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 (γ) 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 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 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 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 hybrid imaging system includes a first imaging system configured to acquire anatomical data of a first field of view of an anatomical structure. A second imaging system configured to acquire functional data of the anatomical structure, the second imaging system acquiring functional data in a two-pass list-mode acquisition scheme. A reconstruction processor configured to reconstruct the functional data based on attenuation data into an attenuation corrected image and reconstruct the anatomical data into one or more high resolution images of one or more regions of interest.
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 a quality control (QC) method (100). The method includes: receiving a current QC data set acquired by a pixelated detector (14) and one or more prior QC data sets acquired by the pixelated detector; determining stability levels of detector pixels (16) of the pixelated detector over time from the current QC data set and the one or more prior QC data sets; labeling a detector pixel of the pixelated detector as dead when the stability level determined for the detector pixel is outside of a stability threshold range; and displaying, on a display device (24) operatively connected with the workstation, an identification (28) of the detector pixels labelled as dead.
Abstract:
A database (52) stores image recipient reconstruction profiles each comprising image reconstruction parameter values. An image reconstruction module (30) is configured to reconstruct medical imaging data to generate a reconstructed image. An image reconstruction setup module (50) is configured to retrieve an image recipient reconstruction profile from the database (52) for an intended image recipient associated with a set of medical imaging data and to invoke the image reconstruction module (30) to reconstruct the set of medical imaging data using image reconstruction parameter values of the retrieved image recipient reconstruction profile to generate a reconstructed image for the intended image recipient. A feedback acquisition module (54) is configured to acquire feedback from the intended image recipient pertaining to the reconstructed image for the intended image recipient. A profile updating module (56) is configured to update the image recipient reconstruction profile of the intended image recipient based on the acquired feedback.
Abstract:
A positron emission tomography (PET) system includes a memory (18), a subject support (3), a categorizing unit (20), and a reconstruction unit (22). The memory (18) continuously records detected coincident event pairs detected by PET detectors (4). The subject support (3) supports a subject and moves in a continuous movement through a field of view (10) of the PET detectors (4). The categorizing unit (20) categorizes the recorded coincident pairs into each of a plurality of spatially defined virtual frame (14). The reconstruction unit (22) reconstructs the categorized coincident pairs of each virtual frame into a frame image and combines the frame images into a common elongated image.
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 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.