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<p>Diffuse Optical Tomography (DOT) 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. The conventional model mistakes and intricate boundaries can confound accurate reconstructions, whereas the noise in measurements requires regularization techniques, which may generate further difficulties. In this article, a comprehensive review of regularization techniques is presented aimed at enhancing image reconstruction in DOT. The review encompasses a wide range of regularization methods, including Tikhonov regularization, Total Variation regularization, sparse regularization, etc. Each technique is evaluated in terms of its mathematical formulation, underlying assumptions, advantages, and limitations. It also assists in executing more constraints, which direct the reconstruction towards an individual and stable solution. Furthermore, the impact of regularization parameters is 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 to 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 uncovering the hurdles of different regularization methods for image reconstruction in DOT. Overall, this review 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. Furthermore, this study highlights the challenges posed by multiple random scattering, noise, and inverse problems in extracting information from scattered photon data.</p>

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

  • 期刊论文

  • 发表日期：

    2025年5月14日

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  • Harish G. Siddalingaiah,
  • Ravi Prasad K. Jagannath,
  • Gurusiddappa R. Prashanth

摘要

Diffuse Optical Tomography (DOT) 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. The conventional model mistakes and intricate boundaries can confound accurate reconstructions, whereas the noise in measurements requires regularization techniques, which may generate further difficulties. In this article, a comprehensive review of regularization techniques is presented aimed at enhancing image reconstruction in DOT. The review encompasses a wide range of regularization methods, including Tikhonov regularization, Total Variation regularization, sparse regularization, etc. Each technique is evaluated in terms of its mathematical formulation, underlying assumptions, advantages, and limitations. It also assists in executing more constraints, which direct the reconstruction towards an individual and stable solution. Furthermore, the impact of regularization parameters is 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 to 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 uncovering the hurdles of different regularization methods for image reconstruction in DOT. Overall, this review 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. Furthermore, this study highlights the challenges posed by multiple random scattering, noise, and inverse problems in extracting information from scattered photon data.