Fundamental study on improving the quality of X-ray fluorescence computed tomography images by applying deep image prior to projection images as a pre-denoising method.

Purpose: We are developing a three-dimensional X-ray fluorescence computed tomography (3D XFCT) system using non-radioactive-labeled compounds for preclinical studies as a new modality that provides images of biological functions. Improvements in image quality and detection limits are required for the in vivo imaging. The aim of this study was to improve the quality of XFCT images by applying a deep image prior (DIP), which is a type of convolutional neural network, to projection images as a pre-denoising method, and then compare with DIP post-denoising.