Fluorescence Molecular Tomography Reconstruction Using Hybrid Regularization Method

Due to highly scattering of light in biological tissues, the inverse problem of fluorescent molecular tomography (FMT) is ill-posed by nature. Overcoming this difficulties requires regularization of the solution. Nevertheless, to choose a proper regularization parameter is not an easy task in practice. This work applies a hybrid regularization method in reconstruction. Instead of choosing a termination point for the original large-scale problem, this method achieves a reliable solution by inner Tikhonov regularization on a projected subspace problem. The choice of regularization parameter is realized in an iterative scheme. Numerical simulations are implemented to evaluate the performance of the algorithm. Results are also compared with those of truncated singular value decomposition (TSVD) and least square QR (LSQR). It is indicated that this hybrid method can reduce the computation time, as well as improve location accuracy of the fluorescence target in heterogeneous media.