A multithread based new sparse matrix method in bioluminescence tomography

Among many molecular imaging modalities, bioluminescence tomography (BLT) stands out as an effective approach for in vivo imaging because of its noninvasive molecular and cellular level detection ability, high sensitivity and low cost in comparison with other imaging technologies. However, there exists the case that large scale problem with large number of points and elements in the structure of mesh standing for the small animal or phantom. And the large scale problem's system matrix generated by the diffuse approximation (DA) model using finite element method (FEM) is large. So there wouldn't be enough random access memory (RAM) for the program and the related inverse problem couldn't be solved. Considering the sparse property of the BLT system matrix, we've developed a new sparse matrix (ZSM) to overcome the problem. And the related algorithms have all been speeded up by multi-thread technologies. Then the inverse problem is solved by Tikhonov regularization method in adaptive finite element (AFE) framework. Finally, the performance of this method is tested on a heterogeneous phantom and the boundary data is obtained through Monte Carlo simulation. During the process of solving the forward model, the ZSM can save more processing time and memory space than the usual way, such as those not using sparse matrix and those using Triples or Cross Linked sparse matrix. Numerical experiments have shown when more CPU cores are used, the processing speed is increased. By incorporating ZSM, BLT can be applied to large scale problems with large system matrix.