Deep Image Prior PET Reconstruction using a SIRF-Based Objective

Widespread adoption of deep learning in medical imaging has been hampered, in part, due to a lack of integration with clinically applicable software. In this work, we establish a direct connection between an established PET reconstruction suite, SIRF, and PyTorch. This allows for advanced reconstruction methodologies to be deployed on clinical data with an unsupervised deep learning approach: the Deep Image Prior. Results show consistent quality metrics for DIP in comparison to OSMAP.