@inproceedings{7d0e3802f6ec45d3a17f907e094f63b4,
title = "The Role of Spatial Preprocessing in Deep Learning-Based DOT",
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).",
keywords = "CW-DOT, DL-DOT, Deep learning, Preprocessing methods",
author = "Ben Wiesel and Shlomi Arnon",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; Diffuse Optical Spectroscopy and Imaging IX 2023 ; Conference date: 25-06-2023 Through 28-06-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.1117/12.2669878",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Davide Contini and Yoko Hoshi and O'Sullivan, {Thomas D.}",
booktitle = "Diffuse Optical Spectroscopy and Imaging IX",
address = "United States",
}