21 December 2016 Self-guided reconstruction for time-domain fluorescence molecular lifetime tomography
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Abstract
Fluorescence probes have distinct yields and lifetimes when located in different environments, which makes the reconstruction of fluorescence molecular lifetime tomography (FMLT) challenging. To enhance the reconstruction performance of time-domain (TD) FMLT with heterogeneous targets, a self-guided L 1 regularization projected steepest descent (SGL1PSD) algorithm is proposed. Different from other algorithms performed in time domain, SGL1PSD introduces a time-resolved strategy into fluorescence yield reconstruction. The algorithm consists of four steps. Step 1 reconstructs the initial yield map with full time gate strategy; steps 2–4 reconstruct the inverse lifetime map, the yield map, and the inverse lifetime map again with time-resolved strategy, respectively. The reconstruction result of each step is used as a priori for the reconstruction of the next step. Projected iterated Tikhonov regularization algorithm is adopted for the yield map reconstructions in steps 1 and 3 to provide a solution with iterative refinement and nonnegative constraint. The inverse lifetime map reconstructions in steps 2 and 4 are based on L 1 regularization projected steepest descent algorithm, which employ the L 1 regularization to reduce the ill-posedness of the high-dimensional nonlinear problem. Phantom experiments with heterogeneous targets at different edge-to-edge distances demonstrate that SGL1PSD can provide high resolution and quantification accuracy for TD FMLT.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Chuangjian Cai, Wenjuan Cai, Jiaju Cheng, Yuxuan Yang, Jianwen Luo, "Self-guided reconstruction for time-domain fluorescence molecular lifetime tomography," Journal of Biomedical Optics 21(12), 126012 (21 December 2016). https://doi.org/10.1117/1.JBO.21.12.126012 . Submission: Received: 11 September 2016; Accepted: 30 November 2016
Received: 11 September 2016; Accepted: 30 November 2016; Published: 21 December 2016
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