title: Correcting motion in multiplanar cardiac magnetic resonance images.
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.
  
  
  CONCLUSIONS
  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.
authors: Nanchang University, No. 999, Xuefu Dadao, Nanchang, People's Republic of China.
authors: Min
authors: M
authors: Wan
authors: Nanchang University, No. 999, Xuefu Dadao, Nanchang, People's Republic of China.
authors: Wei
authors: W
authors: Huang
authors: National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.|Duke-NUS Medical School Singapore, 8 College Road, Singapore, 169857, Singapore.
authors: Jun-Mei
authors: JM
authors: Zhang
authors: National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
authors: Xiaodan
authors: X
authors: Zhao
authors: Duke-NUS Medical School Singapore, 8 College Road, Singapore, 169857, Singapore.
authors: John Carson
authors: JC
authors: Allen
authors: National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.|Duke-NUS Medical School Singapore, 8 College Road, Singapore, 169857, Singapore.
authors: Ru San
authors: RS
authors: Tan
authors: Nanchang University, No. 999, Xuefu Dadao, Nanchang, People's Republic of China.
authors: Xiaofeng
authors: X
authors: Wan
authors: National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore. zhong.liang@nhcs.com.sg.|Duke-NUS Medical School Singapore, 8 College Road, Singapore, 169857, Singapore. zhong.liang@nhcs.com.sg.
authors: Liang
authors: L
authors: Zhong
mesh_terms: D000368:Aged; D000465:Algorithms; D006321:Heart / Q000033:anatomy & histology / Q000000981:diagnostic imaging* / Q000502:physiology; D006352:Heart Ventricles / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D009068:Movement
keywords: Cardiac magnetic resonance (CMR); Motion correction