A self-supervised learning approach for high-resolution diffuse optical tomography using neural fields

Linlin Li; Siyuan Shen; Shengyu Gao; Yuehan Wang; Liangtao Gu; Shiying Li; Xingjun Zhu; Jiahua Jiang; Jingyi Yu; Wuwei Ren

doi:10.1117/12.2691305

28 July 2023 A self-supervised learning approach for high-resolution diffuse optical tomography using neural fields

Linlin Li, Siyuan Shen, Shengyu Gao, Yuehan Wang, Liangtao Gu, Shiying Li, Xingjun Zhu, Jiahua Jiang, Jingyi Yu, Wuwei Ren

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Proceedings Volume 12753, Second Conference on Biomedical Photonics and Cross-Fusion (BPC 2023); 127530B (2023) https://doi.org/10.1117/12.2691305
Event: Second Conference on Biomedical Photonics and Cross-Fusion (BPC 2023), 2023, Shanghai, China

Abstract

Diffuse optical tomography (DOT) has shown promise in biomedical research, such as breast cancer diagnostics and brain imaging, by reconstructing hidden objects within scattering media. However, the conventional reconstruction framework faces challenges due to the highly ill-posed inverse problem of reconstructing optical properties. This work introduces a novel approach, neural field-based diffuse optical tomography (NeuDOT), which leverages a multi-layer perceptron (MLP) to learn an implicit function that maps spatial coordinates to their corresponding optical absorption coefficients. The performance of the NeuDOT method has been evaluated through several phantom studies, demonstrating its potential for high spatial resolution DOT reconstruction

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Linlin Li, Siyuan Shen, Shengyu Gao, Yuehan Wang, Liangtao Gu, Shiying Li, Xingjun Zhu, Jiahua Jiang, Jingyi Yu, and Wuwei Ren "A self-supervised learning approach for high-resolution diffuse optical tomography using neural fields", Proc. SPIE 12753, Second Conference on Biomedical Photonics and Cross-Fusion (BPC 2023), 127530B (28 July 2023); https://doi.org/10.1117/12.2691305

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KEYWORDS

Absorption

Diffuse optical tomography

Medical image reconstruction

Optical properties

Image restoration

3D acquisition

Biological imaging

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