System and method for on-the-fly noise equivalent count estimation for positron emission tomography imaging

A system for estimating noise equivalent counts, comprising:

• a memory encoding processor-executable routines;
• a processor configured to access the memory and to execute the processor-executable routines, wherein the processor-executable routines, when executed by the processor, cause the processor to:
• one or more times during a scan of an object with a positron emission tomography (PET) scanner, a plurality of coincidence events are detected by a detector array of the PET scanner:
• obtain a total of the plurality of coincidence events detected by the detector array;
• estimate random coincidence events detected by the detector array;
• estimate scatter coincidence events detected by the detector array;
• estimate true coincidence events detected by the detector array based on the total of the plurality of coincidence events, the estimated random coincidence events, and the estimated scatter coincidence events;
• determine a scatter fraction based on the estimated scatter events and the estimated true coincidence events;
• estimate noise equivalent counts based at least on the scatter fraction, the total of the plurality of coincidence events, the estimated true coincidence events, the estimated scatter coincidence events, and the estimated random coincidence events;
• and
• obtain a pre-computed mask of the object imaged during the scan, wherein the pre-computed mask is in sinogram space.

A non-transitory computer-readable medium, the non-transitory computer-readable medium comprising processor-executable code that when executed by a processor, causes the processor to:

• one or more times during a scan of an object with a positron emission tomography (PET) scanner, a plurality of coincidence events are detected by a detector array of the PET scanner:
• obtain a total of the plurality of coincidence events detected by the detector array;
• generate an emission sinogram;
• estimate random coincidence events detected by the detector array and generate a random sinogram based on the estimated random coincidence events;
• estimate scatter coincidence events detected by the detector array by inputting the emission sinogram and an attenuation correction factor sinogram into a trained deep learning-based scatter estimation model and outputting from the deep learning-based scatter estimation model a scatter sinogram;
• determine the estimated random coincidence events that have passed through the object during the scan by applying a pre-computed mask of the object to the random sinogram, wherein the pre-computed mask is in sinogram space;
• determine the estimated scatter coincidence events that have passed through the object during the scan by applying the pre-computed mask to the scatter sinogram;
• estimate true coincidence events detected by the detector array based on the total of the plurality of coincidence events, the estimated random coincidence events, and the estimated scatter coincidence events;
• determine a scatter fraction based on the estimated scatter events and the estimated true coincidence events;
• and
• estimate the noise equivalent counts based on the scatter fraction, the total of the plurality of coincidence events, the estimated random coincidence events, the estimated true coincidence events, the estimated scatter coincidence events that have passed through the object during the scan, and the estimated random coincidence events that have passed through the object during the scan.