Dose Reduction in Dynamic PET Imaging Using Self-Supervised Two-Step Deep Image Prior

Le résumé fourni par la source

Reducing the radiotracer dose in dynamic brain PET imaging lowers patient radiation exposure but also degrades the signal-to-noise (SNR) ratio and introduces bias in both dynamic frames and resulting parametric images. These effects compromise the accuracy of quantitative assessments of target proteins. In this study, we propose a self-supervised two-step deep image prior (TS-DIP) method that integrates composite image-based pretraining with individual frame-based fine-tuning to enhance reduced-dose dynamic brain PET imaging. We evaluated the TS-DIP method on 1/10-count dynamic datasets acquired using two tracers with distinct uptake patterns,11C-UCB-J (targeting synaptic density) and11C-LSN3172176 (targeting muscarinic cholinergic receptors). Compared with conventional, supervised deep learning, and conditional DIP denoising approaches, the TS-DIP demonstrated a superior tradeoff between noise reduction and bias in both dynamic frames and parametric images. Averaged across all dynamic frames of11C-UCB-J subjects, the proposed method achieved a 75% reduction in noise with an bias of 2.1%, corresponding to 63% and 60% noise reduction in theK1(bias, -1.2%) andVT(bias, -3.1%) parametric images, respectively. For11C-LSN3172176 subjects, the average noise reductions (bias) were 80% (3.4%) in dynamic frames, 62%(-0.38%) inK1images, and 77% (-3.4%) inVTimages. These results highlight the generalizability of the proposed TS-DIP method across tracers with varying activity distributions and its potential to enable accurate, reduced-dose dynamic brain PET imaging without the need for large training datasets.

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