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Title: Correcting motion in multiplanar cardiac magnetic resonance images
Authors: Wan, M
Huang, W
Zhang, J.-M 
Zhao, X
Allen, J.C 
Tan, R.S 
Wan, X
Zhong, L 
Keywords: Alignment
Magnetic resonance imaging
Medical imaging
Motion analysis
Motion control
Cardiac magnetic resonance
Cardiac magnetic resonance images
Effective approaches
Iterative closest point algorithm
Motion correction
Myocardial infarction patients
Quantitative assessments
Quantitative validation
Iterative methods
cardiovascular magnetic resonance
cine magnetic resonance imaging
clinical article
computer system
heart infarction
heart left ventricle
image display
image processing
image reconstruction
imaging software
imaging system
middle aged
nuclear magnetic resonance scanner
priority journal
quantitative study
radiological procedures
validation study
anatomy and histology
diagnostic imaging
heart ventricle
movement (physiology)
nuclear magnetic resonance imaging
Heart Ventricles
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Middle Aged
Issue Date: 2016
Citation: Wan, M, Huang, W, Zhang, J.-M, Zhao, X, Allen, J.C, Tan, R.S, Wan, X, Zhong, L (2016). Correcting motion in multiplanar cardiac magnetic resonance images. BioMedical Engineering Online 15 (1) : 93. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: Background: Misalignment in cardiac magnetic resonance (CMR) images can adversely affect three-dimensional left ventricle modelling and downstream quantitative analysis. Currently, there are two types of approaches for dealing with realignment and motion distortion problems, one image based and the other geometry based. Image-based approaches are limited by the inherent non-homogeneity and anisotropy of CMR images. Geometry-based approaches rely on idealized models and over-simplified assumptions. This study was motivated by the need for a robust and effective approach for correcting motion related distortions due to misalignment in CMR images. Methods: A cine cardiac magnetic resonance image sequence was acquired using our routine clinical imaging protocol. The left ventricular endocardium was delineated manually with software assistance on all long and short-axis images. Long and short-axis contours were projected onto a patient-based coordinate system and then realigned using iterative registration. The realigned contour points were used to reconstruct the shape of the left ventricle for quantitative validation. Results: The method was tested on five myocardial infarction patients whose images showed substantial misalignment. Realignment time was about 16 seconds per case, using a 2.5 GHz CPU desktop with obvious elimination of the distortion in the reconstructed model. Using the long-axis contour as a reference in evaluating the reconstructed models, it was apparent that the models with realigned contours had better accuracy than the non-realigned ones. Conclusion: This study presents a novel, geometry-based method for correcting motion distortions in CMR images. The method incorporates (1) manual delineation, (2) registration based on a generalized, iterative closest point algorithm, and (3) reconstruction of the shape of the left ventricle for quantitative validation. The effectiveness of our approach is corroborated both visually and by quantitative assessment. We envision the use of our method in current clinical practice as a means of improving accuracy in the evaluation of cardiac function. © 2016 The Author(s).
Source Title: BioMedical Engineering Online
ISSN: 1475925X
DOI: 10.1186/s12938-016-0216-8
Rights: Attribution 4.0 International
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