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      A Novel Framework for Early Detection of Hypertension using Magnetic Resonance Angiography

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          Abstract

          Hypertension is a leading mortality cause of 410,000 patients in USA. Cerebrovascular structural changes that occur as a result of chronically elevated cerebral perfusion pressure are hypothesized to precede the onset of systemic hypertension. A novel framework is presented in this manuscript to detect and quantify cerebrovascular changes (i.e. blood vessel diameters and tortuosity changes) using magnetic resonance angiography (MRA) data. The proposed framework consists of: 1) A novel adaptive segmentation algorithm to delineate large as well as small blood vessels locally using 3-D spatial information and appearance features of the cerebrovascular system; 2) Estimating the cumulative distribution function (CDF) of the 3-D distance map of the cerebrovascular system to quantify alterations in cerebral blood vessels’ diameters; 3) Calculation of mean and Gaussian curvatures to quantify cerebrovascular tortuosity; and 4) Statistical and correlation analyses to identify the relationship between mean arterial pressure (MAP) and cerebral blood vessels’ diameters and tortuosity alterations. The proposed framework was validated using MAP and MRA data collected from 15 patients over a 700-days period. The novel adaptive segmentation algorithm recorded a 92.23% Dice similarity coefficient (DSC), a 94.82% sensitivity, a 99.00% specificity, and a 10.00% absolute vessels volume difference (AVVD) in delineating cerebral blood vessels from surrounding tissues compared to the ground truth. Experiments demonstrated that MAP is inversely related to cerebral blood vessel diameters (p-value < 0.05) globally (over the whole brain) and locally (at circle of Willis and below). A statistically significant direct correlation (p-value < 0.05) was found between MAP and tortuosity (medians of Gaussian and mean curvatures, and average of mean curvature) globally and locally (at circle of Willis and below). Quantification of the cerebrovascular diameter and tortuosity changes may enable clinicians to predict elevated blood pressure before its onset and optimize medical treatment plans of pre-hypertension and hypertension.

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          Most cited references21

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          A generalized Gaussian image model for edge-preserving MAP estimation.

          The authors present a Markov random field model which allows realistic edge modeling while providing stable maximum a posterior (MAP) solutions. The model, referred to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian distribution used in robust detection and estimation. The model satisfies several desirable analytical and computational properties for map estimation, including continuous dependence of the estimate on the data, invariance of the character of solutions to scaling of data, and a solution which lies at the unique global minimum of the a posteriori log-likelihood function. The GGMRF is demonstrated to be useful for image reconstruction in low-dosage transmission tomography.
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            Twisted Blood Vessels: Symptoms, Etiology and Biomechanical Mechanisms

            Tortuous arteries and veins are commonly observed in humans and animals. While mild tortuosity is asymptomatic, severe tortuosity can lead to ischemic attack in distal organs. Clinical observations have linked tortuous arteries and veins with aging, atherosclerosis, hypertension, genetic defects and diabetes mellitus. However, the mechanisms of their formation and development are poorly understood. This review summarizes the current clinical and biomechanical studies on the initiation, development and treatment of tortuous blood vessels. We submit a new hypothesis that mechanical instability and remodeling could be mechanisms for the initiation and development of these tortuous vessels.
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              Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

              Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast).
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                Author and article information

                Contributors
                aselba01@louisville.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                31 July 2019
                31 July 2019
                2019
                : 9
                : 11105
                Affiliations
                [1 ]ISNI 0000 0001 2113 1622, GRID grid.266623.5, Bioimaging Laboratory, Bioengineering Department, , University of Louisville, ; Louisville, KY 40292 USA
                [2 ]ISNI 0000 0001 2113 1622, GRID grid.266623.5, Computer Engineering and Computer Science Department, , University of Louisville, ; Louisville, KY USA
                [3 ]ISNI 0000000103426662, GRID grid.10251.37, Faculty of Computer Science and Information, Information Technology Department, , Mansoura University, ; Mansoura, 35516 Egypt
                [4 ]Electrical and Computer Engineering Department, University of Abu Dhabi, Abu Dhabi, UAE
                Author information
                http://orcid.org/0000-0002-1931-3416
                http://orcid.org/0000-0001-7264-1323
                Article
                47368
                10.1038/s41598-019-47368-1
                6668478
                31366941
                08f2883c-3cb9-46aa-bea2-e6a581cf7884
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 September 2018
                : 11 July 2019
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                © The Author(s) 2019

                Uncategorized
                diagnosis,translational research
                Uncategorized
                diagnosis, translational research

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