Abstract: A system and method include execution of a first scan to acquire a first PET dataset, back-projection of the first PET dataset to generate a first histo-image, input of the first histo-image to a trained neural network, and reception of a first output image from the trained neural network.

Abstract: A method of conducting a multi-pass dynamic positron emission tomography (PET) scans using continuous bed motion (CBM) mode is disclosed where the method involves acquiring PET sinogram data using CBM mode as the patient bed is moving in a first direction; acquiring PET sonogram data using CBM mode as the patient bed is moving in a second direction that is opposite from the first direction; and reconstructing 3-D PET image from the acquired PET sonogram data.

Abstract: Systems and methods to determine random coincidence rates include determination of a detector rate for each of a plurality of detectors of a positron emission tomography scanner based on a frame of positron emission tomography data, determination of a sensitivity for each detector crystal of the plurality of detectors, based on the detector rate of the detector including the detector crystal, determination of a singles rate for each detector crystal based on the detector rate of the detector including the detector crystal and the determined sensitivity of the detector crystal, estimation of a mean random coincidence rate for each of a plurality of pairs of the detector crystals based on the singles rate of each detector crystal of each of the plurality of pairs of the detector crystals, correction of the acquired frame of positron emission tomography data based on the estimated mean random coincidence rates, and reconstruction of a positron emission tomography image based on the corrected frame of positron em

Abstract: Systems and methods to determine random coincidence rates include determination of a detector rate for each of a plurality of detectors of a positron emission tomography scanner based on a frame of positron emission tomography data, determination of a sensitivity for each detector crystal of the plurality of detectors, based on the detector rate of the detector including the detector crystal, determination of a singles rate for each detector crystal based on the detector rate of the detector including the detector crystal and the determined sensitivity of the detector crystal, estimation of a mean random coincidence rate for each of a plurality of pairs of the detector crystals based on the singles rate of each detector crystal of each of the plurality of pairs of the detector crystals, correction of the acquired frame of positron emission tomography data based on the estimated mean random coincidence rates, and reconstruction of a positron emission tomography image based on the corrected frame of positron em

Abstract: A method for performing a multi-bed scan includes receiving scanner-specific information including scanner sensitivity and receiving patient-specific information including attenuation. An attenuation-weighted sensitivity profile is calculated based on the scanner sensitivity and the attenuation. Individual bed scan times for each bed in a multi-bed study is calculated based on the attenuation-weighted sensitivity profile and the multi-bed scan is performed using the calculated individual bed scan times.

Abstract: A system and method include execution of a first scan to acquire a first PET dataset, back-projection of the first PET dataset to generate a first histo-image, input of the first histo-image to a trained neural network, and reception of a first output image from the trained neural network.

Abstract: A method for self-time alignment procedure for a PET scanner system is disclosed. Modeled time-of-flight (TOF) data are compared against the measured TOF data in order to find individual detector's time offsets (TOs). Then the TOs are estimated simultaneously by matching the TOF center of mass between the modeled and measured TOF data.

Abstract: A method for reconstructing medical images comprises: identifying a plurality of organs in a body of a subject based on an anatomic image; assigning a plurality of voxels in the body to respective ones of the plurality of organs based on the anatomic image; and reconstructing activity images of the body using respectively different processing for the voxels assigned to each respective one of the plurality of organs.

Abstract: A method of processing and reconstructing dynamic positron emission tomography (PET) sinogram data comprises: acquiring PET sinogram data using continuous bed motion having a varying velocity; recording a plurality of position-time coordinate pairs while acquiring the PET sinogram data; determining respective acquisition times of each of a plurality of slices of the image, based on the plurality of position-time coordinates; and reconstructing respective parametric images for each respective slice in the plurality of slices.

Abstract: A method for reconstructing medical images comprises: identifying a plurality of organs in a body of a subject based on an anatomic image; assigning a plurality of voxels in the body to respective ones of the plurality of organs based on the anatomic image; and reconstructing activity images of the body using respectively different processing for the voxels assigned to each respective one of the plurality of organs.

Abstract: A method for self-time alignment procedure for a PET scanner system is disclosed. Modeled time-of-flight (TOF) data are compared against the measured TOF data in order to find individual detector's time offsets (TOs). Then the TOs are estimated simultaneously by matching the TOF center of mass between the modeled and measured TOF data.

Abstract: A method and system for simultaneously monitoring a positron emission tomography scanner performance during a continuous-bed-motion acquisition is disclosed.

Abstract: A method comprises: detecting a plurality of radiation events using a plurality of radiation detectors; determining a fraction of the plurality of radiation events, such that a coincidence circuit has sufficient capacity to process each radiation event in the fraction of the plurality of radiation events; counting the determined fraction of the plurality of radiation events using the coincidence circuit, and excluding a remainder of the plurality of radiation events from the counting; and performing positron emission tomography (PET) processing on each radiation event in the fraction of the plurality of radiation events.

Abstract: A method for generating a nuclear image includes obtaining, via a camera, a surface image of a patient. A synthetic computed-tomography (CT) image of the patient is generated based on the surface image. First time-of-flight (TOF) data for the patient is obtained via a nuclear imaging modality. Attenuation correction is applied to the first TOF data. The synthetic image is applied as a density map during the attenuation correction. A nuclear image is generated from the attenuation corrected first TOF data.

Abstract: A method of processing and reconstructing dynamic positron emission tomography (PET) sinogram data comprises: acquiring PET sinogram data using continuous bed motion having a varying velocity; recording a plurality of position-time coordinate pairs while acquiring the PET sinogram data; determining respective acquisition times of each of a plurality of slices of the image, based on the plurality of position-time coordinates; and reconstructing respective parametric images for each respective slice in the plurality of slices.

Abstract: A method of scanner correction includes obtaining a first photopeak location for a first crystal in a detector. Image data is received from the first crystal. The first crystal generates the image data during a current imaging procedure. The image data is processed using the first photopeak location. A second photopeak location is determined for the first crystal from the image data. A difference between the first photopeak location and the second photopeak location of the first crystal is determined and the image data is reprocessed using the second photopeak location when the difference between the first photopeak location and the second photopeak location exceeds a predetermined threshold. An image is generated using the image data.

Abstract: A method and system for simultaneously monitoring a positron emission tomography scanner performance during a continuous-bed-motion acquisition is disclosed.

Abstract: A method of conducting a multi-pass dynamic positron emission tomography (PET) scans using continuous bed motion (CBM) mode is disclosed where the method involves acquiring PET sinogram data using CBM mode as the patient bed is moving in a first direction; acquiring PET sonogram data using CBM mode as the patient bed is moving in a second direction that is opposite from the first direction; and reconstructing 3-D PET image from the acquired PET sonogram data.

Abstract: Motion correction is performed in time-of-flight (TOF) positron emission tomography (PET). Rather than applying motion correction to reconstructed images or as part of reconstruction, the motion correction is applied in the projection domain of the PET data. The TOF data from the PET scan is altered to account for the motion. The TOF data is altered prior to starting reconstruction. The motion in the patient or image domain is forward projected to provide motion in the projection domain of the TOF data. The projected motion of different phases is applied to the TOF data from different phases, respectively, to create a combined dataset of motion corrected TOF data representing the patient at a reference phase. The dataset is larger (e.g., similar size from projection data dimension point of view, but contains more counts per projection data unit or is more dense) than available at one phase of the physiological cycle and is then used in reconstruction.

Abstract: A method includes overlaying a grid on a set of dynamic PET, SPECT, CT or MR data, so as to define a set of voxels defining a plurality of cluster seeds; extracting a respective time activity curve (TAC) for dynamic PET or SPECT data or time varying signals in the case of dynamic CT or MR data, for each voxel based on the data; selecting a subset of the cluster seeds defined by the grid as initial cluster centroids of a set of clusters; assigning each TAC to a respective cluster in the set of clusters; computing a respective average TAC of each cluster; generating a parametric image based on the respective average TACs for the clusters; repeating the overlaying, determining, selecting, assigning, computing, and generating; and averaging the generated parametric images.