imaging apparatus includes a nuclear medicine imaging device (10), a patient table (14), and a table controller (18) comprising an electronic processor and actuators configured to position the patient table along an axial direction and in a transverse plane that is transverse to the axial direction. An automatic positioning engine (40) comprises an electronic processor (42) programmed to determine an optimal position of the patient table in the transverse plane for imaging a target of interest in a patient based on a prior image (20, 34) of the patient. The table controller operates the patient table to position the patient table in accord with the determined optimal position of the patient table.

Abstract: A PET detector array (8) comprising detector pixels acquires PET detection counts along lines of response (LORs). The counts are reconstructed to generate a reconstructed PET image (36, 46). The reconstructing is corrected for missing LORs which are missing due to dead detector pixels of the PET detector array. The correction may be by estimating counts along the missing LORs (60) by interpolating counts along LORs (66) neighboring the missing LORs. The interpolation may be iterative to handle contiguous groups of missing detector pixels. The correction may be by computing a sensitivity matrix having matrix elements corresponding to image elements (80, 82) of the reconstructed PET image. In this case, each matrix element is computed as a summation over all LORs intersecting the corresponding image element excepting the missing LORs. The computed sensitivity matrix is used in the reconstructing.

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: 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 system (10) and a method (100) iteratively reconstruct an image of a target volume of a subject. In each iteration of a plurality of iterations, an estimate image of the target volume (54) is forward projected (58) and compared (62) to received event data (44) to determine a discrepancy (64). The discrepancy (64) is back projected (66) and the back projection (68) updates (70) the estimate image (54). In at least one iteration, the estimate image (54) is filtered (52) in the image domain prior to being back projected.

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: An imaging method and corresponding system (10) account for cascade gammas. Event data describing detected gamma rays emitted from a target volume of a subject are received. The detected gamma rays include cascade gammas emitted from a radionuclide within the target volume. Cascade and annihilation gamma emissions from the target volume and coincidence detection of the imaging system (10) are simulated using a Monte Carlo (MC) simulation technique to generate a cascade dataset comprised of annihilation coincidence events and cascade coincidence events. The event data is reconstructed into an image representation of the target volume with correction of cascade coincidence using the relationship between the annihilation coincidence events and the cascade coincidence events in the cascade data set.

Abstract: A whole body SPECT system (10) includes a patient support (14) and a static gantry (12) which includes a plurality of rings (40a,40b,40c) of radiation detectors (42). The patient support (14) supports a patient and moves the patient in an axial direction (18) through the static gantry (12). One or more processors (20,24,32) connected to the plurality of detectors records strikes of gamma photons in the radiation detectors (42) and reconstruct the recorded strikes of the gamma photons into a whole body image.

Abstract: A positron emission tomography (PET) imaging system (10) includes a singles unit (24), a delay unit (26), a scaling unit (28), and a reconstruction unit (30). The singles unit (24) is configured to generate a correction sinogram of random coincidences defined by a coincidence time window tw and a time period t based on rij=2 t si sj where rij is an estimate of the random coincidences in the time period t between singles at detector locations si and sj. The delay unit (26) is configured to determine delay coincidences in the time period t defined by a delay coincidence time window of a paired coincidences including tw to 2 tw. The scaling unit (28) is configured to scale the correction sinogram of random coincidences based on the delay coincidences. The reconstruction unit (30) is configured to iteratively reconstruct one or more images based on the scaled random sinogram and a prompt sinogram for the time period t.

Abstract: Method and apparatus are disclosed for generating a scatter-corrected image from positron emission tomography (PET) or other radioemission imaging data (20) acquired of an object (12) in a field of view (14). A background portion (26B) of the PET imaging data is identified corresponding to a background region (14B) of the FOV that is outside of the object. An outside-FOV activity estimate (40) for at least one spatial region outside of the FOV and into which the object extends is adjusted (e.g. iterative or several randomly selected estimates) to optimize a simulated scatter distribution for the combination of the PET imaging data and the outside FOV activity estimate to match the background portion (26B) of the PET imaging data. The PET imaging data are reconstructed to generate a scatter-corrected PET image of the object in the FOV using the optimized simulated scatter distribution.

Abstract: A diagnostic imaging system includes a plurality of radiation detectors (20) configured to detect radiation events emanating from an imaging region. The system includes a calibration phantom (14) configured to be disposed in the imaging region spanning substantially an entire field of view and to generate radiation event pairs that define lines-of-response, wherein the calibration phantom is thin such that each LOR intersects the calibration phantom along its length, the thickness of the phantom being smaller than the length of the LORs. A calibration processor (24) receives input of the radiation detectors and calculates an incidence angle independent crystal delay Ti for each detector. The calibration processor (24) constructs a first look-up table for the timing correction of each LOR and a second look-up table for the angle depth of interaction correction for each crystal by combining Ti and ?i.

Abstract: Method and apparatus are disclosed for generating a scatter-corrected image from positron emission tomography (PET) or other radioemission imaging data (20) acquired of an object (12) in a field of view (14). A background portion (26B) of the PET imaging data is identified corresponding to a background region (14B) of the FOV that is outside of the object. An outside-FOV activity estimate (40) for at least one spatial region outside of the FOV and into which the object extends is adjusted (e.g. iterative or several randomly selected estimates) to optimize a simulated scatter distribution for the combination of the PET imaging data and the outside FOV activity estimate to match the background portion (26B) of the PET imaging data. The PET imaging data are reconstructed to generate a scatter-corrected PET image of the object in the FOV using the optimized simulated scatter distribution.

Abstract: A diagnostic imaging system comprises a plurality of radiation detectors (20) configured to detect radiation events emanating from an imaging region. The system comprises a calibration phantom (14) configured to be disposed in the imaging region spanning substantially an entire field of view and to generate radiation event pairs that define lines-of-response, wherein the calibration phantom is thin such that each LOR intersects the calibration phantom along its length, the thickness of the phantom being smaller than the length of the LORs. A calibration processor (24) receives input of the radiation detectors and calculates an incidence angle independent crystal delay ?i for each detector.

Abstract: A radioemission scanner (12) is operated to acquire tomographic radioemission data of a radiopharmaceutical in a subject in an imaging field of view (FOV). An imaging system is operated to acquire extension imaging data of the subject in an extended FOV disposed outside of and adjacent the imaging FOV along an axial direction (18). A distribution of the radiopharmaceutical in the subject in the extended FOV is estimated based on the extension imaging data, and further based on a database (32) of reference subjects. The tomographic radioemission data are reconstructed to generate a reconstructed image (26) of the subject in the imaging FOV. The reconstruction includes correcting the reconstructed image for scatter from the extended FOV into the imaging FOV based on the estimated distribution of the radiopharmaceutical in the subject in the extended FOV.

Abstract: An image processing apparatus includes a scatter simulation processor which processes measured sinograms generated from imaging data acquired for an imaging subject by an imaging apparatus to produce a scatter sinogram that represents a shape of scatter contribution. A scatter scaling processor utilizes a Monte Carlo simulation to determine a scatter fraction and scales the scatter sinogram to generate a scaled scatter sinogram that matches the scatter contribution in the measured sinogram. A reconstruction processor reconstructs the imaging data into an image representation using the scaled scatter sinogram for scatter correction.

Abstract: A medical imaging system includes a data store (16) of reconstruction procedures, a selector (24), a reconstructor (14), a fuser (28), and a display (22). The data store (16) of reconstruction procedures identifies a plurality of reconstruction procedures. The selector (24) selects at least two reconstruction procedures from the data store of reconstruction procedures based on a received input, each reconstruction procedure optimized for one or more image characteristics. The reconstructor (14) concurrently performs the selected at least two reconstruction procedures, each reconstruction procedure generates at least one image (26) from the at least one data store of imaging data (12). The fuser (28) fuses the at least two generated medical images to create a medical diagnostic image which includes characteristics from each generated image (26). The display (22) displays the medical diagnostic image.