| 1. |
- Bissell, Malenka M., et al.
(författare)
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4D Flow cardiovascular magnetic resonance consensus statement : 2023 update
- 2023
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Ingår i: Journal of Cardiovascular Magnetic Resonance. - : BMC. - 1097-6647 .- 1532-429X. ; 25:1
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Forskningsöversikt (refereegranskat)abstract
- Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 ‘4D Flow CMR Consensus Statement’. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards.
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| 2. |
- Abrahamyan, Lilit, et al.
(författare)
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A Problem Solving Environment for Image-Based Computational Hemodynamics
- 2005
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Konferensbidrag (refereegranskat)abstract
- We introduce a complete problem solving environment designed for pulsatile flows in 3D complex geometries, especially arteries. Three-dimensional images from arteries, obtained from e.g. Magnetic Resonance Imaging, are segmented to obtain a geometrical description of the arteries of interest. This segmented artery is prepared for blood flow simulations in a 3D editing tool, allowing to define in- and outlets, to filter and crop part of the artery, to add certain structures ( e.g. a by-pass, or stents ), and to generate computational meshes as input to the blood flow simulators. Using dedicated fluid flow solvers the time dependent blood flow in the artery during one systole is computed. The resulting flow, pressure and shear stress fields are then analyzed using a number of visualization techniques. The whole environment can be operated from a desktop virtual reality system, and is embedded in a Grid computing environment.
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| 3. |
- Shahzad, Rahil, et al.
(författare)
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Automated extraction and labelling of the arterial tree from whole-body MRA data
- 2015
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Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415 .- 1361-8423. ; 24:1, s. 28-40
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Tidskriftsartikel (refereegranskat)abstract
- In this work, we present a fully automated algorithm for extraction of the 3D arterial tree and labelling the tree segments from whole-body magnetic resonance angiography (WB-MRA) sequences. The algorithm developed consists of two core parts (i) 3D volume reconstruction from different stations with simultaneous correction of different types of intensity inhomogeneity, and (ii) Extraction of the arterial tree and subsequent labelling of the pruned extracted tree. Extraction of the arterial tree is performed using the probability map of the "contrast" class, which is obtained as one of the results of the inhomogeneity correction scheme. We demonstrate that such approach is more robust than using the difference between the pre- and post-contrast channels traditionally used for this purpose. Labelling the extracted tree is performed by using a combination of graph-based and atlas-based approaches. Validation of our method with respect to the extracted tree was performed on the arterial tree subdivided into 32 segments, 82.4% of which were completely detected, 11.7% partially detected, and 5.9% were missed on a cohort of 35 subjects. With respect to automated labelling accuracy of the 32 segments, various registration strategies were investigated on a training set consisting of 10 scans. Further analysis on the test set consisting of 25 data sets indicates that 69% of the vessel centerline tree in the head and neck region, 80% in the thorax and abdomen region, and 84% in the legs was accurately labelled to the correct vessel segment. These results indicate clinical potential of our approach in enabling fully automated and accurate analysis of the entire arterial tree. This is the first study that not only automatically extracts the WB-MRA arterial tree, but also labels the vessel tree segments.
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