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...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 22; číslo 24; s. 9934
Hlavní autori:	Liu, Xuechao, Zhang, Tao, Ye, Jian’an, Tian, Xiang, Zhang, Weirui, Yang, Bin, Dai, Meng, Xu, Canhua, Fu, Feng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 01.12.2022 MDPI
Predmet:	Algorithms Brain Brain Injuries - diagnostic imaging brain injury monitoring imaging Comparative analysis Electric Impedance electrical impedance tomography fast iterative shrinkage-thresholding algorithm Humans Image Processing, Computer-Assisted - methods Injuries Inverse problems Ischemia least absolute shrinkage and selection operator model Methods Numerical analysis Patient monitoring equipment Phantoms, Imaging reconstruction algorithm Simulation methods Tomography Tomography - methods Tomography, X-Ray Computed Traumatic brain injury reconstruction algorithm least absolute shrinkage and selection operator model fast iterative shrinkage-thresholding algorithm electrical impedance tomography brain injury monitoring imaging
ISSN:	1424-8220, 1424-8220
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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Zhang, Weirui Liu, Xuechao Ye, Jian’an Yang, Bin Xu, Canhua Dai, Meng Tian, Xiang Zhang, Tao Fu, Feng
AuthorAffiliation	1 Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China 3 Drug and Instrument Supervision and Inspection Station, Xining Joint Logistics Support Center, Lanzhou 730050, China 2 Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an 710032, China
AuthorAffiliation_xml	– name: 2 Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an 710032, China – name: 3 Drug and Instrument Supervision and Inspection Station, Xining Joint Logistics Support Center, Lanzhou 730050, China – name: 1 Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
Author_xml	– sequence: 1 givenname: Xuechao orcidid: 0000-0002-0358-0683 surname: Liu fullname: Liu, Xuechao – sequence: 2 givenname: Tao orcidid: 0000-0002-1535-4309 surname: Zhang fullname: Zhang, Tao – sequence: 3 givenname: Jian’an orcidid: 0000-0003-1968-7669 surname: Ye fullname: Ye, Jian’an – sequence: 4 givenname: Xiang surname: Tian fullname: Tian, Xiang – sequence: 5 givenname: Weirui surname: Zhang fullname: Zhang, Weirui – sequence: 6 givenname: Bin surname: Yang fullname: Yang, Bin – sequence: 7 givenname: Meng surname: Dai fullname: Dai, Meng – sequence: 8 givenname: Canhua surname: Xu fullname: Xu, Canhua – sequence: 9 givenname: Feng surname: Fu fullname: Fu, Feng
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Keywords	reconstruction algorithm least absolute shrinkage and selection operator model fast iterative shrinkage-thresholding algorithm electrical impedance tomography brain injury monitoring imaging
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Snippet	Electrical impedance tomography (EIT) is low-cost and noninvasive and has the potential for real-time imaging and bedside monitoring of brain injury. However,...
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SubjectTerms	Algorithms Brain Brain Injuries - diagnostic imaging brain injury monitoring imaging Comparative analysis Electric Impedance electrical impedance tomography fast iterative shrinkage-thresholding algorithm Humans Image Processing, Computer-Assisted - methods Injuries Inverse problems Ischemia least absolute shrinkage and selection operator model Methods Numerical analysis Patient monitoring equipment Phantoms, Imaging reconstruction algorithm Simulation methods Tomography Tomography - methods Tomography, X-Ray Computed Traumatic brain injury
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Title	Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography
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