Source reconstruction for spectrally-resolved bioluminescence tomography with sparse a priori information.
Through restoration of the light source information in small animals in vivo, optical molecular imaging, such as fluorescence molecular tomography (FMT) and bioluminescence tomography (BLT), can depict biological and physiological changes observed using molecular probes. A priori information plays an indispensable role in tomographic reconstruction. As a type of a priori information, the sparsity characteristic of the light source has not been sufficiently considered to date. In this paper, we introduce a compressed sensing method to develop a new tomographic algorithm for spectrally-resolved bioluminescence tomography. This method uses the nature of the source sparsity to improve the reconstruction quality with a regularization implementation. Based on verification of the inverse crime, the proposed algorithm is validated with Monte Carlo-based synthetic data and the popular Tikhonov regularization method. Testing with different noise levels and single/multiple source settings at different depths demonstrates the improved performance of this algorithm. Experimental reconstruction with a mouse-shaped phantom further shows the potential of the proposed algorithm.
Lu
Yujie
Y
David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA. ylu@mednet.ucla.edu
Zhang
Xiaoqun
X
Douraghy
Ali
A
Stout
David
D
Tian
Jie
J
Chan
Tony F
TF
Chatziioannou
Arion F
AF
D000818:Animals; D003198:Computer Simulation; D049452:Green Fluorescent Proteins / Q000378:metabolism; D008163:Luminescent Measurements / Q000379:methods*; D008164:Luminescent Proteins / Q000378:metabolism; D051379:Mice; D009010:Monte Carlo Method; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D013057:Spectrum Analysis; D014054:Tomography / Q000379:methods*
D008164:Luminescent Proteins; C413663:fluorescent protein 583; D049452:Green Fluorescent Proteins