Title: Non-Convex Sparse Regularization Approach Framework for High Multiple-Source Resolution in Cerenkov Luminescence Tomography
|
| Authors: Guo, HB; Hu, ZH; He, XW; Zhang, XJ; Liu, MH; Zhang, ZY; Shi, XJ; Zheng, S; Tian, J
|
| Author Full Names: Guo, Hongbo; Hu, Zhenhua; He, Xiaowei; Zhang, Xiaojun; Liu, Muhan; Zhang, Zeyu; Shi, Xiaojing; Zheng, Sheng; Tian, Jie
|
| Source: OPTICS EXPRESS, 25 (23):28068-28085; 10.1364/OE.25.028068 NOV 13 2017
|
| Language: English
|
| Abstract: With the help of the clinical application of CLI in tumour and lymph node imaging, Cerenkov luminescence tomography (CLT) has the potential to be used for cancer staging. If staging cancer based on optical image of tumour, node and metastasis, one of the critical issues is multiple-source resolution. Because of the ill-posedness of the inverse problem and the diversity of tumor biological characteristics, the multiple-source resolution is a meaningful but challenge problem. In this paper, based on the compression perception theory, a non-convex sparse regularization algorithm (nCSRA) framework was proposed to improve the capacity of multiple-source resolving. Two typical algorithms (homotopy and iterative shrinkage-thresholding algorithm) were explored to test the performance of nCSRA. In numerical simulations and in vivo imaging experiments, the comparison results showed that the proposed nCSRA framework can significantly enhance the multiple-source resolution capability in aspect of spatial resolution, intensity resolution, and size resolution. (C) 2017 Optical Society of America
|
| ISSN: 1094-4087
|
| IDS Number: FM6CR
|
| Unique ID: WOS:000415136700008
*Click Here to View Full Record
