Comprehensive study of methods for automatic choice of regularization parameter for diffuse optical tomography

Zhonghua Sun; Yaqi Wang; Kebin Jia; Jinchao Feng

doi:10.1117/1.OE.56.4.041310

23 December 2016 Comprehensive study of methods for automatic choice of regularization parameter for diffuse optical tomography

Zhonghua Sun, Yaqi Wang, Kebin Jia, Jinchao Feng

Author Affiliations +

Funded by: NSFC, Natural Science Foundation of China, Beijing Natural Science Foundation, BNSF, Beijing Nova Program, BNP, STPBMEC, Science Technology Project of Beijing Municipal Education Commission, Postdoctoral Science Foundation of China, PSFC, BJUT, Basic Research Fund of Beijing University of Technology

Optical Engineering, Vol. 56, Issue 4, 041310 (December 2016). https://doi.org/10.1117/1.OE.56.4.041310

Abstract

The image reconstruction in diffuse optical tomography (DOT) is a typical inverse problem; therefore, regularization techniques are essential to obtain a reliable solution. The most general form of regularization is Tikhonov regularization. With any Tikhonov regularized reconstruction algorithm, one of the crucial issues is the selection of the regularization parameter that controls the trade-off between the regularized solution and fidelity to the given sets of data. Automatic methods such as L-curve, generalized cross-validation, minimal residual method, projection error method, and model function method have been introduced to select the regularization parameter over the years. However, little investigation of comparison of all the algorithms has been reported in DOT. The performance of the five methods for choosing regularization parameter is comprehensively compared, and advantages and limitations are discussed.

Citation Download Citation

Zhonghua Sun, Yaqi Wang, Kebin Jia, and Jinchao Feng "Comprehensive study of methods for automatic choice of regularization parameter for diffuse optical tomography," Optical Engineering 56(4), 041310 (23 December 2016). https://doi.org/10.1117/1.OE.56.4.041310

Received: 10 October 2016; Accepted: 5 December 2016; Published: 23 December 2016

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