分子影像学杂志2026，Vol.49Issue(8)：981-989,9.DOI:10.12122/j.issn.1674-4500.2026.08.01
基于改进深度图像先验的PET图像部分容积校正方法
Partial volume correction in PET imaging using improved deep image prior
摘要
Abstract
Objective To propose an unsupervised few-shot PET partial volume correction method based on an improved deep image prior.Methods Propose a novel PET partial volume correction method by enhancing the classical deep image prior framework.Specifically,population information is used for pre-training;the conventional loss function is replaced with a back-projection fidelity term combined with regularization by denoising;neural blind deconvolution is introduced to estimate the system point spread function;MRI anatomical features are fused via an attention mechanism to further improve correction performance.Results Validation experiments were carried out on PET-SORTEO simulation dataset and ADNI clinical dataset.The test results of quantitative indicators showed that the PSNR,SSIM,CNR,CLIP and NIQE of the proposed method achieved superior performance over the compared approaches.Conclusion The proposed method,built upon the modified deep image prior for PET partial volume correction,effectively enhances the image quality of PET images.关键词
部分容积校正/深度图像先验/神经盲反卷积/注意力网络Key words
partial volume correction/deep image prior/neural blind deconvolution/attention network引用本文复制引用
刘政,高园园,傅蓉..基于改进深度图像先验的PET图像部分容积校正方法[J].分子影像学杂志,2026,49(8):981-989,9.基金项目
国家自然科学基金(32101108)Supported by National Natural Science Foundation of China(32101108). （32101108）
