期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 39, 期 10, 页码 3207-3217出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2020.2987640
关键词
Image reconstruction; Imaging; Mathematical model; Shape; Slabs; Iterative methods; Luminescence; Cerenkov luminescence tomography; sparse reconstruction; inverse problem; tumor
类别
资金
- National Key Research and Development Program of China [2017YFA0205200, 2016YFC0102600]
- National Natural Science Foundation of China (NSFC) [81930053, 61622117, 81671759, 81227901]
- Chinese Academy of Sciences [GJJSTD20170004]
- Scientific Instrument Developing Project of the Chinese Academy of Sciences [YZ201672]
- Key Research Program of the Chinese Academy of Sciences [KGZD-EW-T03]
- Beijing Natural Science Foundation [JQ19027]
- Beijing Nova Program [Z181100006218046]
- innovative research team of highlevel local universities in Shanghai
- Zhuhai High-level Health Personnel Team Project (Zhuhai) [HLHPTP201703]
Cerenkov luminescence tomography (CLT) is a promising imaging tool for obtaining three-dimensional (3D) non-invasive visualization of the in vivo distribution of radiopharmaceuticals. However, the reconstruction performance remains unsatisfactory for biomedical applications because the inverse problem of CLT is severely ill-conditioned and intractable. In this study, therefore, a novel non-negative iterative convex refinement (NNICR) approach was utilized to improve the CLT reconstruction accuracy, robustness as well as the shape recovery capability. The spike and slab prior information was employed to capture the sparsity of Cerenkov source, which could be formalized as a non-convex optimization problem. The NNICR approach solved this non-convex problem by refining the solutions of the convex sub-problems. To evaluate the performance of the NNICR approach, numerical simulations and in vivo tumor-bearing mice models experiments were conducted. Conjugated gradient based Tikhonov regularization approach (CG-Tikhonov), fast iterative shrinkage-thresholding algorithm based Lasso approach (Fista-Lasso) and Elastic-Net regularization approach were used for the comparison of the reconstruction performance. The results of these experiments demonstrated that the NNICR approach obtained superior reconstruction performance in terms of location accuracy, shape recovery capability, robustness and in vivo practicability. It was believed that this study would facilitate the preclinical and clinical applications of CLT in the future.
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