Low-count whole-body PET/MRI restoration: an evaluation of dose reduction spectrum and five state-of-the-art artificial intelligence models.

ABSTRACT:

Purpose

To provide a holistic and complete comparison of the five most advanced AI models in the augmentation of low-dose ¹⁸F-FDG PET data over the entire dose reduction spectrum.

Methods

In this multicenter study, five AI models were investigated for restoring low-count whole-body PET/MRI, covering convolutional benchmarks - U-Net, enhanced deep super-resolution network (EDSR), generative adversarial network (GAN) - and the most cutting-edge image reconstruction transformer models in computer vision to date - Swin transformer image restoration network (SwinIR) and EDSR-ViT (vision transformer). The models were evaluated against six groups of count levels representing the simulated 75%, 50%, 25%, 12.5%, 6.25%, and 1% (extremely ultra-low-count) of the clinical sta

SUBMITTER: Wang YJ 

PROVIDER: S-EPMC10387227 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature