Abstract: Data processing systems (DPS) and related methods for nuclear medicine imaging. At an input interface (IN), first projection data (?), or a first image (V) reconstructable from the first projection data, is received. The first projection data is associated with a first waiting period (?T*). The first waiting period indicates the time period from administration of a tracer agent to a start of acquisition by a nuclear medicine imaging apparatus (IA) of the projection date. A trained machine learning module (MLM) estimates, based on the first projection data (?) or on the first image (V), a second projection data (??) or a second image (V?) associable with a second waiting period (?T), longer than the first waiting period (?T*). Nuclear imaging can thus be conducted quicker. Similar machine learning based data processing systems and related methods are also envisaged to reduce acquisition time periods or the time it takes to reconstruct imagery.

Abstract: The invention refers to an apparatus that allows to improve the image quality of nuclear images, e.g. PET images. The apparatus (110) comprises a providing unit (111) for providing nuclear image data of a region of interest, a providing unit (112) for providing a motion signal indicative of a motion of the region of interest, a determination unit (113) for determining different motion states of the region of interest based on the motion signal, a determination unit (114) for determining for each motion state nuclear image data corresponding to the motion state, a reconstruction unit (115) for reconstructing an absorption map for each motion state based on the corresponding nuclear image data of the respective motion state, and a reconstruction unit (116) for reconstructing one or more nuclear images of the region of interest based on the nuclear image data and the absorption maps reconstructed for each motion state.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (181, 182, 20) to perform an imaging method (100). The method includes: reconstructing emission imaging data to generate an emission image of a lesion; converting intensity values of the emission image to at least one standardized uptake value (SUV value) for the lesion; processing input data using a regression neural network (NN) (28) to output an SUV correction factor for the lesion, wherein the input data includes at least two of (i) image data comprising the emission image or a feature vector representing the emission image, (ii) the at least one SUV value, (iii) a size of the lesion, and (iv) reconstruction parameters used in the reconstructing; and controlling a display device (24) to display at least one of (I) the SUV correction factor and (II) a corrected SUV value generated by applying the SUV correction factor to the at least one SUV value.