The Role of Spatial Preprocessing in Deep Learning-Based DOT

Ben Wiesel, Shlomi Arnon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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).

Original languageEnglish
Title of host publicationDiffuse Optical Spectroscopy and Imaging IX
EditorsDavide Contini, Yoko Hoshi, Thomas D. O'Sullivan
ISBN (Electronic)9781510664654
StatePublished - 1 Jan 2023
EventDiffuse Optical Spectroscopy and Imaging IX 2023 - Munich, Germany
Duration: 25 Jun 202328 Jun 2023

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceDiffuse Optical Spectroscopy and Imaging IX 2023


  • CW-DOT
  • DL-DOT
  • Deep learning
  • Preprocessing methods

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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