Modified CS-MUSIC for diffuse optical tomography using joint sparsity

Abstract

In this paper, a modified compressive multiple signal classification (CS-MUSIC) algorithm has been proposed for diffuse optical tomography (DOT) reconstruction. The basic principle involved in DOT is that it illuminates the biological tissue using near infrared light and reconstructs the optical parameters of the tissue from the boundary measurements. The inverse problem of diffuse optical tomography is non-linear and severely ill-conditioned due to the zig-zag nature of light propagation by photons that diffuses through the tissue. Although nonlinear iterative methods are commonly used to solve this problem, they are computationally expensive since the forward problem has to be solved iteratively as well as they do not perform well for complex geometries. Recently, the DOT with compressive sensing (CS) has received a great attention due to its efficient possible reconstructions in DOT imaging. In this, the DOT inverse problem has been formulated as an multiple measurement vector (MMV) problem by using joint sparsity and CS frame work. The modified CS-MUSIC is a novel, non-iterative, and exact algorithm to reconstruct the absorption parameter change for Δα from the boundary data. In addition, this algorithm takes hybridization of sensor array signal processing and probabilistic compressive sensing. The experimental validation of the proposed algorithm has been done on a paraffin wax rectangular phantom through a DOT imaging setup. The performance metrics such as structural similarity index (SSIM), mean square error (MSE), normalized mean square error (NMSE) have been used to evaluate the performance of the reconstruction in this paper. Extensive numerical simulations show that the modified CS-MUSIC algorithm outperforms the current state-of-the-art algorithms and reliably reconstructs the absorption change in DOT.