Abstract: An imaging apparatus and associated methods are provided to receive measured projection data in a primary region and correct for scatter by processing the imaging data as two separate components: non-scatter-corrected data and scatter-only data. Separate image processing (e.g., reconstruction) allows for the use of individualized data processing, including filters, suited to the source data, thereby focusing on specific aspects of the source data, including, for example, noise and artifact reduction, resolution, edge preservation, etc. Combining the separately processed data results in an optimized balance of these aspects with improved image quality.

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 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: 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: 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 acquisition characteristics such as count rates or operating temperature of the detectors.

Abstract: A device (10) for a patient to lie on during a medical imaging procedure includes a main body (12). A matrix of pressure sensors (16) disposed on a top surface (14) of the main body are configured to continuously measure pressure across the top surface. At least one electronic processor (22) is operatively connected to read the pressure sensors. A non-transitory storage medium stores instructions readable and executable by the at least one electronic processor to use the matrix of pressure sensors to perform at least one of: a sag estimation operation (200); a motion estimation operation (300); and a respiratory monitoring operation (400).

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 computer tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (116) configured to detect x-ray radiation and generate a signal indicative thereof, and a reconstructor (118) configured to reconstruct the signal and generate sequential spares time line perfusion volumetric image data. The computer tomography scanner further includes a processor (132) configured to process the sequential spares time line perfusion volumetric image data using a trained neural network of a perfusion data enhancing module (136) to produce sequential dense time line perfusion volumetric image data.

Abstract: A non-spectral computed tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (114) configured to detect x-ray radiation and generate non-spectral data, and a memory (134) configured to store a spectral image module (130) that includes computer executable instructions including a neural network trained to produce spectral volumetric image data. The neural network is trained with training spectral volumetric image data and training non-spectral data. The non-spectral computed tomography scanner further includes a processor (126) configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.

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 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 system (1) includes a device (12, 116 or 118) with memory with spectral volumetric image data generated by a spectrally configured computed tomography scanner including a radiation source and a radiation detector and an image guided system (14) configured to employ the spectral volumetric image data for an image guided procedure. A computer readable medium is encoded with computer executable instructions, where the computer executable instructions, when executed by a processor, causes the processor to: obtain spectral volumetric image data generated by a spectrally configured computed tomography scanner including a radiation source and a radiation detector, and employ the spectral volumetric image data for an image guided procedure. A method includes receiving spectral volumetric image data generated by a spectrally configured computed tomography scanner including a radiation source and a radiation detector, and utilizing he spectral volumetric image data for an image guided procedure.

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 imaging method (100). The method includes: estimating one or more figures of merit for a reconstructed image by applying a trained deep learning transform (30) to input data including at least imaging parameters and not including a reconstructed image; selecting values for the imaging parameters based on the estimated one or more figures of merit; generating a reconstructed image using the selected values for the imaging parameters; and displaying the 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 spectral computed tomography imaging system (102) includes a radiation source (112) configured to emit x-ray radiation and a detector array (114) configured to detect x-ray radiation and generate spectral data. The spectral imaging system further includes a memory (134) configured to store a virtual non-contrast image enhancing module (136) that includes computer executable instructions including a neural network trained to produce image quality enhanced virtual non-contrast images. The neural network is trained with training spectral data and training non-contrast-enhanced images generated from a non-contrast-enhanced scan. The spectral imaging system further includes a processor (132) configured to process the spectral data with the trained neural network to produce the image quality enhanced virtual non-contrast images.

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: An imaging system includes an X-ray tube (202) having a focal spot (204) and a port window (206), and a filter (208) having at least a first region (310) with a first material having first X-ray attenuation characteristics for a redetermined X-ray photon energy range of interest and a second region (312) with a different X-ray attenuation characteristic. The filter is disposed between the port window and an examination region (108) and is configured to rotate such that the at least the first and the second regions sweep through and filter X-ray radiation emitted from the focal spot. The system further includes an X-ray radiation flux detector (2802, 2902) configured to detect an X-ray radiation flux of the filtered X-ray radiation, a detector array (112) configured to detect the filtered X-ray radiation traversing the examination region and produce a signal indicative thereof, and a reconstructor (114) configured to process the signal based on the detected flux to reconstruct volumetric image data.

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.