Application of computational breast phantoms to evaluate reconstruction methods for fluorescence molecular tomography

Abstract

Fluorescence molecular tomography (FMT) has potential of providing high contrast images for breast tumor detection. Computational phantom provides a convenient way to a wide variety of fluorophore distribution configurations in patients and perform comprehensive evaluation of the imaging systems and methods for FMT. In this study, a digital breast phantom was used to compare the performance of a novel sparsity-based reconstruction method and Tikhonov regularization method for resolving tumors with different amount of separation. The results showed that the proposed sparse reconstruction method yielded better performance. This simulation-based approach with computational phantoms enabled an evaluation of the reconstruction methods for FMT for breast-cancer detection.