Diffusion equation engine deep learning for diffuse optical tomography

Chengpu Wei; Zhe Li; Zhonghua Sun; Kebin Jia; Jinchao Feng

doi:10.1117/12.2606609

2 March 2022 Diffusion equation engine deep learning for diffuse optical tomography

Chengpu Wei, Zhe Li, Zhonghua Sun, Kebin Jia, Jinchao Feng

Proceedings Volume 11952, Multimodal Biomedical Imaging XVII; 1195206 (2022) https://doi.org/10.1117/12.2606609
Event: SPIE BiOS, 2022, San Francisco, California, United States

Conference Poster

Abstract

Diffuse optical tomography (DOT) is a promising non-invasive optical imaging technique that can provide functional information of biological tissues. Since diffuse light undergoes multiple scattering in biological tissues and boundary measurements are limited, DOT reconstruction is ill-posedness and ill-conditioned. To overcome these limitations, Tikhonov regularization is the most popular algorithm. Recently, deep learning based reconstruction methods have attracted increasing attention, and promising results have been reported. However, they lack generalization for unstructured physical model. Therefore, a model-base convolution neural network framework (Model-CNN) is developed. It composes of two layers: data consistency layer and depth layer, which increases the interpretability of the model. Its performance is evaluated with numerical simulations. Our results demonstrate that Model-CNN can get better reconstructed results than those obtained by Tikhonov Regularization in terms of ABE, MSE, and PSNR.

Conference Presentation

Citation Download Citation

Chengpu Wei, Zhe Li, Zhonghua Sun, Kebin Jia, and Jinchao Feng "Diffusion equation engine deep learning for diffuse optical tomography", Proc. SPIE 11952, Multimodal Biomedical Imaging XVII, 1195206 (2 March 2022); https://doi.org/10.1117/12.2606609

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