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      Image-Based 3D Characterization of Abdominal Aortic Aneurysm Deformation After Endovascular Aneurysm Repair

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          Abstract

          An abdominal aortic aneurysm (AAA) is a focal dilation of the abdominal aorta, that if not treated, tends to grow and may rupture. The most common treatment for AAAs is the endovascular aneurysm repair (EVAR), which requires that patients undergo Computed Tomography Angiography (CTA)-based post-operative lifelong surveillance due to the possible appearance of complications. These complications may again lead to AAA dilation and rupture. However, there is a lack of advanced quantitative image-analysis tools to support the clinicians in the follow-up. Currently, the approach is to evaluate AAA diameter changes along time to infer the progress of the patient and the post-operative risk of AAA rupture. An increased AAA diameter is usually associated with a higher rupture risk, but there are some small AAAs that rupture, whereas other larger aneurysms remain stable. This means that the diameter-based rupture risk assessment is not suitable for all the cases, and there is increasing evidence that the biomechanical behavior of the AAA may provide additional valuable information regarding the progression of the disease and the risk of rupture. Hence, we propose a promising methodology for post-operative CTA time-series registration and subsequent aneurysm biomechanical strain analysis. From these strains, quantitative image-based descriptors are extracted using a principal component analysis of the tensile and compressive strain fields. Evaluated on 22 patients, our approach yields a mean area under the curve of 88.6% when correlating the strain-based quantitative descriptors with the long-term patient prognosis. This suggests that the strain information directly extracted from the CTA images is able to capture the biomechanical behavior of the aneurysm without relying on finite element modeling and simulation. Furthermore, the extracted descriptors set the basis for possible future imaging biomarkers that may be used in clinical practice. Apart from the diameter, these biomarkers may be used to assess patient prognosis and to enable informed decision making after an EVAR intervention, especially in difficult uncertain cases.

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          Most cited references16

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          Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks

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            Meta analysis on mortality of ruptured abdominal aortic aneurysms.

            To assess the mortality of patients with ruptured abdominal aortic aneurysms undergoing open surgery and examine changes in mortality over time. Literature databases were searched for relevant articles published between 1991 and 2006. Two reviewers independently performed study inclusion and data extraction. Primary outcome measure was 30 day or in-hospital mortality. Subgroup analyses were performed examining the effect of population- and hospital-based studies, hospital volume and type of surgeon. From a total of 1419 identified studies, 145 observational studies met the inclusion criteria of which 116 were included in the systematic review comprising 60,822 patients. Overall mortality was 48.5% (95% CI: 48.1-48.9%) and did not change significantly over the years. Age increased over the years. For overall mortality a trend was seen in favour of high-volume hospitals. This meta-analysis suggests that mortality of patients with RAAA treated by open surgery has not changed over the past 15 years. This could be explained by increased age of patients undergoing RAAA repair.
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              Image registration via level-set motion: applications to atlas-based segmentation.

              Image registration is an often encountered problem in various fields including medical imaging, computer vision and image processing. Numerous algorithms for registering image data have been reported in these areas. In this paper, we present a novel curve evolution approach expressed in a level-set framework to achieve image intensity morphing and a simple non-linear PDE for the corresponding coordinate registration. The key features of the intensity morphing model are that (a) it is very fast and (b) existence and uniqueness of the solution for the evolution model are established in a Sobolev space as opposed to using viscosity methods. The salient features of the coordinate registration model are its simplicity and computational efficiency. The intensity morph is easily achieved via evolving level-sets of one image into the level-sets of the other. To explicitly estimate the coordinate transformation between the images, we derive a non-linear PDE-based motion model which can be solved very efficiently. We demonstrate the performance of our algorithm on a variety of images including synthetic and real data. As an application of the PDE-based motion model, atlas based segmentation of hippocampal shape from several MR brain scans is depicted. In each of these experiments, automated hippocampal shape recovery results are validated via manual "expert" segmentations.
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                Author and article information

                Contributors
                Journal
                Front Bioeng Biotechnol
                Front Bioeng Biotechnol
                Front. Bioeng. Biotechnol.
                Frontiers in Bioengineering and Biotechnology
                Frontiers Media S.A.
                2296-4185
                01 November 2019
                2019
                : 7
                : 267
                Affiliations
                [1] 1Vicomtech Foundation , San Sebastián, Spain
                [2] 2Bioengineering Area, Biodonostia Health Research Institute , San Sebastián, Spain
                [3] 3BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra , Barcelona, Spain
                [4] 4Donostia University Hospital , San Sebastián, Spain
                [5] 5ICREA , Barcelona, Spain
                Author notes

                Edited by: Svein Kleiven, Royal Institute of Technology, Sweden

                Reviewed by: Alessandro Borghi, University College London, United Kingdom; Kenneth L. Monson, The University of Utah, United States

                *Correspondence: Karen López-Linares klopez@ 123456vicomtech.org

                This article was submitted to Biomechanics, a section of the journal Frontiers in Bioengineering and Biotechnology

                Article
                10.3389/fbioe.2019.00267
                6838223
                622776d6-ad5a-4b21-bfe7-5161585d21b0
                Copyright © 2019 López-Linares, García, García, Cortes, Piella, Macía, Noailly and González Ballester.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 10 July 2019
                : 27 September 2019
                Page count
                Figures: 13, Tables: 0, Equations: 3, References: 25, Pages: 17, Words: 8415
                Funding
                Funded by: Osasun Saila, Eusko Jaurlaritzako 10.13039/501100010585
                Funded by: Ministerio de Economía, Industria y Competitividad, Gobierno de España 10.13039/501100010198
                Categories
                Bioengineering and Biotechnology
                Original Research

                abdominal aortic aneurysm,strain analysis,biomechanics,deformation,prognosis,follow-up,computed tomography angiography,biomarker

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