Three-dimensional bioluminescence tomography based on Bayesian approach.

Area of Science:

• Biomedical Imaging
• Computational Biology
• Medical Physics

Background:

• Bioluminescence tomography (BLT) is an inverse problem prone to ill-posedness due to limited measurements.
• Incorporating prior information is crucial for accurate BLT reconstruction.

Purpose of the Study:

• To develop a novel Bayesian approach for BLT reconstruction.
• To reduce the ill-posed nature of BLT using a generalized adaptive Gaussian Markov random field (GAGMRF) prior.
• To achieve accurate and efficient bioluminescent source localization.

Main Methods:

• A Bayesian framework was employed to integrate multiple types of prior information.
• A GAGMRF prior model was developed for source density estimation.
• Finite element analysis was utilized for complex heterogeneous phantoms.
• Source distribution was determined by maximizing log posterior probability.

Main Results:

• The algorithm demonstrated high computational efficiency.
• Accurate localization of bioluminescent sources was achieved.
• Validation was performed using 3-D micro-CT mouse atlas simulations and physical phantom experiments.

Conclusions:

• The proposed Bayesian approach with GAGMRF prior effectively addresses the ill-posedness in BLT.
• The method offers a viable solution for accurate and efficient bioluminescent source reconstruction.
• The finite element method ensures applicability to complex biological systems.