Predicting FDG PET from Multi-contrast MRIs using Deep Learning in Patients with Brain Neoplasms

Le résumé fourni par la source

PET is a widely used imaging technique, but it requires exposing subjects to radiation and is not offered in the majority of medical centers in the world. Here, we proposed to synthesize FDG-PET images from multi-contrast MR images using a U-Net-based network with attention modules and transformer blocks. The experiments on a dataset with 87 brain lesions in 59 patients demonstrated that the proposed method was able to generate high-quality PET from MR images without the need for radiotracer injection. We also demonstrate methods to handle potential missing or corrupted sequences.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.