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 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 plurality of processors with logic units to train one or more neural networks for image construction, at least in part, using established one or more levels of compression for image data from a region of interest (ROI).

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 plurality of processors with logic units to train one or more neural networks for image construction, at least in part, using established one or more levels of compression for image data from a region of interest (ROI).

Abstract: This disclosure introduces an approach that includes techniques for determining an optimal weighted execution sequence of available reconstruction algorithms using a multi-processor unit. The introduced approach includes executing a series of optimal weighted execution sequence candidates on a representative slice of the image data and comparing their results to select one of the candidates as the optimal weighted execution sequence.

Abstract: A machine learning guided image segmentation process is performed by an electronic processor (10). Image segmentation (22) is performed to generate an initial segmented representation (50) of an anatomical structure in the medical image. Parameters of a geometric shape are fitted (52) to the anatomical structure in the medical image to produce initial fitted shape parameters (54). A classification is assigned for the anatomical structure in the medical image using at least one classifier (60) operating on the initial fitted shape parameters and the initial segmented representation of the anatomical structure. A final segmented representation (72) of the anatomical structure in the medical image is generated by operations including repeating (70) the image segmentation using the classification as prior knowledge. In illustrative embodiments, the anatomical structure is a heart and the geometric shape is an ellipsoid.

Abstract: A lung segmentation processor (40) is configured to classify magnetic resonance (MR) images based on noise characteristics. The MR segmenatation processor generates a lung region of interest (ROI) and detailed structure segmentation of the lung from the ROI. The MR segmentation processor performs an iterative normalization and region definition approach that captures the entire lung and the soft tissues within the lung accurately. Accuracy of the segmentation relies on artifact classification coming inherently from MR images. The MR segmentation processor (40) correlates segmented lung internal tissue pixels with the lung density to determine the attenuation coefficients based on the correlation. Lung densities are computed using MR data obtained from imaging sequences that minimize echo and acquisition times. The densities differentiate healthy tissues and lesions, which an attenuation map processor (36) uses to create localized attenuation maps for the lung.

Abstract: Iterative reconstruction (20) of imaging data is performed to generate a sequence of update images (22) terminating at a reconstructed image. During the iterative reconstruction, at least one of an update image and a parameter of the iterative reconstruction is adjusted using an adjustment process separate from the iterative reconstruction. In some embodiments using an edge-preserving regularization prior (26), the adjustment process (30) adjusts an edge preservation threshold to reduce gradient steepness above which edge preservation applies for later iterations compared with earlier iterations. In some embodiments, the adjustment process includes determining (36, 38) for each pixel, voxel, or region of a current update image whether its evolution prior to the current update image 22) satisfies an artifact feature criterion. A local noise suppression operation (40) is performed on the pixel, voxel, or region if the evolution satisfies the artifact feature criterion and is not performed otherwise.

Abstract: A diagnostic imaging system retrieves data (206) from a plurality of accessible data sources, the accessible data sources storing data including physiological data describing a subject to be imaged, a nature of a requested diagnostic image, image preferences of a clinician who requested the diagnostic image, and previously reconstructed images of the requested nature of the subject and/or other subjects, reconstruction parameters and/or sub-routines used to reconstruct the previously reconstructed images. The system analyzes (6, 12) the retrieved data to automatically generate reconstruction parameters and/or sub-steps specific to the nature of the requested diagnostic image, the subject, and the clinician image preferences. The system controls a display device (10, 216) to display the generated reconstruction parameters and/or sub-routines to the user for a user selection.

Abstract: A diagnostic imaging system retrieves data (206) from a plurality of accessible data sources, the accessible data sources storing data including physiological data describing a subject to be imaged, a nature of a requested diagnostic image, image preferences of a clinician who requested the diagnostic image, and previously reconstructed images of the requested nature of the subject and/or other subjects, reconstruction parameters and/or sub-routines used to reconstruct the previously reconstructed images. The system analyzes (6, 12) the retrieved data to automatically generate reconstruction parameters and/or sub-steps specific to the nature of the requested diagnostic image, the subject, and the clinician image preferences. The system controls a display device (10, 216) to display the generated reconstruction parameters and/or sub-routines to the user for a user selection.

Abstract: An image data processor (106) includes a structural image data processor (114) that employs a multi-structure atlas to segment a region of interest from structural image data that includes tissue of interest and that segments the tissue of interests from the region of interest. The image data processor further includes functional image data processor (116) that identifies the tissue of interest in functional image data based on the segmented tissue of interest. An image data processor includes a multi-structure atlas generator (104) that generates a multi-structure atlas. The multi-structure atlas physically maps structure to tissue of interest such that locate the structure in structural image data based on the multi-structure atlas localizes the tissue of interest to the region of interest.

Abstract: A diagnostic imaging system utilizing a reduced crystal design pattern is utilized to image a subject and collect event data. The reduced crystal design pattern includes filled crystal locations and empty crystal locations. A processor accounts for empty crystal locations by selecting windows that include nearest neighbor filled crystal locations. The nearest neighbor filled crystal locations include event data which is averaged by the processor and assigned to the empty crystal location. A weighted average based on distance or event strength is incorporated.

Abstract: A diagnostic imaging system utilizing a reduced crystal design pattern is utilized to image a subject and collect event data. The reduced crystal design pattern includes filled crystal locations and empty crystal locations. A processor accounts for empty crystal locations by selecting windows that include nearest neighbor filled crystal locations. The nearest neighbor filled crystal locations include event data which is averaged by the processor and assigned to the empty crystal location. A weighted average based on distance or event strength is incorporated.

Abstract: PET/MR images are compensated with simplified adaptive algorithms for truncated parts of the body. The compensation adapts to a specific location of truncation of the body or organ in the MR image, and to attributes of the truncation in the truncated body part. Anatomical structures in a PET image that do not require any compensation are masked using a MR image with a smaller field of view. The organs that are not masked are then classified as types of anatomical structures, the orientation of the anatomical structures, and type of truncation. Structure specific algorithms are used to compensate for a truncated anatomical structure. The compensation is validated for correctness and the ROI is filled in where there is missing voxel data. Attenuation maps are generated from the compensated ROI.

Abstract: A cardiac imaging method includes acquiring a projection image representation which includes a myocardium (S100). The myocardium is segmented and a mask is generated (S102). The mask is optimized (S104). A blood pool is determined from the optimized mask (S106) and the mask is skeletonized based on a clusterfication of the myocardial slices (S108). The center of mass is determined (S110) from the blood pool and the skeletonized mask. Myocardial parameters are determined (S112) from the skeletonized mask.

Abstract: An image data processor (106) includes a structural image data processor (114) that employs a multi-structure atlas to segment a region of interest from structural image data that includes tissue of interest and that segments the tissue of interests from the region of interest. The image data processor further includes functional image data processor (116) that identifies the tissue of interest in functional image data based on the segmented tissue of interest. An image data processor includes a multi-structure atlas generator (104) that generates a multi-structure atlas. The multi-structure atlas physically maps structure to tissue of interest such that locate the structure in structural image data based on the multi-structure atlas localizes the tissue of interest to the region of interest.

Abstract: A diagnostic imaging system retrieves data (206) from a plurality of accessible data sources, the accessible data sources storing data including physiological data describing a subject to be imaged, a nature of a requested diagnostic image, image preferences of a clinician who requested the diagnostic image, and previously reconstructed images of the requested nature of the subject and/or other subjects, reconstruction parameters and/or sub-routines used to reconstruct the previously reconstructed images. The system analyzes (6, 12) the retrieved data to automatically generate reconstruction parameters and/or sub-steps specific to the nature of the requested diagnostic image, the subject, and the clinician image preferences. The system controls a display device (10, 216) to display the generated reconstruction parameters and/or sub-routines to the user for a user selection.

Abstract: An image data processor (106) includes a structural image data processor (114) that employs a multi-structure atlas to segment a region of interest from structural image data that includes tissue of interest and that segments the tissue of interests from the region of interest. The image data processor further includes functional image data processor (116) that identifies the tissue of interest in functional image data based on the segmented tissue of interest. An image data processor includes a multi-structure atlas generator (104) that generates a multi-structure atlas. The multi-structure atlas physically maps structure to tissue of interest such that locate the structure in structural image data based on the multi-structure atlas localizes the tissue of interest to the region of interest.