A method of imaging includes obtaining a plurality of dynamic sinograms, each dynamic sinogram representing detection events of gamma rays at a plurality of detector elements, summing the plurality of dynamic sinograms to generate an activity map based on a radioactivity level of the gamma rays; reconstructing, using the plurality of dynamic sinograms, a plurality of dynamic images, each of the plurality of dynamic images corresponding to one of the each of the plurality of dynamic sinograms, and generating, using the plurality of dynamic sinograms and the activity map, at least one parametric image.

A tomography apparatus includes a data obtainer and an image processor. The data obtainer performs a tomography scan on a moving object and obtains raw data of the object The image processor reconstructs a first tomography image of the object for a first slice section in a first phase from the raw data and reconstructs a second tomography image in a second phase, which is different from the first phase, for the first slice section of the object by using the raw data. The image processor also generates motion information indicating a three-dimensional (3D) motion of the object. The second phase is a phase beyond a phase range of the raw data.

A nuclear imaging apparatus (8) acquires nuclear imaging data comprising events wherein each event records at least spatial localization information and a timestamp for a nuclear decay event. An event-preserving image reconstruction module (22) reconstructs the nuclear imaging data using an event-preserving reconstruction algorithm to generate an image represented as an event-preserving reconstructed image dataset (I.sub.D) comprising for each event the timestamp and at least one spatial voxel assignment. One or more structures are identified in the image and independent motion compensation is performed for each structure. In one approach, an events group is identified corresponding to the structure comprising events assigned to the structure by the event-preserving reconstructed image dataset; a time binning of the events of each events group is optimized based on a motion profile for the structure; time bin images are generated; and the structure is spatially registered in the time bin images.

A method includes re-sampling current image data representing a reference motion state into a plurality of different groups, each group corresponding to a different motion state of moving tissue of interest, forward projecting each of the plurality of groups, generating a plurality of groups of forward projected data, each group of forward projected data corresponding to a group of the re-sampled current image data, determining update projection data based on a comparison between the forward projected data and the measured projection data, grouping the update projection data into a plurality of groups, each group corresponding to a different motion state of the moving tissue of interest, back projecting each of the plurality of groups, generating a plurality of groups of update image data, re-sampling each group of update image data to the reference motion state of the current image, and generating new current image data based on the current image data and the re-sampled update image data.

Systems and methods to partially-gate PET data include acquisition of first data describing a plurality of coincidences detected during a scan of an object, each of the plurality of coincidences associated with a coincidence time and a line of response, acquisition of a motion signal associated with motion of the object during the scan, determination of lines of response which are associated with a region of the object, determination of time periods of region motion based on the motion signal, modification of the first data to remove coincidences which are associated with the determined lines of response and which are associated with a coincidence time during a time period of region motion, reconstruction of an image of the object based on the modified first data, and display of the image.

For controlling reconstruction in emission tomography, the quality of data for detected emissions and/or the application controls the settings used in reconstruction. For example, a count density of the detected emissions is used to control the number of iterations in reconstruction to more likely avoid over and under fitting. The count density may be adaptively determined by re-binning through pixel size adjustment to find a smallest pixel size providing a sufficient count density. As another example, the detected data may have poor quality due to motion or high body mass index (BMI) of the patient, so the reconstruction is set to perform differently (e.g., less smoothing for high motion or a different number of iterations for high BMI). The quality of the data may be used in conjunction with the application or task for imaging the patient to control the reconstruction.

Methods and systems for acquiring a visualization of a target. For example, a computer-implemented method for acquiring a visualization of a target includes: generating a first sampling mask; acquiring first k-space data of the target at a first phase using the first sampling mask; generating a first image of the target based at least in part on the first k-space data; generating a second sampling mask using a model based on at least one selected from the first sampling mask, the first k-space data, and the first image; acquiring second k-space data of the target at a second phase using the second sampling mask; and generating a second image of the target based at least in part on the second k-space data.

Various methods and systems are provided for stationary CT imaging. In one embodiment, a method for an imaging system includes activating a plurality of emitters of a stationary distributed x-ray source unit to emit x-ray beams toward an object within an imaging volume, where the x-ray source unit does not rotate around the imaging volume, receiving attenuated x-ray beams with one or more detector arrays to form a sparse view projection dataset, where each attenuated x-ray beam generates a different view, and reconstructing an image from the sparse view projection dataset using a sparse view reconstruction method.

An imaging apparatus and associated methods are provided to efficiently estimate scatter during multi-fraction treatments for improved quality and workflow. Estimated scatter from one fraction during a treatment course can be utilized during subsequent fractions, allowing for measurements with higher scatter-to-primary ratios. The quality of scatter estimates can be maintained, while workflow improves and dosage decreases. Scan configuration limits can be utilized to maintain a minimum level of scatter measurement quality. Patient information can be monitored to ensure that prior fraction scatter estimates are still applicable to current patient status.

An imaging method may include obtaining original imaging data of an object in a raw-data domain including original time of flight (TOF) information. The method may also include gating the original imaging data into a plurality of data sets in the raw-data domain. The method may also include determining a plurality of motion vector fields based on the plurality of data sets. The method may also include generating corrected imaging data in the raw-data domain by performing motion correction on at least one of the plurality of data sets based on the original TOF information and at least one corresponding MVF of the plurality of MVFs. The method may also include generating one or more target images of the object by performing, based on the corrected imaging data, image reconstruction.