The sparse estimation methods that utilize the -norm, with being between 0 and 1, have shown better utility in providing optimal solutions to the inverse problem in diffuse optical tomography. These -norm-based regularizations make the optimization function nonconvex, and algorithms that implement -norm minimization utilize approximations to the original -norm function. In this work, three such typical methods for implementing the -norm were considered, namely, iteratively reweighted -minimization (IRL1), iteratively reweighted least squares (IRLS), and the iteratively thresholding method (ITM). These methods were deployed for performing diffuse optical tomographic image reconstruction, and a systematic comparison with the help of three numerical and gelatin phantom cases was executed. The results indicate that these three methods in the implementation of -minimization yields similar results, with IRL1 fairing marginally in cases considered here in terms of shape recovery and quantitative accuracy of the reconstructed diffuse optical tomographic images.
© 2014 Optical Society of AmericaFull Article | PDF Article
Phaneendra K. Yalavarthy, Brian W. Pogue, Hamid Dehghani, Colin M. Carpenter, Shudong Jiang, and Keith D. Paulsen
Opt. Express 15(13) 8043-8058 (2007)
Shinpei Okawa, Yoko Hoshi, and Yukio Yamada
Biomed. Opt. Express 2(12) 3334-3348 (2011)
Sree Harsha Katamreddy and Phaneendra K. Yalavarthy
J. Opt. Soc. Am. A 29(5) 649-656 (2012)