1
|
Yan X, Wang Y, Li W, Zhu M, Wang W, Xu C, Li K, Liu B, Shi X. A preliminary study on the application of electrical impedance tomography based on cerebral perfusion monitoring to intracranial pressure changes. Front Neurosci 2024; 18:1390977. [PMID: 38863884 PMCID: PMC11166027 DOI: 10.3389/fnins.2024.1390977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 05/10/2024] [Indexed: 06/13/2024] Open
Abstract
Background In intracranial pathologic conditions of intracranial pressure (ICP) disturbance or hemodynamic instability, maintaining appropriate ICP may reduce the risk of ischemic brain injury. The change of ICP is often accompanied by the change of intracranial blood status. As a non-invasive functional imaging technique, the sensitivity of electrical impedance tomography (EIT) to cerebral hemodynamic changes has been preliminarily confirmed. However, no team has conducted a feasibility study on the dynamic detection of ICP by EIT technology from the perspective of non-invasive whole-brain blood perfusion monitoring. In this study, human brain EIT image sequence was obtained by in vivo measurement, from which a variety of indicators that can reflect the tidal changes of the whole brain impedance were extracted, in order to establish a new method for non-invasive monitoring of ICP changes from the level of cerebral blood perfusion monitoring. Methods Valsalva maneuver (VM) was used to temporarily change the cerebral blood perfusion status of volunteers. The electrical impedance information of the brain during this process was continuously monitored by EIT device and real-time imaging was performed, and the hemodynamic indexes of bilateral middle cerebral arteries were monitored by transcranial Doppler (TCD). The changes in monitoring information obtained by the two techniques were compared and observed. Results The EIT imaging results indicated that the image sequence showed obvious tidal changes with the heart beating. Perfusion indicators of vascular pulsation obtained from EIT images decreased significantly during the stabilization phase of the intervention (PAC: 242.94 ± 100.83, p < 0.01); perfusion index which reflects vascular resistance increased significantly in the stable stage of intervention (PDT: 79.72 ± 18.23, p < 0.001). After the intervention, the parameters gradually returned to the baseline level before compression. The changes of EIT indexes in the whole process are consistent with the changes of middle cerebral artery velocity related indexes shown in TCD results. Conclusion The EIT image combined with the blood perfusion index proposed in this paper can reflect the decrease of cerebral blood flow under the condition of increased ICP in real time and intuitively. With the advantages of high time resolution and high sensitivity, EIT provides a new idea for non-invasive bedside measurement of ICP.
Collapse
Affiliation(s)
- Xiaoheng Yan
- Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, China
- Belt and Road Joint Laboratory on Measurement and Control Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Wang
- Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, China
| | - Weichen Li
- College of Life Sciences, Northwest University, Xi’an, China
| | - Mingxu Zhu
- Department of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Weice Wang
- Department of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Canhua Xu
- Department of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Kun Li
- Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, China
| | - Benyuan Liu
- Department of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Xuetao Shi
- Department of Biomedical Engineering, Air Force Medical University, Xi’an, China
| |
Collapse
|
2
|
Chumnanvej S, Chumnanvej S, Tripathi S. Assessing the benefits of digital twins in neurosurgery: a systematic review. Neurosurg Rev 2024; 47:52. [PMID: 38236336 DOI: 10.1007/s10143-023-02260-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/17/2023] [Accepted: 12/22/2023] [Indexed: 01/19/2024]
Abstract
Digital twins are virtual replicas of their physical counterparts, and can assist in delivering personalized surgical care. This PRISMA guideline-based systematic review evaluates current literature addressing the effectiveness and role of digital twins in many stages of neurosurgical management. The aim of this review is to provide a high-quality analysis of relevant, randomized controlled trials and observational studies addressing the neurosurgical applicability of a variety of digital twin technologies. Using pre-specified criteria, we evaluated 25 randomized controlled trials and observational studies on the applications of digital twins, including navigation, robotics, and image-guided neurosurgeries. All 25 studies compared these technologies against usual surgical approaches. Risk of bias analyses using the Cochrane risk of bias tool for randomized trials (Rob 2) found "low" risk of bias in the majority of studies (23/25). Overall, this systematic review shows that digital twin applications have the potential to be more effective than conventional neurosurgical approaches when applied to brain and spinal surgery. Moreover, the application of these novel technologies may also lead to fewer post-operative complications.
Collapse
Affiliation(s)
- Sorayouth Chumnanvej
- Neurosurgery Division, Department of Surgery, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Siriluk Chumnanvej
- Department of Anesthesiology and Operating Room, Phramongkutklao Hospital, Bangkok, Thailand
| | - Susmit Tripathi
- Department of Neurology, New York Presbyterian-Weill Cornell Medical Center, New York, NY, USA.
| |
Collapse
|
3
|
Toivanen J, Paldanius A, Dekdouk B, Candiani V, Hänninen A, Savolainen T, Strbian D, Forss N, Hyvönen N, Hyttinen J, Kolehmainen V. Simulation-based feasibility study of monitoring of intracerebral hemorrhages and detection of secondary hemorrhages using electrical impedance tomography. J Med Imaging (Bellingham) 2024; 11:014502. [PMID: 38299159 PMCID: PMC10826852 DOI: 10.1117/1.jmi.11.1.014502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/22/2023] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
Purpose We present a simulation-based feasibility study of electrical impedance tomography (EIT) for continuous bedside monitoring of intracerebral hemorrhages (ICH) and detection of secondary hemorrhages. Approach We simulated EIT measurements for six different hemorrhage sizes at two different hemorrhage locations using an anatomically detailed computational head model. Using this dataset, we test the ICH monitoring and detection performance of our tailor-made, patient-specific stroke-monitoring algorithm that utilizes a novel combination of nonlinear region-of-interest difference imaging, parallel level sets regularization and a prior-conditioned least squares algorithm. We compare the results of our algorithm to the results of two reference algorithms, a total variation regularized absolute imaging algorithm and a linear difference imaging algorithm. Results The tailor-made stroke-monitoring algorithm is capable of indicating smaller changes in the simulated hemorrhages than either of the reference algorithms, indicating better monitoring and detection performance. Conclusions Our simulation results from the anatomically detailed head model indicate that EIT equipped with a patient-specific stroke-monitoring algorithm is a promising technology for the unmet clinical need of having a technology for continuous bedside monitoring of brain status of acute stroke patients.
Collapse
Affiliation(s)
- Jussi Toivanen
- University of Eastern Finland, Department of Technical Physics, Kuopio, Finland
| | - Antti Paldanius
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Bachir Dekdouk
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland
| | | | - Asko Hänninen
- University of Eastern Finland, Department of Technical Physics, Kuopio, Finland
| | - Tuomo Savolainen
- University of Eastern Finland, Department of Technical Physics, Kuopio, Finland
| | - Daniel Strbian
- Helsinki University Hospital, HUS Neurocenter, Helsinki, Finland
| | - Nina Forss
- Helsinki University Hospital, HUS Neurocenter, Helsinki, Finland
- Aalto University, Department of Neuroscience and Biomedical Engineering, Helsinki, Finland
| | - Nuutti Hyvönen
- Aalto University, Department of Mathematics and Systems Analysis, Helsinki, Finland
| | - Jari Hyttinen
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Ville Kolehmainen
- University of Eastern Finland, Department of Technical Physics, Kuopio, Finland
| |
Collapse
|
4
|
Ton C, Salehi S, Abasi S, Aggas JR, Liu R, Brandacher G, Guiseppi-Elie A, Grayson WL. Methods of ex vivo analysis of tissue status in vascularized composite allografts. J Transl Med 2023; 21:609. [PMID: 37684651 PMCID: PMC10492401 DOI: 10.1186/s12967-023-04379-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 07/21/2023] [Indexed: 09/10/2023] Open
Abstract
Vascularized composite allotransplantation can improve quality of life and restore functionality. However, the complex tissue composition of vascularized composite allografts (VCAs) presents unique clinical challenges that increase the likelihood of transplant rejection. Under prolonged static cold storage, highly damage-susceptible tissues such as muscle and nerve undergo irreversible degradation that may render allografts non-functional. Skin-containing VCA elicits an immunogenic response that increases the risk of recipient allograft rejection. The development of quantitative metrics to evaluate VCAs prior to and following transplantation are key to mitigating allograft rejection. Correspondingly, a broad range of bioanalytical methods have emerged to assess the progression of VCA rejection and characterize transplantation outcomes. To consolidate the current range of relevant technologies and expand on potential for development, methods to evaluate ex vivo VCA status are herein reviewed and comparatively assessed. The use of implantable physiological status monitoring biochips, non-invasive bioimpedance monitoring to assess edema, and deep learning algorithms to fuse disparate inputs to stratify VCAs are identified.
Collapse
Affiliation(s)
- Carolyn Ton
- Department of Biomedical Engineering, Johns Hopkins University, 400 North Broadway, Smith Building 5023, Baltimore, MD, 21231, USA
- Translational Tissue Engineering Center, Johns Hopkins University, 400 North Broadway, Smith Building 5023, Baltimore, MD, 21231, USA
| | - Sara Salehi
- Department of Biomedical Engineering, Johns Hopkins University, 400 North Broadway, Smith Building 5023, Baltimore, MD, 21231, USA
- Translational Tissue Engineering Center, Johns Hopkins University, 400 North Broadway, Smith Building 5023, Baltimore, MD, 21231, USA
| | - Sara Abasi
- Department of Biomedical Engineering, Center for Bioelectronics, Biosensors and Biochips (C3B®), Texas A&M University, Emerging Technologies Building 3120, 101 Bizzell St, College Station, TX, 77843, USA
- Department of Electrical and Computer Engineering, Center for Bioelectronics, Biosensors and Biochips (C3B®), Texas A&M University, Emerging Technologies Building 3120, 101 Bizzell St, College Station, TX, 77843, USA
- Media and Metabolism, Wildtype, Inc., 2325 3rd St., San Francisco, CA, 94107, USA
| | - John R Aggas
- Department of Biomedical Engineering, Center for Bioelectronics, Biosensors and Biochips (C3B®), Texas A&M University, Emerging Technologies Building 3120, 101 Bizzell St, College Station, TX, 77843, USA
- Department of Electrical and Computer Engineering, Center for Bioelectronics, Biosensors and Biochips (C3B®), Texas A&M University, Emerging Technologies Building 3120, 101 Bizzell St, College Station, TX, 77843, USA
- Test Development, Roche Diagnostics, 9115 Hague Road, Indianapolis, IN, 46256, USA
| | - Renee Liu
- Department of Biomedical Engineering, Johns Hopkins University, 400 North Broadway, Smith Building 5023, Baltimore, MD, 21231, USA
- Translational Tissue Engineering Center, Johns Hopkins University, 400 North Broadway, Smith Building 5023, Baltimore, MD, 21231, USA
| | - Gerald Brandacher
- Department of Plastic and Reconstructive Surgery, Vascularized Composite Allotransplantation (VCA) Laboratory, Reconstructive Transplantation Program, Center for Advanced Physiologic Modeling (CAPM), Johns Hopkins University, Ross Research Building/Suite 749D, 720 Rutland Avenue, Baltimore, MD, 21205, USA.
| | - Anthony Guiseppi-Elie
- Department of Biomedical Engineering, Center for Bioelectronics, Biosensors and Biochips (C3B®), Texas A&M University, Emerging Technologies Building 3120, 101 Bizzell St, College Station, TX, 77843, USA.
- Department of Electrical and Computer Engineering, Center for Bioelectronics, Biosensors and Biochips (C3B®), Texas A&M University, Emerging Technologies Building 3120, 101 Bizzell St, College Station, TX, 77843, USA.
- Department of Cardiovascular Sciences, Houston Methodist Institute for Academic Medicine and Houston Methodist Research Institute, 6670 Bertner Ave., Houston, TX, USA.
- ABTECH Scientific, Inc., Biotechnology Research Park, 800 East Leigh Street, Richmond, VA, USA.
| | - Warren L Grayson
- Department of Biomedical Engineering, Johns Hopkins University, 400 North Broadway, Smith Building 5023, Baltimore, MD, 21231, USA.
- Translational Tissue Engineering Center, Johns Hopkins University, 400 North Broadway, Smith Building 5023, Baltimore, MD, 21231, USA.
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Institute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD, USA.
| |
Collapse
|
5
|
Li M, Zhu R, Li G, Yin S, Zeng L, Bai Z, Chen J, Jiang B, Li L, Wu Y. Point-of-care testing for cerebral edema types based on symmetric cancellation near-field coupling phase shift and support vector machine. Biomed Eng Online 2023; 22:80. [PMID: 37582824 PMCID: PMC10428563 DOI: 10.1186/s12938-023-01145-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 08/07/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND Cerebral edema is an extremely common secondary disease in post-stroke. Point-of-care testing for cerebral edema types has important clinical significance for the precise management to prevent poor prognosis. Nevertheless, there has not been a fully accepted bedside testing method for that. METHODS A symmetric cancellation near-field coupling phase shift (NFCPS) monitoring system is established based on the symmetry of the left and right hemispheres and the fact that unilateral lesions do not affect healthy hemispheres. For exploring the feasibility of this system to reflect the occurrence and development of cerebral edema, 13 rabbits divided into experimental group (n = 8) and control group (n = 5) were performed 24-h NFCPS continuous monitoring experiments. After time difference offset and feature band averaging processing, the changing trend of NFCPS at the stages dominated by cytotoxic edema (CE) and vasogenic edema (VE), respectively, was analyzed. Furthermore, the features under the different time windows were extracted. Then, a discriminative model of cerebral edema types based on support vector machines (SVM) was established and performance of multiple feature combinations was compared. RESULTS The NFCPS monitoring outcomes of experimental group endured focal ischemia modeling by thrombin injection show a trend of first decreasing and then increasing, reaching the lowest value of - 35.05° at the 6th hour. Those of control group do not display obvious upward or downward trend and only fluctuate around the initial value with an average change of - 0.12°. Furthermore, four features under the 1-h and 2-h time windows were extracted. Based on the discriminative model of cerebral edema types, the classification accuracy of 1-h window is higher than 90% and the specificity is close to 1, which is almost the same as the performance of the 2-h window. CONCLUSION This study proves the feasibility of NFCPS technology combined with SVM to distinguish cerebral edema types in a short time, which is promised to become a new solution for immediate and precise management of dehydration therapy after ischemic stroke.
Collapse
Affiliation(s)
- Mingyan Li
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054 China
- College of Artificial Intelligence, Chongqing University of Technology, Chongqing, 401135 China
| | - Rui Zhu
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054 China
| | - Gen Li
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054 China
- Department of Neurosurgery, Southwest Hospital, Army Medical University, Chongqing, 400038 China
| | - Shengtong Yin
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054 China
| | - Lingxi Zeng
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054 China
| | - Zelin Bai
- College of Biomedical Engineering, Army Medical University, Chongqing, 400038 China
| | - Jingbo Chen
- College of Biomedical Engineering, Army Medical University, Chongqing, 400038 China
| | - Bin Jiang
- College of Artificial Intelligence, Chongqing University of Technology, Chongqing, 401135 China
| | - Lihong Li
- College of Artificial Intelligence, Chongqing University of Technology, Chongqing, 401135 China
| | - Yu Wu
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054 China
| |
Collapse
|
6
|
Zhang W, Jiao Y, Zhang T, Liu X, Ye J, Zhang Y, Yang B, Dai M, Shi X, Fu F, Wang L, Xu C. Early detection of acute ischemic stroke using Contrast-enhanced electrical impedance tomography perfusion. Neuroimage Clin 2023; 39:103456. [PMID: 37379734 PMCID: PMC10318520 DOI: 10.1016/j.nicl.2023.103456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 06/08/2023] [Accepted: 06/19/2023] [Indexed: 06/30/2023]
Abstract
A cerebral contrast-enhanced electrical impedance tomography perfusion method is developed for acute ischemic stroke during intravenous thrombolytic therapy. Several clinical contrast agents with stable impedance characteristics and high-conductivity contrast were screened experimentally as electrical impedance contrast agent candidates. The electrical impedance tomography perfusion method was tested on rabbits with focal cerebral infarction, and its capability for early detection was verified based on perfusion images. The experimental results showed that ioversol 350 performed significantly better as an electrical impedance contrast agent than other contrast agents (p < 0.01). Additionally, perfusion images of focal cerebral infarction in rabbits confirmed that the electrical impedance tomography perfusion method could accurately detect the location and area of different cerebral infarction lesions (p < 0.001). Therefore, the cerebral contrast-enhanced electrical impedance tomography perfusion method proposed herein combines traditional, dynamic continuous imaging with rapid detection and could be applied as an early, rapid-detection, auxiliary, bedside imaging method for patients after a suspected ischemic stroke in both prehospital and in-hospital settings.
Collapse
Affiliation(s)
- Weirui Zhang
- Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China; Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China; Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi'an 710032, People's Republic of China
| | - Yang Jiao
- Department of Neurosurgery, Tangdu Hospital of Fourth Military Medical University, Xi'an 710032, People's Republic of China
| | - Tao Zhang
- Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China; Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi'an 710032, People's Republic of China; Drug and Instrument Supervision and Inspection Station, Xining Joint Logistics Support Center, Lanzhou 730050, People's Republic of China
| | - Xuechao Liu
- Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China; Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi'an 710032, People's Republic of China
| | - Jianan Ye
- Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China; Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi'an 710032, People's Republic of China
| | - Yuyan Zhang
- Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China; Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi'an 710032, People's Republic of China
| | - Bin Yang
- Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China; Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi'an 710032, People's Republic of China
| | - Meng Dai
- Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China; Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi'an 710032, People's Republic of China
| | - Xuetao Shi
- Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China; Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi'an 710032, People's Republic of China
| | - Feng Fu
- Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China; Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi'an 710032, People's Republic of China
| | - Liang Wang
- Department of Neurosurgery, Tangdu Hospital of Fourth Military Medical University, Xi'an 710032, People's Republic of China
| | - Canhua Xu
- Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China; Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi'an 710032, People's Republic of China.
| |
Collapse
|
7
|
He C, Teng C, Xiong Z, Lin X, Li H, Li X. Intracranial pressure monitoring in neurosurgery: the present situation and prospects. Chin Neurosurg J 2023; 9:14. [PMID: 37170383 PMCID: PMC10176793 DOI: 10.1186/s41016-023-00327-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 04/24/2023] [Indexed: 05/13/2023] Open
Abstract
Intracranial pressure (ICP) is one of the most important indexes in neurosurgery. It is essential for doctors to determine the numeric value and changes of ICP, whether before or after an operation. Although external ventricular drainage (EVD) is the gold standard for monitoring ICP, more and more novel monitoring methods are being applied clinically.Invasive wired ICP monitoring is still the most commonly used in practice. Meanwhile, with the rise and development of various novel technologies, non-invasive types and invasive wireless types are gradually being used clinically or in the testing phase, as a complimentary approach of ICP management. By choosing appropriate monitoring methods, clinical neurosurgeons are able to obtain ICP values safely and effectively under particular conditions.This article introduces diverse monitoring methods and compares the advantages and disadvantages of different monitoring methods. Moreover, this review may enable clinical neurosurgeons to have a broader view of ICP monitoring.
Collapse
Affiliation(s)
- Chenqi He
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China
| | - Chubei Teng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China
- Department of Neurosurgery, the First Affiliated Hospital, University of South China, Hengyang, Hunan, 421001, People's Republic of China
| | - Zujian Xiong
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China
| | - Xuelei Lin
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China
| | - Hongbo Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China
| | - Xuejun Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China.
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China.
| |
Collapse
|
8
|
Jiao Y, Zhang T, Fan C, Cao H, Chao M, Han L, Zhang W, Mao L, Liu R, Xu C, Wang L. Real-time imaging of traumatic brain injury using magnetic induction tomography. Physiol Meas 2023; 44. [PMID: 36827707 DOI: 10.1088/1361-6579/acbeff] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/24/2023] [Indexed: 02/26/2023]
Abstract
Objective. Early diagnosis of traumatic brain injury (TBI) is crucial for its prognosis; however, traditional computed tomography diagnostic methods rely on large medical devices with an associated lag time to receive results. Therefore, an imaging modality is needed that provides real-time monitoring, can easily be carried out to assess the extent of TBI damage, and thus guides treatment.Approach. In the present study, an improved magnetic induction tomography (MIT) data acquisition system was used to monitor TBI in an animal model and distinguish the injury level. A pneumatically controlled cortical impactor was used to strike the parietal lobe of anesthetized rabbits two or three times under the same parameter mode to establish two different rabbit models of TBI. The MIT data acquisition system was used to record data and continuously monitor the brain for one hour without intervention.Main results. A target with increased conductivity was clearly observed in the reconstructed image. The position was relatively fixed and accurate, and the average positioning error of the image was 0.013 72 m. The normalized mean reconstruction value of all images increased with time. The slope of the regression line of the normalized mean reconstruction value differed significantly between the two models (p< 0.0001).Significance. This indicates that in the animal model, the unique features of MIT may facilitate the early monitoring of TBI and distinguish different degrees of injuries, thereby reducing the risk and mortality of associated complications.
Collapse
Affiliation(s)
- Yang Jiao
- Department of Neurosurgery, Tangdu Hospital of the Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, People's Republic of China
| | - Tao Zhang
- Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China
| | - Chao Fan
- Department of Neurosurgery, Tangdu Hospital of the Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, People's Republic of China
| | - Haiyan Cao
- Department of Neurosurgery, Tangdu Hospital of the Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, People's Republic of China
| | - Min Chao
- Department of Neurosurgery, Tangdu Hospital of the Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, People's Republic of China
| | - Liying Han
- Department of Neurosurgery, Tangdu Hospital of the Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, People's Republic of China
| | - Weirui Zhang
- Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China
| | - Lei Mao
- HangZhou UTRON Technology Co., Ltd, Hang Zhou, People's Republic of China
| | - Ruigang Liu
- Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China
| | - Canhua Xu
- Department of Biomedical Engineering, the Fourth Military Medical University, Xi'an 710032, People's Republic of China
| | - Liang Wang
- Department of Neurosurgery, Tangdu Hospital of the Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, People's Republic of China
| |
Collapse
|
9
|
Müller SJ, Henkes E, Gounis MJ, Felber S, Ganslandt O, Henkes H. Non-Invasive Intracranial Pressure Monitoring. J Clin Med 2023; 12:jcm12062209. [PMID: 36983213 PMCID: PMC10051320 DOI: 10.3390/jcm12062209] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/09/2023] [Accepted: 03/11/2023] [Indexed: 03/15/2023] Open
Abstract
(1) Background: Intracranial pressure (ICP) monitoring plays a key role in the treatment of patients in intensive care units, as well as during long-term surgeries and interventions. The gold standard is invasive measurement and monitoring via ventricular drainage or a parenchymal probe. In recent decades, numerous methods for non-invasive measurement have been evaluated but none have become established in routine clinical practice. The aim of this study was to reflect on the current state of research and shed light on relevant techniques for future clinical application. (2) Methods: We performed a PubMed search for “non-invasive AND ICP AND (measurement OR monitoring)” and identified 306 results. On the basis of these search results, we conducted an in-depth source analysis to identify additional methods. Studies were analyzed for design, patient type (e.g., infants, adults, and shunt patients), statistical evaluation (correlation, accuracy, and reliability), number of included measurements, and statistical assessment of accuracy and reliability. (3) Results: MRI-ICP and two-depth Doppler showed the most potential (and were the most complex methods). Tympanic membrane temperature, diffuse correlation spectroscopy, natural resonance frequency, and retinal vein approaches were also promising. (4) Conclusions: To date, no convincing evidence supports the use of a particular method for non-invasive intracranial pressure measurement. However, many new approaches are under development.
Collapse
Affiliation(s)
- Sebastian Johannes Müller
- Neuroradiologische Klinik, Klinikum Stuttgart, D-70174 Stuttgart, Germany
- Correspondence: ; Tel.: +49-(0)711-278-34501
| | - Elina Henkes
- Neuroradiologische Klinik, Klinikum Stuttgart, D-70174 Stuttgart, Germany
| | - Matthew J. Gounis
- New England Center for Stroke Research, Department of Radiology, University of Massachusetts, Worcester, MA 01655, USA
| | - Stephan Felber
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Stiftungsklinikum Mittelrhein, D-56068 Koblenz, Germany
| | - Oliver Ganslandt
- Neurochirurgische Klinik, Klinikum Stuttgart, D-70174 Stuttgart, Germany
| | - Hans Henkes
- Neuroradiologische Klinik, Klinikum Stuttgart, D-70174 Stuttgart, Germany
- Medizinische Fakultät, Universität Duisburg-Essen, D-47057 Duisburg, Germany
| |
Collapse
|
10
|
Liu X, Zhang T, Ye J, Tian X, Zhang W, Yang B, Dai M, Xu C, Fu F. Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography. SENSORS (BASEL, SWITZERLAND) 2022; 22:9934. [PMID: 36560297 PMCID: PMC9783778 DOI: 10.3390/s22249934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/07/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
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.
Collapse
Affiliation(s)
- Xuechao Liu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
- Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an 710032, China
| | - Tao Zhang
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
- Drug and Instrument Supervision and Inspection Station, Xining Joint Logistics Support Center, Lanzhou 730050, China
| | - Jian’an Ye
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
| | - Xiang Tian
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
| | - Weirui Zhang
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
| | - Bin Yang
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
- Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an 710032, China
| | - Meng Dai
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
- Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an 710032, China
| | - Canhua Xu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
- Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an 710032, China
| | - Feng Fu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
- Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an 710032, China
| |
Collapse
|
11
|
Zhang T, Tian X, Liu X, Ye J, Fu F, Shi X, Liu R, Xu C. Advances of deep learning in electrical impedance tomography image reconstruction. Front Bioeng Biotechnol 2022; 10:1019531. [PMID: 36588934 PMCID: PMC9794741 DOI: 10.3389/fbioe.2022.1019531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 12/02/2022] [Indexed: 12/15/2022] Open
Abstract
Electrical impedance tomography (EIT) has been widely used in biomedical research because of its advantages of real-time imaging and nature of being non-invasive and radiation-free. Additionally, it can reconstruct the distribution or changes in electrical properties in the sensing area. Recently, with the significant advancements in the use of deep learning in intelligent medical imaging, EIT image reconstruction based on deep learning has received considerable attention. This study introduces the basic principles of EIT and summarizes the application progress of deep learning in EIT image reconstruction with regards to three aspects: a single network reconstruction, deep learning combined with traditional algorithm reconstruction, and multiple network hybrid reconstruction. In future, optimizing the datasets may be the main challenge in applying deep learning for EIT image reconstruction. Adopting a better network structure, focusing on the joint reconstruction of EIT and traditional algorithms, and using multimodal deep learning-based EIT may be the solution to existing problems. In general, deep learning offers a fresh approach for improving the performance of EIT image reconstruction and could be the foundation for building an intelligent integrated EIT diagnostic system in the future.
Collapse
Affiliation(s)
- Tao Zhang
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China,Drug and Instrument Supervision and Inspection Station, Xining Joint Logistics Support Center, Lanzhou, China
| | - Xiang Tian
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - XueChao Liu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - JianAn Ye
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - Feng Fu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - XueTao Shi
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - RuiGang Liu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - CanHua Xu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China,*Correspondence: CanHua Xu,
| |
Collapse
|
12
|
Gu Y, Zhou C, Piao Z, Yuan H, Jiang H, Wei H, Zhou Y, Nan G, Ji X. Cerebral edema after ischemic stroke: Pathophysiology and underlying mechanisms. Front Neurosci 2022; 16:988283. [PMID: 36061592 PMCID: PMC9434007 DOI: 10.3389/fnins.2022.988283] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 08/01/2022] [Indexed: 11/30/2022] Open
Abstract
Ischemic stroke is associated with increasing morbidity and has become the main cause of death and disability worldwide. Cerebral edema is a serious complication arising from ischemic stroke. It causes an increase in intracranial pressure, rapid deterioration of neurological symptoms, and formation of cerebral hernia, and is an important risk factor for adverse outcomes after stroke. To date, the detailed mechanism of cerebral edema after stroke remains unclear. This limits advances in prevention and treatment strategies as well as drug development. This review discusses the classification and pathological characteristics of cerebral edema, the possible relationship of the development of cerebral edema after ischemic stroke with aquaporin 4, the SUR1-TRPM4 channel, matrix metalloproteinase 9, microRNA, cerebral venous reflux, inflammatory reactions, and cerebral ischemia/reperfusion injury. It also summarizes research on new therapeutic drugs for post-stroke cerebral edema. Thus, this review provides a reference for further studies and for clinical treatment of cerebral edema after ischemic stroke.
Collapse
Affiliation(s)
- Yuhang Gu
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Chen Zhou
- Beijing Institute of Brain Disorders, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China
| | - Zhe Piao
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Honghua Yuan
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Huimin Jiang
- Beijing Institute of Brain Disorders, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China
| | - Huimin Wei
- Advanced Innovation Center for Big Data-Based Precision Medicine, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yifan Zhou
- Beijing Institute of Brain Disorders, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China
| | - Guangxian Nan
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
- *Correspondence: Guangxian Nan,
| | - Xunming Ji
- Beijing Institute of Brain Disorders, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Xunming Ji,
| |
Collapse
|
13
|
Ke XY, Hou W, Huang Q, Hou X, Bao XY, Kong WX, Li CX, Qiu YQ, Hu SY, Dong LH. Advances in electrical impedance tomography-based brain imaging. Mil Med Res 2022; 9:10. [PMID: 35227324 PMCID: PMC8883715 DOI: 10.1186/s40779-022-00370-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 02/08/2022] [Indexed: 11/10/2022] Open
Abstract
Novel advances in the field of brain imaging have enabled the unprecedented clinical application of various imaging modalities to facilitate disease diagnosis and treatment. Electrical impedance tomography (EIT) is a functional imaging technique that measures the transfer impedances between electrodes on the body surface to estimate the spatial distribution of electrical properties of tissues. EIT offers many advantages over other neuroimaging technologies, which has led to its potential clinical use. This qualitative review provides an overview of the basic principles, algorithms, and system composition of EIT. Recent advances in the field of EIT are discussed in the context of epilepsy, stroke, brain injuries and edema, and other brain diseases. Further, we summarize factors limiting the development of brain EIT and highlight prospects for the field. In epilepsy imaging, there have been advances in EIT imaging depth, from cortical to subcortical regions. In stroke research, a bedside EIT stroke monitoring system has been developed for clinical practice, and data support the role of EIT in multi-modal imaging for diagnosing stroke. Additionally, EIT has been applied to monitor the changes in brain water content associated with cerebral edema, enabling the early identification of brain edema and the evaluation of mannitol dehydration. However, anatomically realistic geometry, inhomogeneity, cranium completeness, anisotropy and skull type, etc., must be considered to improve the accuracy of EIT modeling. Thus, the further establishment of EIT as a mature and routine diagnostic technique will necessitate the accumulation of more supporting evidence.
Collapse
Affiliation(s)
- Xi-Yang Ke
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Wei Hou
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Qi Huang
- CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, Jiangsu, China
| | - Xue Hou
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Xue-Ying Bao
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Wei-Xuan Kong
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China
| | - Cheng-Xiang Li
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Yu-Qi Qiu
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Si-Yi Hu
- CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, Jiangsu, China.
| | - Li-Hua Dong
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China. .,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China. .,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China.
| |
Collapse
|
14
|
Deshmukh KP, Rahmani Dabbagh S, Jiang N, Tasoglu S, Yetisen AK. Recent Technological Developments in the Diagnosis and Treatment of Cerebral Edema. ADVANCED NANOBIOMED RESEARCH 2021. [DOI: 10.1002/anbr.202100001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Karthikeya P. Deshmukh
- Department of Chemical Engineering Imperial College London Imperial College Road, Kensington London SW7 2AZ UK
| | - Sajjad Rahmani Dabbagh
- Department of Mechanical Engineering Koc University Rumelifeneri Yolu, Sariyer Istanbul 34450 Turkey
| | - Nan Jiang
- West China School of Basic Medical Sciences & Forensic Medicine Sichuan University Chengdu 610041 China
| | - Savas Tasoglu
- Department of Mechanical Engineering Koc University Rumelifeneri Yolu, Sariyer Istanbul 34450 Turkey
- Boğaziçi Institute of Biomedical Engineering Boğaziçi University Istanbul 34684 Turkey
| | - Ali K. Yetisen
- Department of Chemical Engineering Imperial College London Imperial College Road, Kensington London SW7 2AZ UK
| |
Collapse
|
15
|
Rosa BMG, Yang GZ. Bladder Volume Monitoring Using Electrical Impedance Tomography With Simultaneous Multi-Tone Tissue Stimulation and DFT-Based Impedance Calculation Inside an FPGA. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:775-786. [PMID: 32746355 DOI: 10.1109/tbcas.2020.3008831] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this article, a novel method for measuring the volume of the urinary bladder non-invasively is presented that relies on the principles dictated by Electrical Impedance Tomography (EIT). The electronic prototype responsible for injecting innocuous electrical currents to the lower abdominal region and measuring the developed voltage levels is fully described, as well as the computational models for resolution of the so-called Forward and Inverse Problems in Imaging. The simultaneous multi-tone injection of current provided by a high performance Field Programmable Gate Array (FPGA), combined with impedance estimation by the Discrete Fourier Transform (DFT) constitutes a novelty in Urodynamics with potential to monitor continuously the intravesical volume of patients in a much faster and comfortable way than traditional transurethral catheterization methods. The resolution of the Inverse Problem is performed by the Gauss-Newton method with Laplacian regularization, allowing to obtain a sectional representation of the volume of urine encompassed by the bladder and surrounding body tissues. Experimentation has been carried out with synthetic phantoms and human subjects with results showing a good correlation between the levels of abdominal admittivity acquired by the EIT system and the volume of ingested water.
Collapse
|
16
|
Li H, Liu X, Xu C, Yang B, Fu D, Dong X, Fu F. Managing erroneous measurements of dynamic brain electrical impedance tomography after reconnection of faulty electrodes. Physiol Meas 2020; 41:035002. [PMID: 32000152 DOI: 10.1088/1361-6579/ab71f4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Electrode detachment may occur during dynamic brain electrical impedance tomography (EIT) measurements. After the faulty electrodes have been reset, EIT can restore to steady monitoring but the corrupted data, which will challenge interpretation of the results, are notoriously difficult to recover. APPROACH Here, a piecewise processing method (PPM) is introduced to manage the erroneous EIT data after reattachment of faulty electrodes. In the PPM, we define the three phases before, during and after reconnection of the faulty electrode as PI, PII and PIII, respectively. Using this definition, an empirical mode decomposition-based interpolation method is introduced to compensate the corrupted data in PII, using the valid measurements in PI and PIII. Then, the compensated data in PII are spliced at the end of PI. Thus, there will be a surge at the junction of PII and PIII due to the changes in contact state of the repositioned electrodes. Finally, to ensure all the EIT data are obtained under constant electrode settings, we calculate the above changes and eliminate them from the data after PII. To verify the performance of the PPM, experiments based on head models, with anatomical structures and with human subjects were conducted. Metrics including permutation entropy (PE) and image correlation (IC) were proposed to measure the stability of the signal and the quality of the reconstructed EIT images, respectively. MAIN RESULTS The results demonstrated that the PE of the processed data was reduced to 0.25 and the IC improved to 0.78. SIGNIFICANCE Without iterative calculations the PPM could efficiently manage the erroneous EIT data after reattachment of the faulty electrodes.
Collapse
Affiliation(s)
- Haoting Li
- Haoting Li and Xuechao Liu contributed equally to this work
| | | | | | | | | | | | | |
Collapse
|
17
|
Comparing the Effect of Foot Massage with Grape Seed Oil and Sweet Almond Oil on Physiological Leg Edema in Primigravidae: A Randomized Clinical Trial. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2020; 2020:6835814. [PMID: 32419817 PMCID: PMC7201824 DOI: 10.1155/2020/6835814] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 08/07/2019] [Accepted: 08/29/2019] [Indexed: 11/30/2022]
Abstract
Background Leg edema is a prevalent problem in pregnancy causing activity restrictions for pregnant women. This study was performed to compare the effect of foot massage using grape seed oil and sweet almond oil on physiological leg edema. Methods A randomized clinical trial was conducted on 90 primigravidae referred to public health centres of Zahedan, Iran. The participants' gestational age was 30–40 weeks. The study was conducted from August 2016 to November 2017. The participants were randomly assigned to three groups (massage with grape seed oil, massage with sweet almond oil, and without intervention). After determining the extent of leg edema, foot massages were done for 20 minutes within 5 days in the two intervention groups. Then, foot circumferences were measured on day 5 after the intervention. Foot circumferences for the control group were measured on days 1 and 5. A nonelastic tape measure was used to measure the circumferences. To analyse the data, SPSS 21 software and statistical tests including one-way ANOVA, Tukey's test, and paired t-test were used. Results The results from this study showed a significant difference in the mean score change of foot circumferences between groups (P=0.001). According to the results of Tukey's test, mean score changes of foot circumferences of both intervention groups were significantly different those of the control group. However, this difference was not significant between the two intervention groups (P=0.865). Conclusion The findings of this study confirmed the effectiveness of foot massage using grape seed and sweet almond oils to reduce pregnancy physiological edema. Therefore, foot massage with appropriate oils can be used as a useful technique by trained midwives in prenatal care centres or at pregnant women houses. This trial is registered with IRCT2015072723370N1.
Collapse
|
18
|
Cao L, Li H, Xu C, Dai M, Ji Z, Shi X, Dong X, Fu F, Yang B. A novel time-difference electrical impedance tomography algorithm using multi-frequency information. Biomed Eng Online 2019; 18:84. [PMID: 31358013 PMCID: PMC6664596 DOI: 10.1186/s12938-019-0703-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 07/20/2019] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Electrical impedance tomography (EIT) is a noninvasive, radiation-free, and low-cost imaging modality for monitoring the conductivity distribution inside a patient. Nowadays, time-difference EIT (tdEIT) is used extensively as it has fast imaging speed and can reflect the dynamic changes of diseases, which make it attractive for a number of medical applications. Moreover, modeling errors are compensated to some extent by subtraction of voltage measurements collected before and after the change. However, tissue conductivity varies with frequency and tdEIT does not efficiently exploit multi-frequency information as it only uses measurements associated with a single frequency. METHODS This paper proposes a tdEIT algorithm that imposes spectral constraints on the framework of the linear least squares problem. Simulation and phantom experiments are conducted to compare the proposed spectral constraints algorithm (SC) with the damped least squares algorithm (DLS), which is a stable tdEIT algorithm used in clinical practice. The condition number and rank of the matrices needing inverses are analyzed, and image quality is evaluated using four indexes. The possibility of multi-tissue imaging and the influence of spectral errors are also explored. RESULTS Significant performance improvement is achieved by combining multi-frequency and time-difference information. The simulation results show that, in one-step iteration, both algorithms have the same condition number and rank, but SC effectively reduces image noise by 20.25% compared to DLS. In addition, deformation error and position error are reduced by 8.37% and 7.86%, respectively. In two-step iteration, the rank of SC is greatly increased, which suggests that more information is employed in image reconstruction. Image noise is further reduced by an average of 32.58%, and deformation error and position error are also reduced by 20.20% and 31.36%, respectively. The phantom results also indicate that SC has stronger noise suppression and target identification abilities, and this advantage is more obvious with iteration. The results of multi-tissue imaging show that SC has the unique advantage of automatically extracting a single tissue to image. CONCLUSIONS SC enables tdEIT to utilize multi-frequency information in cases where the spectral constraints are known and then provides higher quality images for applications.
Collapse
Affiliation(s)
- Lu Cao
- Department of Biomedical Engineering, Air Force Medical University (Fourth Military Medical University), Xi’an, 710032 People’s Republic of China
| | - Haoting Li
- Department of Biomedical Engineering, Air Force Medical University (Fourth Military Medical University), Xi’an, 710032 People’s Republic of China
| | - Canhua Xu
- Department of Biomedical Engineering, Air Force Medical University (Fourth Military Medical University), Xi’an, 710032 People’s Republic of China
| | - Meng Dai
- Department of Biomedical Engineering, Air Force Medical University (Fourth Military Medical University), Xi’an, 710032 People’s Republic of China
| | - Zhenyu Ji
- Department of Biomedical Engineering, Air Force Medical University (Fourth Military Medical University), Xi’an, 710032 People’s Republic of China
| | - Xuetao Shi
- Department of Biomedical Engineering, Air Force Medical University (Fourth Military Medical University), Xi’an, 710032 People’s Republic of China
| | - Xiuzhen Dong
- Department of Biomedical Engineering, Air Force Medical University (Fourth Military Medical University), Xi’an, 710032 People’s Republic of China
| | - Feng Fu
- Department of Biomedical Engineering, Air Force Medical University (Fourth Military Medical University), Xi’an, 710032 People’s Republic of China
| | - Bin Yang
- Department of Biomedical Engineering, Air Force Medical University (Fourth Military Medical University), Xi’an, 710032 People’s Republic of China
| |
Collapse
|