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Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction

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Abstract

For fluorescence molecular tomography (FMT), image quality could be improved by incorporating a sparsity constraint. The L1 norm regularization method has been proven better than the L2 norm, like Tikhonov regularization. However, the Tikhonov method was found capable of achieving a similar quality at a high iteration cost by adopting a zeroing strategy. By studying the reason, a Tikhonov-regularization-based projecting sparsity pursuit method was proposed that reduces the iterations significantly and achieves good image quality. It was proved in phantom experiments through time-domain FMT that the method could obtain higher accuracy and less oversparsity and is more applicable for heterogeneous-target reconstruction, compared with several regularization methods implemented in this Letter.

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DOI:10.3788/COL202018.011701

所属栏目:Biomedical Optics

基金项目:This work was supported by the National Natural Science Foundation of China (Nos. 81561168023, 61871251, and 61871022).

收稿日期:2019-09-09

录用日期:2019-09-29

网络出版日期:2019-12-23

作者单位    点击查看

Jiaju Cheng:Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
Jianwen Luo:Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China

联系人作者:Jianwen Luo(luo_jianwen@tsinghua.edu.cn)

备注:This work was supported by the National Natural Science Foundation of China (Nos. 81561168023, 61871251, and 61871022).

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引用该论文

Jiaju Cheng, Jianwen Luo, "Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction," Chinese Optics Letters 18(1), 011701 (2020)

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