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Spatial frequency domain imaging technology based on Fourier single-pixel imaging

SIGNIFICANCE: Optical properties (absorption coefficient and scattering coefficient) of tissue are the most critical parameters for disease diagnosis-based optical method. In recent years, researchers proposed spatial frequency domain imaging (SFDI) to quantitatively map tissue optical properties in...

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Autores principales: Ren, Hui M., Deng, Guoqing, Zhou, Peng, Kang, Xu, Zhang, Yang, Ni, Jingshu, Zhang, Yuanzhi, Wang, Yikun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786392/
https://www.ncbi.nlm.nih.gov/pubmed/35075831
http://dx.doi.org/10.1117/1.JBO.27.1.016002
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author Ren, Hui M.
Deng, Guoqing
Zhou, Peng
Kang, Xu
Zhang, Yang
Ni, Jingshu
Zhang, Yuanzhi
Wang, Yikun
author_facet Ren, Hui M.
Deng, Guoqing
Zhou, Peng
Kang, Xu
Zhang, Yang
Ni, Jingshu
Zhang, Yuanzhi
Wang, Yikun
author_sort Ren, Hui M.
collection PubMed
description SIGNIFICANCE: Optical properties (absorption coefficient and scattering coefficient) of tissue are the most critical parameters for disease diagnosis-based optical method. In recent years, researchers proposed spatial frequency domain imaging (SFDI) to quantitatively map tissue optical properties in a broad field of contactless imaging. To solve the limitations in wavebands unsuitable for silicon-based sensor technology, a compressed sensing (CS) algorithm is used to reproduce the original signal by a single-pixel detectors. Currently, the existing single-pixel SFDI method mainly uses a random sampling policy to extract and recover signals in the acquisition stage. However, these methods are memory-hungry and time-consuming, and they cannot generate discernible results under low sampling rate. Explorations on high performance and efficiency single-pixel SFDI are of great significance for clinical application. AIM: Fourier single-pixel imaging can reconstruct signals with less time and space costs and has fewer reconstruction errors. We focus on an SFDI algorithm based on Fourier single-pixel imaging and propose our Fourier single-pixel image-based spatial frequency domain imaging method (FSI-SFDI). APPROACH: First, we use Fourier single-pixel imaging algorithm to collect and compress signals and SFDI algorithm to generate optical parameters. Given the basis that the main energy of general image signals is concentrated in the range of low frequency of Fourier frequency domain, our FSI-SFDI uses a circular-sampling scheme to sample data points in the low-frequency region. Then, we reconstruct the image details from these points by optimization-based inverse-FFT method. RESULTS: Our algorithm is tested on simulated data. Results show that the root mean square error (RMSE) of optical parameters is lower than 5% when the data reduction is 92%, and it can generate discernible optical parameter image with low sampling rate. We can observe that our FSI-SFDI primarily recovers the optical properties while keeping the RMSE under the upper bound of 4.5% when we use an image with [Formula: see text] resolution as the example for calculation and analysis. Not only that but also our algorithm consumes less space and time for an image with [Formula: see text] resolution, the signal reconstruction takes only 1.65 ms, and requires less RAM memory. Compared to CS-SFDI method, our FSI-SFDI can reduce the required number of measurements through optimizing algorithm. CONCLUSIONS: Moreover, FSI-SFDI is capable of recovering high-quality resolvable images with lower sampling rate, higher-resolution images with less memory and time consumed than previous CS-SFDI method, which is very promising for clinical data collection and medical analysis.
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spelling pubmed-87863922022-01-31 Spatial frequency domain imaging technology based on Fourier single-pixel imaging Ren, Hui M. Deng, Guoqing Zhou, Peng Kang, Xu Zhang, Yang Ni, Jingshu Zhang, Yuanzhi Wang, Yikun J Biomed Opt Imaging SIGNIFICANCE: Optical properties (absorption coefficient and scattering coefficient) of tissue are the most critical parameters for disease diagnosis-based optical method. In recent years, researchers proposed spatial frequency domain imaging (SFDI) to quantitatively map tissue optical properties in a broad field of contactless imaging. To solve the limitations in wavebands unsuitable for silicon-based sensor technology, a compressed sensing (CS) algorithm is used to reproduce the original signal by a single-pixel detectors. Currently, the existing single-pixel SFDI method mainly uses a random sampling policy to extract and recover signals in the acquisition stage. However, these methods are memory-hungry and time-consuming, and they cannot generate discernible results under low sampling rate. Explorations on high performance and efficiency single-pixel SFDI are of great significance for clinical application. AIM: Fourier single-pixel imaging can reconstruct signals with less time and space costs and has fewer reconstruction errors. We focus on an SFDI algorithm based on Fourier single-pixel imaging and propose our Fourier single-pixel image-based spatial frequency domain imaging method (FSI-SFDI). APPROACH: First, we use Fourier single-pixel imaging algorithm to collect and compress signals and SFDI algorithm to generate optical parameters. Given the basis that the main energy of general image signals is concentrated in the range of low frequency of Fourier frequency domain, our FSI-SFDI uses a circular-sampling scheme to sample data points in the low-frequency region. Then, we reconstruct the image details from these points by optimization-based inverse-FFT method. RESULTS: Our algorithm is tested on simulated data. Results show that the root mean square error (RMSE) of optical parameters is lower than 5% when the data reduction is 92%, and it can generate discernible optical parameter image with low sampling rate. We can observe that our FSI-SFDI primarily recovers the optical properties while keeping the RMSE under the upper bound of 4.5% when we use an image with [Formula: see text] resolution as the example for calculation and analysis. Not only that but also our algorithm consumes less space and time for an image with [Formula: see text] resolution, the signal reconstruction takes only 1.65 ms, and requires less RAM memory. Compared to CS-SFDI method, our FSI-SFDI can reduce the required number of measurements through optimizing algorithm. CONCLUSIONS: Moreover, FSI-SFDI is capable of recovering high-quality resolvable images with lower sampling rate, higher-resolution images with less memory and time consumed than previous CS-SFDI method, which is very promising for clinical data collection and medical analysis. Society of Photo-Optical Instrumentation Engineers 2022-01-24 2022-01 /pmc/articles/PMC8786392/ /pubmed/35075831 http://dx.doi.org/10.1117/1.JBO.27.1.016002 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
spellingShingle Imaging
Ren, Hui M.
Deng, Guoqing
Zhou, Peng
Kang, Xu
Zhang, Yang
Ni, Jingshu
Zhang, Yuanzhi
Wang, Yikun
Spatial frequency domain imaging technology based on Fourier single-pixel imaging
title Spatial frequency domain imaging technology based on Fourier single-pixel imaging
title_full Spatial frequency domain imaging technology based on Fourier single-pixel imaging
title_fullStr Spatial frequency domain imaging technology based on Fourier single-pixel imaging
title_full_unstemmed Spatial frequency domain imaging technology based on Fourier single-pixel imaging
title_short Spatial frequency domain imaging technology based on Fourier single-pixel imaging
title_sort spatial frequency domain imaging technology based on fourier single-pixel imaging
topic Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786392/
https://www.ncbi.nlm.nih.gov/pubmed/35075831
http://dx.doi.org/10.1117/1.JBO.27.1.016002
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