Diffuse Optical Tomography Enhanced by Clustered Sparsity for Functional Brain Imaging

Diffuse optical tomography (DOT) is a noninvasive technique which measures hemodynamic changes in the tissue with near infrared light, which has been increasingly used to study brain functions. Due to the nature of light propagation in the tissue, the reconstruction problem is severely ill-posed. For linearized DOT problems, sparsity regularization has achieved promising results over conventional Tikhonov regularization in recent experimental research. As extensions to standard sparsity, it is widely known that structured sparsity based methods are often superior in terms of reconstruction accuracy, when the data follows some structures. In this paper, we exploit the structured sparsity of diffuse optical images. Based on the functional specialization of the brain, it is observed that the in vivo absorption changes caused by a specific brain function would be clustered in certain region(s) and not randomly distributed. Thus, a new algorithm is proposed for this clustered sparsity reconstruction (CSR). Results of numerical simulations and phantom experiments have demonstrated the superiority of the proposed method over the state-of-the-art methods. An example from human in vivo measurements further confirmed the advantages of the proposed CSR method.