Systems and methods of list-mode image reconstruction in positron emission tomography (pet) systems

An image reconstruction computing device for a positron emission tomography (PET) system, comprising at least one processor electrically coupled with at least one memory device, and the at least one processor programmed to:

• receive event data acquired by a PET system, wherein the PET system includes a detector ring assembly including a plurality of detectors, each detector includes one or more photosensors, the event data are represented as a list of coincidence events, wherein a coincidence event occurs when a positron emitted by radionuclide collides with an electron and a pair of photons are emitted due to collision and annihilation of the positron and the electron, and the pair of photons are detected by the plurality of detectors;
• generate groups of coincidence events based on the event data, each group having a number of coincidence events in the event data and being representative of the event data;
• perform a first level of image updates by iteratively updating a reconstructed image for a first number of times equal to a first number of image updates, wherein each image update is based on a first number of groups, the first number of groups including a first number of coincidence events;
• perform a second level of image updates by iteratively updating the reconstructed image for a second number of times equal to a second number of image updates, wherein each image update is based on a second number of groups, the second number of groups including a second number of coincidence events, and the first number of coincidence events being different from the second number of coincidence events;
• and
• output the reconstructed image.

An image reconstruction computing device, comprising at least one processor electrically coupled with at least one memory device, and the at least one processor programmed to:

• receive event data acquired by a medical imaging system, wherein the event data are represented as a list of coincidence events;
• generate groups of coincidence events based on the event data, each group having a number of coincidence events in the event data and being representative of the event data;
• perform a first level of image updates by iteratively updating a reconstructed image for a first number of times equal to a first number of image updates, wherein each image update is based on a first number of groups, the first number of groups including a first number of coincidence events;
• perform a second level of image updates by iteratively updating the reconstructed image for a second number of times equal to a second number of image updates, wherein each image update is based on a second number of groups, the second number of groups including a second number of coincidence events, and the first number of coincidence events being different from the second number of coincidence events;
• and
• output the reconstructed image.