| Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction |
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| 作者姓名： | 成家驹 罗建文 |
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| 作者单位： | Department of Biomedical Engineering |
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| 基金项目： | supported by the National Natural Science Foundation of China(Nos.81561168023,61871251,and 61871022). |
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| 摘 要： | 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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| 关 键 词： | FLUORESCENCE molecular TOMOGRAPHY SPARSITY PURSUIT Tikhonov REGULARIZATION GOOD image quality high efficiency |
Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction |
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| Jiaju Cheng,Jianwen Luo.Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction[J].中国光学快报(英文版),2020(1):64-69. |
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| Authors: | Jiaju Cheng Jianwen Luo |
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| Institution: | (Department of Biomedical Engineering,School of Medicine,Tsinghua University,Beijing 100084,China) |
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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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