The role of spatial preprocessing in deep learning-based DOT

Ben Wiesel; Shlomi Arnon

doi:10.1117/12.2669878

9 August 2023 The role of spatial preprocessing in deep learning-based DOT

Ben Wiesel, Shlomi Arnon

Proceedings Volume 12628, Diffuse Optical Spectroscopy and Imaging IX; 126281F (2023) https://doi.org/10.1117/12.2669878
Event: European Conferences on Biomedical Optics, 2023, Munich, Germany

Abstract

Diffuse Optical Tomography (DOT) is a non-invasive medical imaging technique that utilizes near-infrared light to study the optical properties of tissues. Recently, deep learning has gained popularity as a reconstruction method to solve DOT. However, despite its success, previous studies only reconstructed semi-homogeneous breasts with an absorption coefficient resolution of 2e-3 1/mm. In this paper, we propose a novel preprocessing method that considers the spatial correlations between different measurements to improve the reconstruction accuracy. Our algorithm is applied on a non-homogeneous breast phantom with absorption coefficient resolution of 5e-7 1/mm to reconstruct its optical properties. We compare our algorithm performance with and without the preprocessing step and to a SOTA analytical inversion technique. The proposed method is able to reduce the RMSE by more than 70% (0.44 to 0.11) and increase the contrast ratio by almost an order of magnitude (0.09 to 0.79).

Citation Download Citation

Ben Wiesel and Shlomi Arnon "The role of spatial preprocessing in deep learning-based DOT", Proc. SPIE 12628, Diffuse Optical Spectroscopy and Imaging IX, 126281F (9 August 2023); https://doi.org/10.1117/12.2669878

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KEYWORDS

Breast

Reconstruction algorithms

Absorption

Deep learning

Education and training

Optical properties

Matrices

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