Abstract: Emission imaging data are reconstructed to generate a low dose reconstructed image. Standardized uptake value (SUV) conversion (30) is applied to convert the low dose reconstructed image to a low dose SUV image. A neural network (46, 48) is applied to the low dose SUV image to generate an estimated full dose SUV image. Prior to applying the neural network the low dose reconstructed image or the low dose SUV image is filtered using a low pass filter (32). The neural network is trained on a set of training low dose SUV images and corresponding training full dose SUV images to transform the training low dose SUV images to match the corresponding training full dose SUV images, using a loss function having a mean square error loss component (34) and a loss component (36) that penalizes loss of image texture and/or a loss component (38) that promotes edge preservation.

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: A non-transitory storage medium storing instructions readable and executable by an imaging workstation (18) including at least one electronic processor (20) to perform an image reconstruction method (100). The method includes: receiving emission imaging data (22) from an image acquisition device (12) wherein the emission imaging data has been filtered using an acquisition energy passband (18); generating filtered imaging data by filtering the emission imaging data with a second energy passband (90) that is narrower than an acquisition energy passband; reconstructing the filtered imaging data to generate an intermediate image; estimating one or more scatter correction factors (SCFs) from the intermediate image; and reconstructing the emission imaging data corrected with the estimated SCFs to generate a reconstructed image.

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: 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) to reconstruct list mode data acquired over a frame acquisition time using a plurality of radiation detectors (17) in which the events of the list mode data is time stamped.

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 acquisition and reconstruction method (30, 32). The method includes: acquiring emission imaging data using an emission image acquisition device (12) wherein the acquiring is scheduled to be performed over an acquisition time; during the acquiring, measuring a count or count rate of the acquired emission imaging data; during the acquiring, adjusting the acquisition time based on the measured count or count rate to generate an adjusted acquisition time; stopping the acquiring at the adjusted acquisition time; and reconstructing the emission imaging data acquired over the adjusted acquisition time to generate one or more reconstructed images.

Abstract: A positron emission tomography (PET) imaging device (10) includes a plurality of PET detector modules (18); and a robotic gantry (20) operatively connected to the PET detector modules. The robotic gantry is configured to control a position of each PET detector module along at least two of an axial axis, a radial axis, and a tangential axis of the corresponding PET detector module.

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: determining a weighting parameter (13) of an edge-preserving regularization or penalty of a regularized image reconstruction of an image acquisition device (12) for an imaging data set obtained by the image acquisition device; determining an edge sensitivity parameter (?) of the edge-preserving algorithm for the imaging data set obtained by the image acquisition device; and reconstructing the imaging data set obtained by the image acquisition device to generate a reconstructed image by applying the regularized image reconstruction including the edge-preserving regularization or penalty with the determined weighting and edge sensitivity parameters to the imaging data set obtained by the image acquisition device.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (20) to perform an image reconstruction method (100). The method includes: performing iterative image reconstruction of imaging data acquired using an image acquisition device (12); selecting an update image from a plurality of update images produced by the iterative image reconstruction; processing the selected update image to generate a hot spot artifact map; and suppressing hot spots identified by the generated hot spot artifact map in a reconstructed image output by the iterative image reconstruction.

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: An imaging method (100) includes: acquiring first training images of one or more imaging subjects using a first image acquisition device (12); acquiring second training images of the same one or more imaging subjects as the first training images using a second image acquisition device (14) of the same imaging modality as the first imaging device; and training a neural network (NN) (16) to transform the first training images into transformed first training images having a minimized value of a difference metric comparing the transformed first training images and the second training images.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by a workstation (14) operatively connected to a display device (20) and including at least one electronic processor (16) to perform an image acquisition and reconstruction method (101). The method includes: retrieving a non-voxel-based reconstructed image comprising non-voxel image elements from a picture and archiving communication system (PACS) database (24) to the workstation; at the workstation, generating at least one voxel-based resampled image from the non-voxel-based reconstructed image; and displaying the at least one voxel-based reconstructed image on the display device.

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: A non-transitory storage medium stores instructions readable and executable by an imaging workstation (14) including at least one electronic processor (16) operatively connected with a display device (20) to perform an image reconstruction method (100). The method includes: reconstructing imaging data acquired by an image acquisition device (12) using an iterative image reconstruction algorithm to generate at least one reconstructed image (22); delineating one or more contours (26) of the at least one reconstructed image to determine a region of interest (ROI) (24) of the at least one reconstructed image; computing at least one quality metric value (30) of the ROI, the at least one quality metric value including at least one of a convergence quality metric, a partial volume effect (PVE) quality metric, and a local count quality metric; and displaying, on the display device, the at least one quality metric value and the at least one reconstructed image showing the ROI.

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 medical imaging subject support table includes a belt conveyor system with a conveyor belt (18) maintained in tension and passing through a bore (14) of an imaging device (12); and motorized pulleys (20) disposed at opposite ends of the bore to move the conveyor belt through the bore. Table supports (24) are positioned outside of the bore of the imaging device on opposite ends of the bore and support the conveyor belt outside the bore of the imaging device.

Abstract: An imaging data set (22) comprising detected counts along lines of response (LORs) is reconstructed (24) to generate a full-volume image at a standard resolution. A region selection graphical user interface (GUI) (26) is provided via which a user-chosen region of interest (ROI) is defined in the full-volume image, and this is automatically adjusted by identifying an anatomical feature corresponding to the user-chosen ROI and adjusting the user-chosen ROI to improve alignment with that feature. A sub-set (32) of the counts of the imaging data set is selected (30) for reconstructing the ROI, and only the selected sub-set is reconstructed (34) to generate a ROI image (36) representing the ROI at a higher resolution than the standard resolution. A fraction of the sub-set of counts may be reconstructed using different reconstruction algorithms (40) to generate corresponding sample ROI images, and a reconstruction algorithm selection graphical user interface (42) employs these sample ROI images.

Abstract: A device (10) for measuring respiration of a patient includes a positron emission tomography (PET) or single photon emission computed tomography (SPECT) imaging device (12). At least one electronic processor (16) is programmed to: extract a first respiration data signal (32) from emission imaging data of a patient acquired by the PET or SPECT imaging device; extract a second respiration data signal (36) from a photoplethysmograph (PPG) signal of the patient; and combine the first and second extracted respiration data signals to generate a respiration signal (40) indicative of respiration of the patient.

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.