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Regression analysis on forward modeling of diffuse optical tomography system for carcinoma cell detection

The forward model design was employed in the Diffuse Optical Tomography (DOT) system to determine the optimal photonic flux in soft tissues like the brain and breast. Absorption coefficient (mua), reduced scattering coefficient (mus), and photonic flux (phi) were the parameters subjected to optimiza...

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Autores principales: Maheswari, K. Uma, Thilak, M., SenthilKumar, N., Nagaprasad, N., Jule, Leta Tesfaye, Seenivasan, Venkatesh, Ramaswamy, Krishnaraj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918525/
https://www.ncbi.nlm.nih.gov/pubmed/36765152
http://dx.doi.org/10.1038/s41598-023-29063-4
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author Maheswari, K. Uma
Thilak, M.
SenthilKumar, N.
Nagaprasad, N.
Jule, Leta Tesfaye
Seenivasan, Venkatesh
Ramaswamy, Krishnaraj
author_facet Maheswari, K. Uma
Thilak, M.
SenthilKumar, N.
Nagaprasad, N.
Jule, Leta Tesfaye
Seenivasan, Venkatesh
Ramaswamy, Krishnaraj
author_sort Maheswari, K. Uma
collection PubMed
description The forward model design was employed in the Diffuse Optical Tomography (DOT) system to determine the optimal photonic flux in soft tissues like the brain and breast. Absorption coefficient (mua), reduced scattering coefficient (mus), and photonic flux (phi) were the parameters subjected to optimization. The Box–Behnken Design (BBD) method of the Response Surface Methodology (RSM) was applied to enhance the Diffuse Optical Tomography experimental system. The DC modulation voltages applied to different laser diodes of 850 nm and 780 nm wavelengths and spacing between the source and detector are the two factors operating on three optimization parameters that predicted the result through two-dimensional tissue image contours. The analysis of the Variance (ANOVA) model developed was substantial (R(2) =  > 0.954). The experimental results indicate that spacing and wavelength were more influential factors for rebuilding image contour. The position of the tumor in soft tissues is inspired by parameters like absorption coefficient and scattering coefficient, which depend on DC voltages applied to the Laser diode. This regression method predicted the values throughout the studied parameter space and was suitable for enhancement learning of diffuse optical tomography systems. The range of residual error percentage evaluated between experimental and predicted values for mua, mus, and phi was 0.301%, 0.287%, and 0.1%, respectively.
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spelling pubmed-99185252023-02-12 Regression analysis on forward modeling of diffuse optical tomography system for carcinoma cell detection Maheswari, K. Uma Thilak, M. SenthilKumar, N. Nagaprasad, N. Jule, Leta Tesfaye Seenivasan, Venkatesh Ramaswamy, Krishnaraj Sci Rep Article The forward model design was employed in the Diffuse Optical Tomography (DOT) system to determine the optimal photonic flux in soft tissues like the brain and breast. Absorption coefficient (mua), reduced scattering coefficient (mus), and photonic flux (phi) were the parameters subjected to optimization. The Box–Behnken Design (BBD) method of the Response Surface Methodology (RSM) was applied to enhance the Diffuse Optical Tomography experimental system. The DC modulation voltages applied to different laser diodes of 850 nm and 780 nm wavelengths and spacing between the source and detector are the two factors operating on three optimization parameters that predicted the result through two-dimensional tissue image contours. The analysis of the Variance (ANOVA) model developed was substantial (R(2) =  > 0.954). The experimental results indicate that spacing and wavelength were more influential factors for rebuilding image contour. The position of the tumor in soft tissues is inspired by parameters like absorption coefficient and scattering coefficient, which depend on DC voltages applied to the Laser diode. This regression method predicted the values throughout the studied parameter space and was suitable for enhancement learning of diffuse optical tomography systems. The range of residual error percentage evaluated between experimental and predicted values for mua, mus, and phi was 0.301%, 0.287%, and 0.1%, respectively. Nature Publishing Group UK 2023-02-10 /pmc/articles/PMC9918525/ /pubmed/36765152 http://dx.doi.org/10.1038/s41598-023-29063-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Maheswari, K. Uma
Thilak, M.
SenthilKumar, N.
Nagaprasad, N.
Jule, Leta Tesfaye
Seenivasan, Venkatesh
Ramaswamy, Krishnaraj
Regression analysis on forward modeling of diffuse optical tomography system for carcinoma cell detection
title Regression analysis on forward modeling of diffuse optical tomography system for carcinoma cell detection
title_full Regression analysis on forward modeling of diffuse optical tomography system for carcinoma cell detection
title_fullStr Regression analysis on forward modeling of diffuse optical tomography system for carcinoma cell detection
title_full_unstemmed Regression analysis on forward modeling of diffuse optical tomography system for carcinoma cell detection
title_short Regression analysis on forward modeling of diffuse optical tomography system for carcinoma cell detection
title_sort regression analysis on forward modeling of diffuse optical tomography system for carcinoma cell detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918525/
https://www.ncbi.nlm.nih.gov/pubmed/36765152
http://dx.doi.org/10.1038/s41598-023-29063-4
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