spectrally resolved bioluminescence tomography with huber-markov random-field regularization
Bioluminescence tomography (BLT) is an emerging imaging technique for earlier disease detection, drug discovery, etc. However, BLT reconstruction is a severely ill-posed problem. Since multispectral information provides the depth information of bioluminescent source and improves the reconstruction quality, therefore, multispectral BLT has attracted much attention. In the paper, a Huber-Markov random-field regularization for multispectral BLT is incorporated into a Bayesian framework to reduce the ill-posedness of BLT and localize the bioluminescent source accurately. For practical application, the optimal permissible source region was used to reduce the ill-posedness and improve the computational efficiency. Finally, the performance of the proposed algorithm was validated by a heterogeneous 3-D micro-CT atlas and a mouse-shaped phantom. Reconstructed results demonstrate feasibility of the proposed algorithm.
bioluminescence tomography bayesian approach