Parametric Reconstruction of Diffuse Optical Tomography Using Gaussian Mixture Model and Genetic Algorithm

The inverse problem of diffused optical tomography is highly ill-posed as well as often nonunique and unstable. The ill-posedness can be reduced by reducing the number of unknown values (e.g., adding soft or hard-prior constraints). In this paper, this has been achieved by parameterization of the tissue optical properties. The optical parameters of the tissue have been modeled by a Gaussian mixture model (GMM) of eight components, and the image is reconstructed by estimating the parameters of the mixture model. Therefore, the number of unknowns is drastically reduced while maintaining some amount of generality by using an 8 component GMM. Unlike conventional GMM, the parameters are estimated using the Genetic algorithm in a multi-step predictor-corrector model. A new cost function is proposed using mean square error and asymmetry measurement of detected intensity. Object centroid error, structural similarity index, maximum error and normalized root mean squared error of reconstructed absorption coefficients are evaluated for performance assessment and comparison. Extensive numerical and experimental results have been illustrated to show the potential of the proposed method. Use of CW (continuous wave) measurement and single imaging modality make the system cost effective and portable.