Registration of dynamic multiview 2D ultrasound and late gadolinium enhanced images of the heart: Application to hypertrophic cardiomyopathy characterization

Describing and analyzing heart multiphysics requires the acquisition and fusion of multisensor cardiac images. Multisensor image fusion enables a combined analysis of these heterogeneous modalities. We propose to register intra-patient multiview 2D+t ultrasound (US) images with multiview late gadolinium-enhanced (LGE) images acquired during cardiac magnetic resonance imaging (MRI), in order to fuse mechanical and tissue state information. The proposed procedure registers both US and LGE to cine MRI. The correction of slice misalignment and the rigid registration of multiview LGE and cine MRI are studied, to select the most appropriate similarity measure. It showed that mutual information performs the best for LGE slice misalignment correction and for LGE and cine registration. Concerning US registration, dynamic endocardial contours resulting from speckle tracking echocardiography were exploited in a geometry-based dynamic registration. We propose the use of an adapted dynamic time warping procedure to synchronize cardiac dynamics in multiview US and cine MRI. The registration of US and LGE MRI was evaluated on a dataset of patients with hypertrophic cardiomyopathy. A visual assessment of 330 left ventricular regions from US images of 28 patients resulted in 92.7% of regions successfully aligned with cardiac structures in LGE. Successfully-aligned regions were then used to evaluate the abilities of strain indicators to predict the presence of fibrosis. Longitudinal peak-strain and peak-delay of aligned left ventricular regions were computed from corresponding regional strain curves from US. The Mann-Withney test proved that the expected values of these indicators change between the populations of regions with and without fibrosis (p < 0.01). ROC curves otherwise proved that the presence of fibrosis is one factor amongst others which modifies longitudinal peak-strain and peak-delay.

[1]  E. G. Caiani,et al.  Automated motion artifacts removal between cardiac long- and short-axis magnetic resonance images , 2012, 2012 Computing in Cardiology.

[2]  Barry J Maron,et al.  2011 ACCF/AHA guideline for the diagnosis and treatment of hypertrophic cardiomyopathy: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. , 2011, The Journal of thoracic and cardiovascular surgery.

[3]  Olivier Gérard,et al.  Integrating functional and anatomical information to guide cardiac resynchronization therapy , 2010, European journal of heart failure.

[4]  P. Grenier,et al.  Comparison of various methods for quantitative evaluation of myocardial infarct volume from magnetic resonance delayed enhancement data. , 2013, International journal of cardiology.

[5]  Claudio Landoni,et al.  Spatial registration of echocardiographic and positron emission tomographic heart studies , 1995, European Journal of Nuclear Medicine.

[6]  Bostjan Likar,et al.  A protocol for evaluation of similarity measures for rigid registration , 2006, IEEE Transactions on Medical Imaging.

[7]  Barry J Maron,et al.  2011 ACCF/AHA Guideline for the Diagnosis and Treatment of Hypertrophic Cardiomyopathy: A Report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines , 2011, Circulation.

[8]  Dong Wei,et al.  A Comprehensive 3-D Framework for Automatic Quantification of Late Gadolinium Enhanced Cardiac Magnetic Resonance Images , 2013, IEEE Transactions on Biomedical Engineering.

[9]  G. W. Hughes,et al.  Minimum Prediction Residual Principle Applied to Speech Recognition , 1975 .

[10]  Alejandro F. Frangi,et al.  3D fusion of cine and late-enhanced cardiac magnetic resonance images , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[11]  Jing Ren,et al.  Dynamic 2D Ultrasound and 3D CT Image Registration of the Beating Heart , 2009, IEEE Transactions on Medical Imaging.

[12]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[13]  Antoine Simon,et al.  Spatio-temporal Registration of 2D US and 3D MR Images for the Characterization of Hypertrophic Cardiomyopathy , 2013, FIMH.

[14]  Jing Ren,et al.  Intra-cardiac 2D US to 3D CT image registration , 2007, SPIE Medical Imaging.

[15]  Antoine Simon,et al.  Multimodal Registration and Data Fusion for Cardiac Resynchronization Therapy Optimization , 2014, IEEE Transactions on Medical Imaging.

[16]  Qi Zhang,et al.  Real-time visualization of 4D cardiac MR images using graphics processing units , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[17]  H. Okayama,et al.  Clinical significance of global two-dimensional strain as a surrogate parameter of myocardial fibrosis and cardiac events in patients with hypertrophic cardiomyopathy. , 2012, European heart journal cardiovascular Imaging.

[18]  D. Berman,et al.  Patient motion correction for multiplanar, multi‐breath‐hold cardiac cine MR imaging , 2007, Journal of magnetic resonance imaging : JMRI.

[19]  Antoine Simon,et al.  Synchronization and Registration of Cine Magnetic Resonance and Dynamic Computed Tomography Images of the Heart , 2016, IEEE Journal of Biomedical and Health Informatics.

[20]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[21]  Barry J Maron,et al.  2011 ACCF/AHA Guideline for the Diagnosis and Treatment of Hypertrophic Cardiomyopathy: Executive Summary A Report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines , 2011, Circulation.

[22]  Heinz-Otto Peitgen,et al.  A Comprehensive Approach to the Analysis of Contrast Enhanced Cardiac MR Images , 2008, IEEE Transactions on Medical Imaging.

[23]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[24]  Nassir Navab,et al.  Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention , 2008, Medical Image Anal..

[25]  Paul Suetens,et al.  Automatic 3-D Breath-Hold Related Motion Correction of Dynamic Multislice MRI , 2010, IEEE Transactions on Medical Imaging.

[26]  Scott D Flamm,et al.  Association between regional ventricular function and myocardial fibrosis in hypertrophic cardiomyopathy assessed by speckle tracking echocardiography and delayed hyperenhancement magnetic resonance imaging. , 2008, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[27]  Nassir Navab,et al.  Entropy and Laplacian images: Structural representations for multi-modal registration , 2012, Medical Image Anal..

[28]  Alban Redheuil,et al.  Robust assessment of the transmural extent of myocardial infarction in late gadolinium-enhanced MRI studies using appropriate angular and circumferential subdivision of the myocardium , 2008, European Radiology.

[29]  David Atkinson,et al.  A study of the motion and deformation of the heart due to respiration , 2002, IEEE Transactions on Medical Imaging.

[30]  C. Prieto The Problem of Motion in Cardiovascular MRI , 2013 .

[31]  Jyrki Lötjönen,et al.  Correction of motion artifacts from cardiac cine magnetic resonance images. , 2005, Academic radiology.