Four-dimensional respiratory motion-resolved whole heart coronary MR angiography.
Details
Serval ID
serval:BIB_A2E839D4AD94
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Four-dimensional respiratory motion-resolved whole heart coronary MR angiography.
Journal
Magnetic resonance in medicine
ISSN
1522-2594 (Electronic)
ISSN-L
0740-3194
Publication state
Published
Issued date
04/2017
Peer-reviewed
Oui
Volume
77
Number
4
Pages
1473-1484
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Publication Status: ppublish
Abstract
Free-breathing whole-heart coronary MR angiography (MRA) commonly uses navigators to gate respiratory motion, resulting in lengthy and unpredictable acquisition times. Conversely, self-navigation has 100% scan efficiency, but requires motion correction over a broad range of respiratory displacements, which may introduce image artifacts. We propose replacing navigators and self-navigation with a respiratory motion-resolved reconstruction approach.
Using a respiratory signal extracted directly from the imaging data, individual signal-readouts are binned according to their respiratory states. The resultant series of undersampled images are reconstructed using an extradimensional golden-angle radial sparse parallel imaging (XD-GRASP) algorithm, which exploits sparsity along the respiratory dimension. Whole-heart coronary MRA was performed in 11 volunteers and four patients with the proposed methodology. Image quality was compared with that obtained with one-dimensional respiratory self-navigation.
Respiratory-resolved reconstruction effectively suppressed respiratory motion artifacts. The quality score for XD-GRASP reconstructions was greater than or equal to self-navigation in 80/88 coronary segments, reaching diagnostic quality in 61/88 segments versus 41/88. Coronary sharpness and length were always superior for the respiratory-resolved datasets, reaching statistical significance (P < 0.05) in most cases.
XD-GRASP represents an attractive alternative for handling respiratory motion in free-breathing whole heart MRI and provides an effective alternative to self-navigation. Magn Reson Med 77:1473-1484, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
Using a respiratory signal extracted directly from the imaging data, individual signal-readouts are binned according to their respiratory states. The resultant series of undersampled images are reconstructed using an extradimensional golden-angle radial sparse parallel imaging (XD-GRASP) algorithm, which exploits sparsity along the respiratory dimension. Whole-heart coronary MRA was performed in 11 volunteers and four patients with the proposed methodology. Image quality was compared with that obtained with one-dimensional respiratory self-navigation.
Respiratory-resolved reconstruction effectively suppressed respiratory motion artifacts. The quality score for XD-GRASP reconstructions was greater than or equal to self-navigation in 80/88 coronary segments, reaching diagnostic quality in 61/88 segments versus 41/88. Coronary sharpness and length were always superior for the respiratory-resolved datasets, reaching statistical significance (P < 0.05) in most cases.
XD-GRASP represents an attractive alternative for handling respiratory motion in free-breathing whole heart MRI and provides an effective alternative to self-navigation. Magn Reson Med 77:1473-1484, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
Keywords
Adult, Aged, Algorithms, Artifacts, Coronary Angiography/methods, Coronary Artery Disease/diagnostic imaging, Coronary Artery Disease/pathology, Coronary Vessels/diagnostic imaging, Coronary Vessels/pathology, Female, Humans, Image Enhancement/methods, Image Interpretation, Computer-Assisted/methods, Magnetic Resonance Angiography/methods, Male, Middle Aged, Reproducibility of Results, Respiratory Mechanics, Respiratory-Gated Imaging Techniques/methods, Sensitivity and Specificity, compressed sensing, coronary MRA, free breathing, motion correction, self-navigation, sparse reconstruction
Pubmed
Web of science
Create date
28/04/2017 11:35
Last modification date
20/08/2019 15:08