Efficient image reconstruction for fluorescence molecular tomography via linear regression approximation scheme with dual augmented Lagrangian method With the development of noncontact fluorescence molecular tomography (FMT) imaging system, multifluorescence projections data can be obtained to improve the quality of reconstruction images. However, it remains a challenging issue to obtain fast and accurate reconstruction of the fluorescent probe distribution due to the large computational burden and the illposed nature of the inverse problem. In this work, we present an innovative method associating dual augmented lagrangian method (DALM) with a linear regression approximation (LRA) strategy to locate the fluorescence probe, which guarantees the accuracy, efficiency, and robustness for FMT reconstruction. Numerical experiments based on a heterogeneous phantom are performed to validate the feasibility of the proposed method. The results demonstrate that the proposed method can achieve accurate target localization, and satisfactory computational efficiency. Furthermore, this approach is robust even under quite illposed condition.

A MajorizeMinimize memory gradient algorithm applied to Xray tomography Some recent efforts on image restoration have been extended to tomographic reconstruction methods with apriori information so that situations with higher amounts of noise or lesser amounts of projections could be better dealt with. This paper deals with the reconstruction of a two or threedimensional image from a small set of tomographic projections (i.e. a small number of projection angles). We adopt a variational reconstruction approach, with smooth regularization on the image gradient for reconstruction. The MajorizeMinimize Memory Gradient algorithm is our proposed solution for the optimization problem. The performance of the proposed strategy is illustrated through synthetic and real image reconstruction examples. The reconstruction error is investigated for convex and nonconvex penalization strategies, as a function of the number of tomographic measurements and the noise level. The nonconvex approach turns out to be more efficient in the case of binary images. In contrast, convex penalization seems to give better results for multilabel or natural images.