  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.
