Analytic signal phase-based myocardial motion estimation in tagged MRI sequences by a bilinear model and motion compensation

被引:10
|
作者
Wang, Liang [1 ]
Basarab, Adrian [2 ]
Girard, Patrick R. [1 ]
Croisille, Pierre [1 ,3 ]
Clarysse, Patrick [1 ]
Delachartre, Philippe [1 ]
机构
[1] Univ Lyon 1, CREATIS, CNRS, INSERM,U1044,INSA Lyon,UMR 5220, F-69621 Villeurbanne, France
[2] Univ Toulouse, IRIT, CNRS, UMR 5505, F-31062 Toulouse 9, France
[3] Univ St Etienne, Univ Hosp St Etienne, Dept Radiol, St Etienne, France
关键词
Motion estimation; Iterative bilinear model; Phase invariance assumption; Analytic signal; Cardiac motion and strains; OPTICAL-FLOW ESTIMATION; DISPLACEMENT; TRACKING; COMPLEX; HEART; DENSE;
D O I
10.1016/j.media.2015.06.005
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Different mathematical tools, such as multidimensional analytic signals, allow for the calculation of 2D spatial phases of real-value images. The motion estimation method proposed in this paper is based on two spatial phases of the 2D analytic signal applied to cardiac sequences. By combining the information of these phases issued from analytic signals of two successive frames, we propose an analytical estimator for 20 local displacements. To improve the accuracy of the motion estimation, a local bilinear deformation model is used within an iterative estimation scheme. The main advantages of our method are: (1) The phase-based method allows the displacement to be estimated with subpixel accuracy and is robust to image intensity variation in time; (2) Preliminary filtering is not required due to the bilinear model. The proposed algorithm, integrating phase-based optical flow motion estimation and the combination of global motion compensation with local bilinear transform, allows spatio-temporal cardiac motion analysis, e.g. strain and dense trajectory estimation over the cardiac cycle. Results from 7 realistic simulated tagged magnetic resonance imaging (MRI) sequences show that our method is more accurate compared with state-of-the-art method for cardiac motion analysis and with another differential approach from the literature. The motion estimation errors (end point error) of the proposed method are reduced by about 33% compared with that of the two methods. In our work, the frame-to-frame displacements are further accumulated in time, to allow for the calculation of myocardial Lagrangian cardiac strains and point trajectories. Indeed, from the estimated trajectories in time on 11 in vivo data sets (9 patients and 2 healthy volunteers), the shape of myocardial point trajectories belonging to pathological regions are clearly reduced in magnitude compared with the ones from normal regions. Myocardial point trajectories, estimated from our phase-based analytic signal approach, seem therefore a good indicator of the local cardiac dynamics. Moreover, they are shown to be coherent with the estimated deformation of the myocardium. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:149 / 162
页数:14
相关论文
共 50 条
  • [41] Modal Parameter Estimation of a Compliant Panel Using Phase-based Motion Magnification and Stereoscopic Digital Image Correlation
    M. Eitner
    M. Musta
    L. Vanstone
    J. Sirohi
    N. Clemens
    [J]. Experimental Techniques, 2021, 45 : 287 - 296
  • [42] Joint Myocardial Motion and Contraction Phase Estimation from Cine MRI Using Variational Data Assimilation
    Tuyisenge, Viateur
    Sarry, Laurent
    Corpetti, Thomas
    Innorta-Coupez, Elisabeth
    Ouchchane, Lemlih
    Cassagnes, Lucie
    [J]. Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, 2015, 8896 : 187 - 195
  • [43] A Markov random field model for 3D left-ventricular motion estimation from tagged MRI
    Chen, YS
    Amini, AA
    [J]. MEDICAL IMAGING 2002: PHYSIOLOGY AND FUNCTION FROM MULTIDIMENSIONAL IMAGES, 2002, 4683 : 150 - 159
  • [44] Modal Parameter Estimation of a Compliant Panel Using Phase-based Motion Magnification and Stereoscopic Digital Image Correlation
    Eitner, M.
    Musta, M.
    Vanstone, L.
    Sirohi, J.
    Clemens, N.
    [J]. EXPERIMENTAL TECHNIQUES, 2021, 45 (03) : 287 - 296
  • [45] Phase-based motion estimation in complex environments using the illumination-invariant log-Gabor filter
    Wang, Yuchao
    Hu, Weihua
    Teng, Jun
    Xia, Yong
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 186
  • [46] Phase-Based Block Matching Applied to Motion Estimation with Unconventional Beamforming Strategies (vol 56, pg 945, 2009)
    Basarab, Adrian
    Gueth, Pierre
    Liebgott, Herve
    Delachartre, Philippe
    [J]. IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL, 2009, 56 (06) : 1289 - 1289
  • [47] A novel marker for robust and accurate phase-based 2D motion estimation from noisy image data
    Miao, Yinan
    Kong, Yeseul
    Jeon, Jun Young
    Nam, Hyeonwoo
    Park, Gyuhae
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 187
  • [48] Gaussian mixture model based phase prior learning for video motion estimation
    Cai, Enjian
    Zhang, Yi
    [J]. Mechanical Systems and Signal Processing, 2022, 175
  • [49] TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation for MRI Using a Reduced Model Joint Optimization
    Haskell, Melissa W.
    Cauley, Stephen F.
    Wald, Lawrence L.
    [J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 2018, 37 (05) : 1253 - 1265
  • [50] Gaussian mixture model based phase prior learning for video motion estimation
    Cai, Enjian
    Zhang, Yi
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2022, 175