Among many molecular imaging modalities, Bioluminescence tomography (BLT) is an important optical molecular
imaging modality. Due to its unique advantages in specificity, sensitivity, cost-effectiveness and low background noise,
BLT is widely studied for live small animal imaging. Since only the photon distribution over the surface is measurable
and the photo propagation with biological tissue is highly diffusive, BLT is often an ill-posed problem and may bear
multiple solutions and aberrant reconstruction in the presence of measurement noise and optical parameter mismatches.
For many BLT practical applications, such as early detection of tumors, the volumes of the light sources are very small
compared with the whole body. Therefore, the L1-norm sparsity regularization has been used to take advantage of the
sparsity prior knowledge and alleviate the ill-posedness of the problem. Iterative shrinkage (IST) algorithm is an
important research achievement in a field of compressed sensing and widely applied in sparse signal reconstruction.
However, the convergence rate of IST algorithm depends heavily on the linear operator. When the problem is ill-posed,
it becomes very slow. In this paper, we present a sparsity regularization reconstruction method for BLT based on the
two-step iterated shrinkage approach. By employing Two-step strategy of iterative reweighted shrinkage (IRS) to
improve IST, the proposed method shows faster convergence rate and better adaptability for BLT. The simulation
experiments with mouse atlas were conducted to evaluate the performance of proposed method. By contrast, the
proposed method can obtain the stable and comparable reconstruction solution with less number of iterations.