Abstract: Systems and methods to estimate 3D TOF scatter include acquisition of 3D TOF data, determination of 2D TOF data from the first TOF data, determination of first estimated scatter based on the second TOF data, reconstruction of a first estimated image based on the first estimated scatter and the second TOF data, determination of attenuated unscattered true coincidences based on the first estimated image, determination of second estimated scatter based on the first TOF data and the attenuated unscattered true coincidences, and reconstruction of an image of the object based on the first TOF data and the second estimated scatter.

Abstract: Systems and methods to estimate 3D TOF scatter include acquisition of 3D TOF data, determination of 2D TOF data from the first TOF data, determination of first estimated scatter based on the second TOF data, reconstruction of a first estimated image based on the first estimated scatter and the second TOF data, determination of attenuated unscattered true coincidences based on the first estimated image, determination of second estimated scatter based on the first TOF data and the attenuated unscattered true coincidences, and reconstruction of an image of the object based on the first TOF data and the second estimated scatter.

Abstract: Systems and methods include acquisition of data representing true coincidences and scatter coincidences detected by the plurality of detectors, allocation of the data into respective ones of a plurality of energy ranges, determination of a baseline response associated with each of a subset of the plurality of energy ranges, generation of data representing expected true coincidences associated with each of the subset of the plurality of energy ranges based on the data allocated to each of the subset of the plurality of energy ranges and the baseline response associated with each of the subset of the plurality of energy ranges, determination of a raw scatter estimate based on the data representing expected true coincidences associated with each of the subset of the plurality of energy ranges and the data allocated to each of the subset of the plurality of energy ranges, and reconstruction of an image based on the raw scatter estimate and the data representing true coincidences and scatter coincidences.

Abstract: Systems and methods include acquisition of data representing true coincidences and scatter coincidences detected by the plurality of detectors, allocation of the data into respective ones of a plurality of energy ranges, determination of a baseline response associated with each of a subset of the plurality of energy ranges, generation of data representing expected true coincidences associated with each of the subset of the plurality of energy ranges based on the data allocated to each of the subset of the plurality of energy ranges and the baseline response associated with each of the subset of the plurality of energy ranges, determination of a raw scatter estimate based on the data representing expected true coincidences associated with each of the subset of the plurality of energy ranges and the data allocated to each of the subset of the plurality of energy ranges, and reconstruction of an image based on the raw scatter estimate and the data representing true coincidences and scatter coincidences.

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.

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.