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

TL;DR

This study uses digital breast phantoms to compare reconstruction methods in fluorescence molecular tomography, demonstrating that a sparsity-based approach outperforms traditional Tikhonov regularization in tumor resolution.

Contribution

The paper introduces a simulation framework with computational breast phantoms to evaluate and compare FMT reconstruction methods, highlighting the effectiveness of a novel sparsity-based technique.

Findings

Sparsity-based reconstruction outperforms Tikhonov regularization.

Simulation approach enables comprehensive evaluation of FMT methods.

Improved tumor resolution with the proposed method.

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.