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REVIEW article

Adv. Opt. Technol.
Sec. Optical Imaging
Volume 13 - 2024 | doi: 10.3389/aot.2024.1431235
This article is part of the Research Topic Deep Learning Enhanced Computational Imaging: Leveraging AI for Advanced Image Reconstruction and Analysis View all articles

Advancing Image Reconstruction in Diffuse Optical Tomography: An Overview of Regularization Methods

Provisionally accepted
HarishG Siddhalingaiah HarishG Siddhalingaiah *RaviprasadK jagganath RaviprasadK jagganath GurusiddappaR Prashanth GurusiddappaR Prashanth
  • National Institute of Technology, Goa, Farmagudi, India

The final, formatted version of the article will be published soon.

    DOT (Diffuse Optical Tomography) is a non-invasive imaging technique used to visualize the internal structure of biological tissues. However, due to the inherent limitations and noise in DOT measurements, accurate image reconstruction remains a significant challenge. In this article, a comprehensive survey of regularization techniques are presented that aimed at enhancing image reconstruction in DOT. The survey encompasses a wide range of regularization methods, including Tikhonov regularization, TV (Total Variation) regularization, sparse regularization etc. Each technique is evaluated in terms of its mathematical formulation, underlying assumptions, advantages, and limitations. Furthermore, the impact of regularization parameters are also discussed, such as regularization strength and regularization matrix, on the quality and accuracy of reconstructed images. Through this review, the studies (ranging from 2009-2024) that have used different regularization methods for reconstructing images in DOT are unveiled. Further, the observations and future suggestions are afforded for exploring the different endeavors undertaken by conventional works and to uncover the hurdles of different regularization methods for image reconstruction in DOT. Overall, this survey aims to provide researchers and practitioners in the field of DOT with an in-depth understanding of different regularization techniques, enabling them to make informed decisions when selecting an appropriate method for enhancing image reconstruction in DOT. By improving the quality and reliability of reconstructed images, these regularization techniques have the potential to advance the field of DOT and facilitate more accurate diagnosis and treatment planning in various biomedical applications.

    Keywords: Regularization methods, Image Reconstruction Strategies, Diffuse optical tomography, diagnosis, bio-medical applications

    Received: 11 May 2024; Accepted: 10 Oct 2024.

    Copyright: © 2024 Siddhalingaiah, jagganath and Prashanth. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: HarishG Siddhalingaiah, National Institute of Technology, Goa, Farmagudi, India

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.