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Automated multi-atlas segmentation of cardiac 4D flow MRI
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra, Linköping, Sweden.ORCID iD: 0000-0003-0908-9470
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.ORCID iD: 0000-0003-2198-9690
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2018 (English)In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 49, p. 128-140Article in journal (Refereed) Published
Abstract [en]

Four-dimensional (4D) flow magnetic resonance imaging (4D Flow MRI) enables acquisition of time-resolved three-directional velocity data in the entire heart and all major thoracic vessels. The segmentation of these tissues is typically performed using semi-automatic methods. Some of which primarily rely on the velocity data and result in a segmentation of the vessels only during the systolic phases. Other methods, mostly applied on the heart, rely on separately acquired balanced Steady State Free Precession (b-SSFP) MR images, after which the segmentations are superimposed on the 4D Flow MRI. While b-SSFP images typically cover the whole cardiac cycle and have good contrast, they suffer from a number of problems, such as large slice thickness, limited coverage of the cardiac anatomy, and being prone to displacement errors caused by respiratory motion. To address these limitations we propose a multi-atlas segmentation method, which relies only on 4D Flow MRI data, to automatically generate four-dimensional segmentations that include the entire thoracic cardiovascular system present in these datasets. The approach was evaluated on 4D Flow MR datasets from a cohort of 27 healthy volunteers and 83 patients with mildly impaired systolic left-ventricular function. Comparison of manual and automatic segmentations of the cardiac chambers at end-systolic and end-diastolic timeframes showed agreements comparable to those previously reported for automatic segmentation methods of b-SSFP MR images. Furthermore, automatic segmentation of the entire thoracic cardiovascular system improves visualization of 4D Flow MRI and facilitates computation of hemodynamic parameters.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 49, p. 128-140
Keywords [en]
4D Flow MRI, Cardiac segmentation, Multi-atlas segmentation, Heart, Magnetic resonance imaging, Automatic segmentations, Directional velocities, Hemodynamic parameters, Left ventricular function, Segmentation methods, Semiautomatic methods, Steady state free precessions, Image segmentation, adult, anatomy, article, cohort analysis, controlled study, error, female, heart cycle, heart left ventricle function, human, human tissue, major clinical study, male, motion, nuclear magnetic resonance imaging, steady state, thickness, volunteer
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-150788DOI: 10.1016/j.media.2018.08.003ISI: 000446286600011PubMedID: 30144652Scopus ID: 2-s2.0-85051830661OAI: oai:DiVA.org:liu-150788DiVA, id: diva2:1243429
Note

Funding details: 310612; Funding details: FP7, Seventh Framework Programme; Funding details: 621-2014-6191, VR, Vetenskapsrådet; Funding details: 223615; Funding details: 20140398; Funding text: This work was partially funded by the FP7-funded project DOPPLER-CIP [grant number 223615]; the European Union’s Seventh Framework Programme ( FP7/2007-2013 ) [grant number 310612 ]; the Swedish Research Council [grant number 621-2014-6191 ]; and the Swedish Heart and Lung Foundation [grant number 20140398 ]. 

Available from: 2018-08-31 Created: 2018-08-31 Last updated: 2018-10-17Bibliographically approved

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The full text will be freely available from 2020-08-13 11:32
Available from 2020-08-13 11:32

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Bustamante, MarianaGupta, VikasForsberg, DanielCarlhäll, CarljohanEngvall, JanEbbers, Tino

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Division of Cardiovascular MedicineFaculty of Medicine and Health SciencesCenter for Medical Image Science and Visualization (CMIV)Department of Clinical Physiology in Linköping
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