【摘 要】
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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
【机 构】
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DepartmentofBiomedicalEngineering,SchoolofMedicine,TsinghuaUniversity,Beijing100084,China
【出 处】
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ChineseOpticsLetters
论文部分内容阅读
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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