A new vessel segmentation algorithm for robust blood flow quantification from two-dimensional phase-contrast magnetic resonance images
Bidhult, Sebastian LU ; Hedström, Erik LU
; Carlsson, Marcus
LU
; Töger, Johannes
LU
; Steding-Ehrenborg, Katarina
LU
; Arheden, Håkan
LU
; Aletras, Anthony H
LU
and Heiberg, Einar
LU
(2019)
In Clinical Physiology and Functional Imaging
39(5).
p.327-338
- 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... (More)
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 into 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±1·9ml in the ascending aorta (n=151; 7 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). Inter-observer bias and variability were lower (p=0·008) for the proposed semi-automatic method (-0·1±0·9ml) compared to manual reference delineations (1·5±5·1ml). Phantom validation showed good agreement between the proposed method and timer-and-beaker flow volumes (0·4±2·7ml). 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. This article is protected by copyright. All rights reserved.
(Less)
- author
- Bidhult, Sebastian
LU
; Hedström, Erik
LU
; Carlsson, Marcus
LU
; Töger, Johannes
LU
; Steding-Ehrenborg, Katarina
LU
; Arheden, Håkan
LU
; Aletras, Anthony H
LU
and Heiberg, Einar
LU
- organization
- publishing date
- 2019-09
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Clinical Physiology and Functional Imaging
- volume
- 39
- issue
- 5
- pages
- 12 pages
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- pmid:31102479
- scopus:85067391310
- ISSN
- 1475-0961
- DOI
- 10.1111/cpf.12582
- language
- English
- LU publication?
- yes
- id
- 44bd94be-0349-449b-ad86-8893c28d45b0
- date added to LUP
- 2019-05-26 16:09:47
- date last changed
- 2024-11-27 08:23:13
@article{44bd94be-0349-449b-ad86-8893c28d45b0,
abstract = {{<p>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 into 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±1·9ml in the ascending aorta (n=151; 7 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). Inter-observer bias and variability were lower (p=0·008) for the proposed semi-automatic method (-0·1±0·9ml) compared to manual reference delineations (1·5±5·1ml). Phantom validation showed good agreement between the proposed method and timer-and-beaker flow volumes (0·4±2·7ml). 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. This article is protected by copyright. All rights reserved.</p>}},
author = {{Bidhult, Sebastian and Hedström, Erik and Carlsson, Marcus and Töger, Johannes and Steding-Ehrenborg, Katarina and Arheden, Håkan and Aletras, Anthony H and Heiberg, Einar}},
issn = {{1475-0961}},
language = {{eng}},
number = {{5}},
pages = {{327--338}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Clinical Physiology and Functional Imaging}},
title = {{A new vessel segmentation algorithm for robust blood flow quantification from two-dimensional phase-contrast magnetic resonance images}},
url = {{http://dx.doi.org/10.1111/cpf.12582}},
doi = {{10.1111/cpf.12582}},
volume = {{39}},
year = {{2019}},
}