Electrical impedance tomography (EIT) allows the measurement of intra-thoracic impedance changes related to cardiovascular activity. As a safe and low-cost imaging modality, EIT is an appealing candidate for non-invasive and continuous haemodynamic monitoring. EIT has recently been shown to allow the assessment of aortic blood pressure via the estimation of the aortic pulse arrival time (PAT). However, finding the aortic signal within EIT image sequences is a challenging task: the signal has a small amplitude and is difficult to locate due to the small size of the aorta and the inherent low spatial resolution of EIT. In order to most reliably detect the aortic signal, our objective was to understand the effect of EIT measurement settings (electrode belt placement, reconstruction algorithm). This paper investigates the influence of three transversal belt placements and two commonly-used difference reconstruction algorithms (Gauss-Newton and GREIT) on the measurement of aortic signals in view of aortic blood pressure estimation via EIT. A magnetic resonance imaging based three-dimensional finite element model of the haemodynamic bio-impedance properties of the human thorax was created. Two simulation experiments were performed with the aim to (1) evaluate the timing error in aortic PAT estimation and (2) quantify the strength of the aortic signal in each pixel of the EIT image sequences. Both experiments reveal better performance for images reconstructed with Gauss-Newton (with a noise figure of 0.5 or above) and a belt placement at the height of the heart or higher. According to the noise-free scenarios simulated, the uncertainty in the analysis of the aortic EIT signal is expected to induce blood pressure errors of at least ± 1.4 mmHg.
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http://dx.doi.org/10.1088/0967-3334/36/6/1147 | DOI Listing |
Nephrol Nurs J
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Professor of Pharmacy Practice, Clinical Pharmacotherapy Specialist, Nephrology & Dialysis, Arnold &Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, NY.
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Professor of Medicine, Department of Internal Medicine, Division of Nephrology, School of Medicine, Virginia Commonwealth University.
Chronic kidney disease (CKD) affects 10% of the global population, with increasing prevalence driven by diabetes, hypertension, and aging populations. CKD often progresses asymptomatically, frequently undetected until advanced stages, and may require costly treatments, such as dialysis or transplantation. CKD imposes a substantial financial burden on health care systems, with management costs rising sharply as the disease progresses, underscoring the need for early, cost-effective interventions.
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Department of Nephrology, Faculty of Medicine, Dokuz Eylul University, Izmir, Türkiye.
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Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China.
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