Source Identification by Consensus-Based Optimization

TL;DR

This paper introduces a consensus-based optimization algorithm for localizing bioluminescent sources using advanced light propagation models, demonstrating improved accuracy and efficiency in simulated and phantom environments.

Contribution

It presents a novel CBO algorithm that effectively localizes sources with higher-order models, balancing computational cost and accuracy in bioluminescence tomography.

Findings

Higher-order models improve localization accuracy in complex media.

CBO achieves reliable source localization with lower computational cost.

Results show better performance in tissue-mimicking phantoms.

Abstract

A consensus-based optimization (CBO) algorithm, which enables derivative and mesh-free optimization, is presented to localize a bioluminescent source. The light propagation is modeled by the radiative transfer equation approximated by spherical harmonics. The approach is investigated for a hierarchy of simplified diffusion models in simulated environments and tissue-mimicking phantoms. In simulations, the state-of-the-art diffusive approximation gives reliable results for heavily scattering media. However, higher-order models achieve better localization and more accurate source intensities for deeper sources and in the presence of artificial noise in strongly absorbing, but only moderately scattering media. In phantoms, higher-order models give lower approximation errors and the most accurate localization, even for a high scattering coefficient. These results demonstrate the potential of CBO to render higher-order models at lower computational cost while ensuring accurate localization in bioluminescence tomography.