Full dose PET image estimation from low-dose PET imaging using deep learning

    公开(公告)号:US11576628B2

    公开(公告)日:2023-02-14

    申请号:US16959289

    申请日:2018-12-26

    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.

    Time-of-flight (TOF) PET image reconstruction using locally modified TOF kernels

    公开(公告)号:US10977841B2

    公开(公告)日:2021-04-13

    申请号:US16317656

    申请日:2017-07-20

    Abstract: An imaging device (1) includes a positron emission tomography (PET) scanner (10) including radiation detectors (12) and coincidence circuitry for detecting electron-positron annihilation events as 511 keV gamma ray pairs defining lines of response (LORs) with each event having a detection time difference At between the 511 keV gamma rays of the pair. At least one processor (30) is programmed to reconstruct a dataset comprising detected electron-positron annihilation events acquired for a region of interest by the PET scanner to form a reconstructed PET image wherein the reconstruction includes TOF localization of the events along respective LORs using a TOF kernel having a location parameter dependent on At and a TOF kernel width or shape that varies over the region of interest. A display device (34) is configured to display the reconstructed PET image.

    Dead pixel correction for digital PET reconstruction

    公开(公告)号:US10852448B2

    公开(公告)日:2020-12-01

    申请号:US16469605

    申请日:2017-12-06

    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.

    Edge preserving penalized reconstruction for step-and-shoot and motion compensated positron emission tomography (PET) studies

    公开(公告)号:US12131410B2

    公开(公告)日:2024-10-29

    申请号:US17293958

    申请日:2019-11-15

    CPC classification number: G06T11/008 A61B6/037 G06T11/006 G06T2211/424

    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: receiving 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 at least a volume (k) and a succeeding volume (k+1) at least partially overlapping the volume (k) along the axial direction; and generating an image of the volume (k) using an iterative image reconstruction process in which an iteration of the iterative image reconstruction process includes: computing a local penalty function for suppressing noise over the volume (k) including reducing the value of the local penalty function in an overlap region; generating an update image of the volume (k) using imaging data from the volume (k) and further using the local penalty function.

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