Graph convolution based residual connected network for morphological reconstruction in fluorescence molecular tomography

Yu Wang; Chang Bian; Yu An; Hanfan Wang; Qian Liang; Yang Du; Jie Tian

doi:10.1117/12.2605349

4 April 2022 Graph convolution based residual connected network for morphological reconstruction in fluorescence molecular tomography

Yu Wang, Chang Bian, Yu An, Hanfan Wang, Qian Liang, Yang Du, Jie Tian

Author Affiliations +

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 120361X (2022) https://doi.org/10.1117/12.2605349
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

In this work, we propose a novel GCN based Residual connected (GCN-RC) network to improve the quality of Fluorescence molecular tomography (FMT) morphological reconstruction. Instead of using a simplified linear model of photon propagation for FMT reconstruction, the method can directly construct a nonlinear mapping relationship between the surface photon density and internal fluorescent source. In order to validate the reconstruction performance of GCN-RC, we performed numerical simulation experiments and in vivo experiments based on tumor-bearing mice. Both numerical simulated and in vivo experimental results demonstrated that GCN-RC achieved improved reconstruction in terms of both source localization and morphology recovery.

Conference Presentation

Citation Download Citation

Yu Wang, Chang Bian, Yu An, Hanfan Wang, Qian Liang, Yang Du, and Jie Tian "Graph convolution based residual connected network for morphological reconstruction in fluorescence molecular tomography", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 120361X (4 April 2022); https://doi.org/10.1117/12.2605349

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