Effects of Loss Functions and Supervision Methods on Total-Body PET Denoising

Abstract

Introduction of the total-body positron emission tomography (TB PET) system is a remarkable advancement in noninvasive imaging, improving annihilation photon detection sensitivity and bringing the quality of positron emission tomography (PET) images one step closer to that of anatomical images. This enables reduced scan times or radiation doses and can ultimately improve other PET images through denoising. This study investigated the effect of loss functions: mean squared error (MSE), Poisson negative log-likelihood derived from the Poisson statistics of radiation activity, and L1 derived from the histogram of count differences between the full and partial scans. Furthermore, the effect of supervision methods, comparing supervised denoising, self-supervised denoising, and interpolation of input and self-supervised denoising based on dependency relations of the partial and full scans are explored. The supervised denoising method using the L1 norm loss function shows high-denoising performance regardless of harsh denoising conditions, and the interpolated self-supervised denoising using MSE loss preserves local features.