A deep learning approach to correctly identify the sequence of coincidences in cross-strip CZT detectors

Nahid Nasiri; Shiva Abbaszadeh

doi:10.1117/12.2582063

15 February 2021 A deep learning approach to correctly identify the sequence of coincidences in cross-strip CZT detectors

Nahid Nasiri, Shiva Abbaszadeh

Author Affiliations +

Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 115953W (2021) https://doi.org/10.1117/12.2582063
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Intra-detector scatters (IRS) and Inter-detector scatters (IDS) are events that often happen in positron emission tomography (PET) due to the Compton scattering of an annihilation photon inside one detector block and also from one detector block to another. One challenge in PET system based on Cadmium zinc telluride (CZT) detectors is the high mass attenuation coefficient for Compton scattering at 511 keV that causes a considerable fraction of Multiple Interaction Photon Events (MIPEs). Besides, in a cross strip CZT detector, there is more ambiguity in pairing anode with its corresponding cathode in MIPEs in IRS. This study utilizes state-of-the-art in deep learning to correctly identify target sequences in cross-strip CZT detectors. It is promising to improve the system's sensitivity by identifying true line-of-responses (LOR)s out of different possible LORs from IRS events, IDS events, and Intra-detector ambiguity, which they are usually discarded.

Conference Presentation

Citation Download Citation

Nahid Nasiri and Shiva Abbaszadeh "A deep learning approach to correctly identify the sequence of coincidences in cross-strip CZT detectors", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115953W (15 February 2021); https://doi.org/10.1117/12.2582063

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