End-to-end reconstruction for mesoscopic fluorescence molecular tomography via deep learning

Shan Gao; Mengzhou Li; Navid Ibtehaj Nizam; Intes Xavier

doi:10.1117/12.2650648

27 March 2023 End-to-end reconstruction for mesoscopic fluorescence molecular tomography via deep learning

Shan Gao, Mengzhou Li, Navid Ibtehaj Nizam, Intes Xavier

Author Affiliations +

Proceedings Volume PC12371, Multimodal Biomedical Imaging XVIII; PC123710H (2023) https://doi.org/10.1117/12.2650648
Event: SPIE BiOS, 2023, San Francisco, California, United States

Conference Poster

Abstract

We propose an end-to-end reconstruction approach for Mesoscopic Fluorescence Molecular Tomography (MFMT) using deep learning. Herein, an optimized deep network based on back-projection with Residual Channel Attention Mechanism architecture is implemented to directly output 3D reconstruction from 2D measurements and diminish the computational burden while overcoming the limitation of the PC's memory during reconstruction. The network is trained by producing a large synthetic dataset through Monte Carlo simulation and validated with in silico data and a phantom experiment. Our results suggest that this approach can reconstruct fluorescence inclusions in scattering media at a mesoscopic scale.

Citation Download Citation

Shan Gao, Mengzhou Li, Navid Ibtehaj Nizam, and Intes Xavier "End-to-end reconstruction for mesoscopic fluorescence molecular tomography via deep learning", Proc. SPIE PC12371, Multimodal Biomedical Imaging XVIII, PC123710H (27 March 2023); https://doi.org/10.1117/12.2650648

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