Δημοσίευση

A new vessel segmentation algorithm for robust blood flow quantification from two-dimensional phase-contrast magnetic resonance images.

ΤίτλοςA new vessel segmentation algorithm for robust blood flow quantification from two-dimensional phase-contrast magnetic resonance images.
Publication TypeJournal Article
Year of Publication2019
AuthorsBidhult, S., Hedström E., Carlsson M., Töger J., Steding-Ehrenborg K., Arheden H., Aletras A. H., & Heiberg E.
JournalClin Physiol Funct Imaging
Volume39
Issue5
Pagination327-338
Date Published2019 Sep
ISSN1475-097X
Λέξεις κλειδιάAdult, Aged, Aged, 80 and over, Algorithms, Aorta, Blood Flow Velocity, Female, Heart Failure, Heart Septal Defects, Atrial, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Observer Variation, Perfusion Imaging, Phantoms, Imaging, Predictive Value of Tests, Pulmonary Artery, Reproducibility of Results, Retrospective Studies, Young Adult
Abstract

Blood flow measurements in the ascending aorta and pulmonary artery from phase-contrast magnetic resonance images require accurate time-resolved vessel segmentation over the cardiac cycle. Current semi-automatic segmentation methods often involve time-consuming manual correction, relying on user experience for accurate results. The purpose of this study was to develop a semi-automatic vessel segmentation algorithm with shape constraints based on manual vessel delineations for robust segmentation of the ascending aorta and pulmonary artery, to evaluate the proposed method in healthy volunteers and patients with heart failure and congenital heart disease, to validate the method in a pulsatile flow phantom experiment, and to make the method freely available for research purposes. Algorithm shape constraints were extracted from manual reference delineations of the ascending aorta (n = 20) and pulmonary artery (n = 20) and were included in a semi-automatic segmentation method only requiring manual delineation in one image. Bias and variability (bias ± SD) for flow volume of the proposed algorithm versus manual reference delineations were 0·0 ± 1·9 ml in the ascending aorta (n = 151; seven healthy volunteers; 144 heart failure patients) and -1·7 ± 2·9 ml in the pulmonary artery (n = 40; 25 healthy volunteers; 15 patients with atrial septal defect). Interobserver bias and variability were lower (P = 0·008) for the proposed semi-automatic method (-0·1 ± 0·9 ml) compared to manual reference delineations (1·5 ± 5·1 ml). Phantom validation showed good agreement between the proposed method and timer-and-beaker flow volumes (0·4 ± 2·7 ml). In conclusion, the proposed semi-automatic vessel segmentation algorithm can be used for efficient analysis of flow and shunt volumes in the aorta and pulmonary artery.

DOI10.1111/cpf.12582
Alternate JournalClin Physiol Funct Imaging
PubMed ID31102479
PubMed Central IDPMC6852024
Grant List / / Skane University Hospital, Region of Scania /
/ / Medicinska Fakulteten, Lunds Universitet /
/ / Hjärt-Lungfonden /
2011-3916 / / Vetenskapsrådet /
2011-4078 / / Vetenskapsrådet /
2012-4944 / / Vetenskapsrådet /
/ / Knut och Alice Wallenbergs Stiftelse /
/ / Swedish Medical Society /

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