Probability method for Cerenkov luminescence tomography based on conformance error minimization

• Published in: Biomedical optics express Vol. 5; no. 7; pp. 2091 - 2112
• Main Authors: Ding, Xintao, Wang, Kun, Jie, Biao, Luo, Yonglong, Hu, Zhenhua, Tian, Jie
• Format: Journal Article
• Language: English
• Published: United States Optical Society of America 01.07.2014
• Subjects: (170.3010) Image reconstruction techniques; (170.6280) Spectroscopy, fluorescence and luminescence; (100.3190) Inverse problems; (110.6960) Tomography; (170.3880) Medical and biological imaging; (170.3660) Light propagation in tissues
• ISSN: 2156-7085; 2156-7085
• DOI: 10.1364/BOE.5.002091

Cerenkov luminescence tomography (CLT) was developed to reconstruct a three-dimensional (3D) distribution of radioactive probes inside a living animal. Reconstruction methods are generally performed within a unique framework by searching for the optimum solution. However, the ill-posed aspect of the inverse problem usually results in the reconstruction being non-robust. In addition, the reconstructed result may not match reality since the difference between the highest and lowest uptakes of the resulting radiotracers may be considerably large, therefore the biological significance is lost. In this paper, based on the minimization of a conformance error, a probability method is proposed that consists of qualitative and quantitative modules. The proposed method first pinpoints the organ that contains the light source. Next, we developed a 0-1 linear optimization subject to a space constraint to model the CLT inverse problem, which was transformed into a forward problem by employing a region growing method to solve the optimization. After running through all of the elements used to grow the sources, a source sequence was obtained. Finally, the probability of each discrete node being the light source inside the organ was reconstructed. One numerical study and two in vivo experiments were conducted to verify the performance of the proposed algorithm, and comparisons were carried out using the hp-finite element method (hp-FEM). The results suggested that our proposed probability method was more robust and reasonable than hp-FEM.