Source reconstruction for spectrally-resolved bioluminescence tomography with sparse A priori information

Yujie Lu, Xiaoqun Zhang, Ali Douraghy, David Stout, Jie Tian, Tony Chan, Arion F. Chatziioannou

Research output: Contribution to journalArticlepeer-review

106 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)8062-8080
Number of pages19
JournalOptics Express
Volume17
Issue number10
DOIs
StatePublished - May 11 2009

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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