Cargando…

Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography

Electrical impedance tomography (EIT) is low-cost and noninvasive and has the potential for real-time imaging and bedside monitoring of brain injury. However, brain injury monitoring by EIT imaging suffers from image noise (IN) and resolution problems, causing blurred reconstructions. To address the...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Xuechao, Zhang, Tao, Ye, Jian’an, Tian, Xiang, Zhang, Weirui, Yang, Bin, Dai, Meng, Xu, Canhua, Fu, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783778/
https://www.ncbi.nlm.nih.gov/pubmed/36560297
http://dx.doi.org/10.3390/s22249934
_version_ 1784857656845926400
author Liu, Xuechao
Zhang, Tao
Ye, Jian’an
Tian, Xiang
Zhang, Weirui
Yang, Bin
Dai, Meng
Xu, Canhua
Fu, Feng
author_facet Liu, Xuechao
Zhang, Tao
Ye, Jian’an
Tian, Xiang
Zhang, Weirui
Yang, Bin
Dai, Meng
Xu, Canhua
Fu, Feng
author_sort Liu, Xuechao
collection PubMed
description Electrical impedance tomography (EIT) is low-cost and noninvasive and has the potential for real-time imaging and bedside monitoring of brain injury. However, brain injury monitoring by EIT imaging suffers from image noise (IN) and resolution problems, causing blurred reconstructions. To address these problems, a least absolute shrinkage and selection operator model is built, and a fast iterative shrinkage-thresholding algorithm with continuation (FISTA-C) is proposed. Results of numerical simulations and head phantom experiments indicate that FISTA-C reduces IN by 63.2%, 47.2%, and 29.9% and 54.4%, 44.7%, and 22.7%, respectively, when compared with the damped least-squares algorithm, the split Bergman, and the FISTA algorithms. When the signal-to-noise ratio of the measurements is 80–50 dB, FISTA-C can reduce IN by 83.3%, 72.3%, and 68.7% on average when compared with the three algorithms, respectively. Both simulation and phantom experiments suggest that FISTA-C produces the best image resolution and can identify the two closest targets. Moreover, FISTA-C is more practical for clinical application because it does not require excessive parameter adjustments. This technology can provide better reconstruction performance and significantly outperforms the traditional algorithms in terms of IN and resolution and is expected to offer a general algorithm for brain injury monitoring imaging via EIT.
format Online
Article
Text
id pubmed-9783778
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-97837782022-12-24 Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography Liu, Xuechao Zhang, Tao Ye, Jian’an Tian, Xiang Zhang, Weirui Yang, Bin Dai, Meng Xu, Canhua Fu, Feng Sensors (Basel) Article Electrical impedance tomography (EIT) is low-cost and noninvasive and has the potential for real-time imaging and bedside monitoring of brain injury. However, brain injury monitoring by EIT imaging suffers from image noise (IN) and resolution problems, causing blurred reconstructions. To address these problems, a least absolute shrinkage and selection operator model is built, and a fast iterative shrinkage-thresholding algorithm with continuation (FISTA-C) is proposed. Results of numerical simulations and head phantom experiments indicate that FISTA-C reduces IN by 63.2%, 47.2%, and 29.9% and 54.4%, 44.7%, and 22.7%, respectively, when compared with the damped least-squares algorithm, the split Bergman, and the FISTA algorithms. When the signal-to-noise ratio of the measurements is 80–50 dB, FISTA-C can reduce IN by 83.3%, 72.3%, and 68.7% on average when compared with the three algorithms, respectively. Both simulation and phantom experiments suggest that FISTA-C produces the best image resolution and can identify the two closest targets. Moreover, FISTA-C is more practical for clinical application because it does not require excessive parameter adjustments. This technology can provide better reconstruction performance and significantly outperforms the traditional algorithms in terms of IN and resolution and is expected to offer a general algorithm for brain injury monitoring imaging via EIT. MDPI 2022-12-16 /pmc/articles/PMC9783778/ /pubmed/36560297 http://dx.doi.org/10.3390/s22249934 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Xuechao
Zhang, Tao
Ye, Jian’an
Tian, Xiang
Zhang, Weirui
Yang, Bin
Dai, Meng
Xu, Canhua
Fu, Feng
Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography
title Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography
title_full Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography
title_fullStr Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography
title_full_unstemmed Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography
title_short Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography
title_sort fast iterative shrinkage-thresholding algorithm with continuation for brain injury monitoring imaging based on electrical impedance tomography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783778/
https://www.ncbi.nlm.nih.gov/pubmed/36560297
http://dx.doi.org/10.3390/s22249934
work_keys_str_mv AT liuxuechao fastiterativeshrinkagethresholdingalgorithmwithcontinuationforbraininjurymonitoringimagingbasedonelectricalimpedancetomography
AT zhangtao fastiterativeshrinkagethresholdingalgorithmwithcontinuationforbraininjurymonitoringimagingbasedonelectricalimpedancetomography
AT yejianan fastiterativeshrinkagethresholdingalgorithmwithcontinuationforbraininjurymonitoringimagingbasedonelectricalimpedancetomography
AT tianxiang fastiterativeshrinkagethresholdingalgorithmwithcontinuationforbraininjurymonitoringimagingbasedonelectricalimpedancetomography
AT zhangweirui fastiterativeshrinkagethresholdingalgorithmwithcontinuationforbraininjurymonitoringimagingbasedonelectricalimpedancetomography
AT yangbin fastiterativeshrinkagethresholdingalgorithmwithcontinuationforbraininjurymonitoringimagingbasedonelectricalimpedancetomography
AT daimeng fastiterativeshrinkagethresholdingalgorithmwithcontinuationforbraininjurymonitoringimagingbasedonelectricalimpedancetomography
AT xucanhua fastiterativeshrinkagethresholdingalgorithmwithcontinuationforbraininjurymonitoringimagingbasedonelectricalimpedancetomography
AT fufeng fastiterativeshrinkagethresholdingalgorithmwithcontinuationforbraininjurymonitoringimagingbasedonelectricalimpedancetomography