Deep dual-domain based framework for PET image reconstruction

被引:0
作者
Gao, Xinrui [1 ]
Chen, Yunmei [2 ]
Hu, Rui [1 ]
Chen, Mengrui [1 ]
Liu, Huafeng [1 ]
机构
[1] Zhejiang Univ, Coll Opt Sci & Engn, State Key Lab Extreme Photon & Instrumentat, Hangzhou 310027, Peoples R China
[2] Univ Florida, Dept Math, Gainesville, FL 32611 USA
来源
MEDICAL IMAGING 2024: PHYSICS OF MEDICAL IMAGING, PT 1 | 2024年 / 12925卷
基金
美国国家科学基金会; 中国国家自然科学基金;
关键词
Positron emission tomography (PET); Dual-domain learning algorithm; Alternating iterative minimization;
D O I
10.1117/12.3006448
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Positron emission tomography (PET) is an advanced nuclear medicine imaging technique widely used in clinical diagnostics such as neurology and oncology. In PET image reconstruction, the widespread adoption of deep learning is attributed to its potent feature extraction capabilities. However, the challenge lies in ensuring that the employed network is interpretable and rational. Additionally, addressing the intricate issue of achieving superior results with a smaller training set remains a formidable task. In this paper, we propose a novel alternating learning dual-domain reconstruction algorithm. This method combines the likelihood function based on the PET imaging model with a learnable dual-domain regularization term as a composite objective. The objective function is minimized through alternating iterations to obtain reconstructed activity image and denoised sinogram. The iterative process enhances the convergence speed by integrating residual structures, and the assurance of result convergence is facilitated through the imposition of judgment conditions. Experimental results demonstrate that our method surpasses OSEM and DeepPET in terms of SSIM and PSNR.
引用
收藏
页数:8
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