Abstract:Purpose To propose and validate a total-body PET (TB-PET) guided deep progressive learning method (DPR) for low-dose clinical imaging of standard axial field-of-view PET/CT scanner (SAFOV-PET).Methods List-mode raw data from a total of 182 scans were collected, including 100 patient scans from a TB-PET, and 15 phantom and 67 patient scans from a SAFOV-PET. Neural networks employed in DPR were trained with the high-quality images obtained from the TB-PET using a progressive learning strategy and evaluated on a … Show more
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