Image-based reconstruction of three-dimensional myocardial infarct geometry for patient-specific modeling of cardiac electrophysiology.

PURPOSE Accurate three-dimensional (3D) reconstruction of myocardial infarct geometry is crucial to patient-specific modeling of the heart aimed at providing therapeutic guidance in ischemic cardiomyopathy. However, myocardial infarct imaging is clinically performed using two-dimensional (2D) late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) techniques, and a method to build accurate 3D infarct reconstructions from the 2D LGE-CMR images has been lacking. The purpose of this study was to address this need. METHODS The authors developed a novel methodology to reconstruct 3D infarct geometry from segmented low-resolution (Lo-res) clinical LGE-CMR images. Their methodology employed the so-called logarithm of odds (LogOdds) function to implicitly represent the shape of the infarct in segmented image slices as LogOdds maps. These 2D maps were then interpolated into a 3D image, and the result transformed via the inverse of LogOdds to a binary image representing the 3D infarct geometry. To assess the efficacy of this method, the authors utilized 39 high-resolution (Hi-res) LGE-CMR images, including 36 in vivo acquisitions of human subjects with prior myocardial infarction and 3 ex vivo scans of canine hearts following coronary ligation to induce infarction. The infarct was manually segmented by trained experts in each slice of the Hi-res images, and the segmented data were downsampled to typical clinical resolution. The proposed method was then used to reconstruct 3D infarct geometry from the downsampled images, and the resulting reconstructions were compared with the manually segmented data. The method was extensively evaluated using metrics based on geometry as well as results of electrophysiological simulations of cardiac sinus rhythm and ventricular tachycardia in individual hearts. Several alternative reconstruction techniques were also implemented and compared with the proposed method. RESULTS The accuracy of the LogOdds method in reconstructing 3D infarct geometry, as measured by the Dice similarity coefficient, was 82.10% ± 6.58%, a significantly higher value than those of the alternative reconstruction methods. Among outcomes of electrophysiological simulations with infarct reconstructions generated by various methods, the simulation results corresponding to the LogOdds method showed the smallest deviation from those corresponding to the manual reconstructions, as measured by metrics based on both activation maps and pseudo-ECGs. CONCLUSIONS The authors have developed a novel method for reconstructing 3D infarct geometry from segmented slices of Lo-res clinical 2D LGE-CMR images. This method outperformed alternative approaches in reproducing expert manual 3D reconstructions and in electrophysiological simulations.

[1]  Alexandra Branzan Albu,et al.  A Morphology-Based Approach for Interslice Interpolation of Anatomical Slices From Volumetric Images , 2008, IEEE Transactions on Biomedical Engineering.

[2]  Carolyn A. Bucholtz,et al.  Shape-based interpolation , 1992, IEEE Computer Graphics and Applications.

[3]  A Prakosa,et al.  Methodology for image-based reconstruction of ventricular geometry for patient-specific modeling of cardiac electrophysiology. , 2014, Progress in biophysics and molecular biology.

[4]  Jeroen J. Bax,et al.  Impact of viability and scar tissue on response to cardiac resynchronization therapy in ischaemic heart failure patients. , 2006, European heart journal.

[5]  W. R. Buckland,et al.  A dictionary of statistical terms , 1958 .

[6]  Yuesong Yang,et al.  Automated quantification of myocardial infarction using graph cuts on contrast delayed enhanced magnetic resonance images. , 2012, Quantitative imaging in medicine and surgery.

[7]  Angel Arenal,et al.  Do the spatial characteristics of myocardial scar tissue determine the risk of ventricular arrhythmias? , 2012, Cardiovascular research.

[8]  Eric Boersma,et al.  Relative Merits of Left Ventricular Dyssynchrony, Left Ventricular Lead Position, and Myocardial Scar to Predict Long-Term Survival of Ischemic Heart Failure Patients Undergoing Cardiac Resynchronization Therapy , 2011, Circulation.

[9]  Gernot Plank,et al.  Automatically Generated, Anatomically Accurate Meshes for Cardiac Electrophysiology Problems , 2009, IEEE Transactions on Biomedical Engineering.

[10]  W G Stevenson,et al.  Programmed electrical stimulation of the heart in patients with life-threatening ventricular arrhythmias: what is the significance of induced arrhythmias and what is the correct stimulation protocol? , 1985, Circulation.

[11]  Wu Qiu,et al.  Myocardial Infarct Segmentation and Reconstruction from 2D Late-Gadolinium Enhanced Magnetic Resonance Images , 2014, MICCAI.

[12]  Gernot Plank,et al.  Three‐dimensional mechanisms of increased vulnerability to electric shocks in myocardial infarction: Altered virtual electrode polarizations and conduction delay in the peri‐infarct zone , 2012, The Journal of physiology.

[13]  Dwight G Nishimura,et al.  Rapid single‐breath‐hold 3D late gadolinium enhancement cardiac MRI using a stack‐of‐spirals acquisition , 2014, Journal of magnetic resonance imaging : JMRI.

[14]  D. Geman,et al.  Computational Medicine: Translating Models to Clinical Care , 2012 .

[15]  Nira Dyn,et al.  Reconstruction of 3D objects from 2D cross-sections with the 4-point subdivision scheme adapted to sets , 2011, Comput. Graph..

[16]  L. Formaggia,et al.  Shape reconstruction from medical images and quality mesh generation via implicit surfaces , 2007 .

[17]  Amedeo Chiribiri,et al.  Risk stratification of post-MI patients for ICD implantation using texture analysis to quantify heterogeneity of scar , 2015, Journal of Cardiovascular Magnetic Resonance.

[18]  Natalia A. Trayanova,et al.  Computational Cardiology: The Heart of the Matter , 2012, ISRN cardiology.

[19]  J. Pu,et al.  Alterations of Na+ currents in myocytes from epicardial border zone of the infarcted heart. A possible ionic mechanism for reduced excitability and postrepolarization refractoriness. , 1997, Circulation research.

[20]  W. Eric L. Grimson,et al.  Using the logarithm of odds to define a vector space on probabilistic atlases , 2007, Medical Image Anal..

[21]  Yoram Rudy,et al.  Ionic mechanisms of electrophysiological heterogeneity and conduction block in the infarct border zone. , 2010, American journal of physiology. Heart and circulatory physiology.

[22]  Viatcheslav Gurev,et al.  Modeling of Whole-Heart Electrophysiology and Mechanics: Toward Patient-Specific Simulations , 2010 .

[23]  David E. Breen,et al.  Contour-Based Surface Reconstruction using Implicit Curve Fitting, and Distance Field Filtering and Interpolation , 2006, VG@SIGGRAPH.

[24]  Terry M. Peters,et al.  Interactive Hierarchical-Flow Segmentation of Scar Tissue From Late-Enhancement Cardiac MR Images , 2014, IEEE Transactions on Medical Imaging.

[25]  G. Plank,et al.  The role of fine-scale anatomical structure in the dynamics of reentry in computational models of the rabbit ventricles , 2012, The Journal of physiology.

[26]  Scott D Flamm,et al.  Standardized image interpretation and post processing in cardiovascular magnetic resonance: Society for Cardiovascular Magnetic Resonance (SCMR) Board of Trustees Task Force on Standardized Post Processing , 2013, Journal of Cardiovascular Magnetic Resonance.

[27]  G Plank,et al.  Computational tools for modeling electrical activity in cardiac tissue. , 2003, Journal of electrocardiology.

[28]  Sanjay Dixit,et al.  Quantitative comparison of spontaneous and paced 12-lead electrocardiogram during right ventricular outflow tract ventricular tachycardia. , 2003, Journal of the American College of Cardiology.

[29]  Shigeo Baba,et al.  Dynamic remodeling of K+ and Ca2+ currents in cells that survived in the epicardial border zone of canine healed infarcted heart. , 2004, American journal of physiology. Heart and circulatory physiology.

[30]  N. Trayanova,et al.  A Computational Model to Predict the Effects of Class I Anti-Arrhythmic Drugs on Ventricular Rhythms , 2011, Science Translational Medicine.

[31]  Richard D. White,et al.  Segmentation of non-viable myocardium in delayed enhancement magnetic resonance images , 2005, The International Journal of Cardiovascular Imaging.

[32]  I. Efimov,et al.  Conduction Remodeling in Human End-Stage Non-Ischemic Left Ventricular Cardiomyopathy Running title : , 2012 .

[33]  N. Trayanova,et al.  Systems Approach to Understanding Electromechanical Activity in the Human Heart: A National Heart, Lung, and Blood Institute Workshop Summary , 2008, Circulation.

[34]  Kevin W Eliceiri,et al.  NIH Image to ImageJ: 25 years of image analysis , 2012, Nature Methods.

[35]  Natalia A. Trayanova,et al.  Abstract 18014: Image-Based Patient-Specific Simulations of Ventricular Electrophysiology for Sudden Arrhythmic Death Risk Stratification , 2013 .

[36]  Michael I. Miller,et al.  Image-Based Estimation of Ventricular Fiber Orientations for Personalized Modeling of Cardiac Electrophysiology , 2012, IEEE Transactions on Medical Imaging.

[37]  Ron Kikinis,et al.  Statistical validation of image segmentation quality based on a spatial overlap index. , 2004, Academic radiology.

[38]  Nicholas S. Peters,et al.  Remodeling of Gap Junctional Channel Function in Epicardial Border Zone of Healing Canine Infarcts , 2003, Circulation research.

[39]  Yogesh Rathi,et al.  Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow , 2007, IEEE Transactions on Image Processing.

[40]  Katherine C. Wu,et al.  Infarct Tissue Heterogeneity by Magnetic Resonance Imaging Identifies Enhanced Cardiac Arrhythmia Susceptibility in Patients With Left Ventricular Dysfunction , 2007, Circulation.

[41]  Elliot R. McVeigh,et al.  Estimation of ventricular fiber orientations in infarcted hearts for patient-specific simulations , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[42]  Terry M. Peters,et al.  Comparison of semi-automated scar quantification techniques using high-resolution, 3-dimensional late-gadolinium-enhancement magnetic resonance imaging , 2015, The International Journal of Cardiovascular Imaging.

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

[44]  R G Gould,et al.  Left atrial volume determination by biplane two-dimensional echocardiography: validation by cine computed tomography. , 1991, American heart journal.

[45]  D. Durrer,et al.  Total Excitation of the Isolated Human Heart , 1970, Circulation.

[46]  Edward Vigmond,et al.  Towards predictive modelling of the electrophysiology of the heart , 2009, Experimental physiology.

[47]  J J Rice,et al.  Distribution of electromechanical delay in the heart: insights from a three-dimensional electromechanical model. , 2010, Biophysical journal.

[48]  Viatcheslav Gurev,et al.  Models of cardiac electromechanics based on individual hearts imaging data , 2011, Biomechanics and modeling in mechanobiology.

[49]  J. Udupa,et al.  Shape-based interpolation of multidimensional objects. , 1990, IEEE transactions on medical imaging.

[50]  Raymond J Kim,et al.  Infarct morphology identifies patients with substrate for sustained ventricular tachycardia. , 2005, Journal of the American College of Cardiology.

[51]  Natalia A Trayanova,et al.  The role of photon scattering in optical signal distortion during arrhythmia and defibrillation. , 2007, Biophysical journal.

[52]  Jennifer Keegan,et al.  Free-breathing 3D late gadolinium enhancement imaging of the left ventricle using a stack of spirals at 3T , 2014, Journal of magnetic resonance imaging : JMRI.

[53]  Xianghua Xie,et al.  Integrated Segmentation and Interpolation of Sparse Data , 2014, IEEE Transactions on Image Processing.

[54]  G. Plank,et al.  A Novel Rule-Based Algorithm for Assigning Myocardial Fiber Orientation to Computational Heart Models , 2012, Annals of Biomedical Engineering.

[55]  D. Mozaffarian,et al.  Heart disease and stroke statistics--2014 update: a report from the American Heart Association. , 2014, Circulation.

[56]  Sebastian Kozerke,et al.  Acute, subacute, and chronic myocardial infarction: quantitative comparison of 2D and 3D late gadolinium enhancement MR imaging. , 2011, Radiology.

[57]  Gernot Plank,et al.  From mitochondrial ion channels to arrhythmias in the heart: computational techniques to bridge the spatio-temporal scales , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[58]  Hiroshi Ashikaga,et al.  Feasibility of image-based simulation to estimate ablation target in human ventricular arrhythmia. , 2013, Heart rhythm.

[59]  Vivek Muthurangu,et al.  Evaluation of techniques for the quantification of myocardial scar of differing etiology using cardiac magnetic resonance. , 2011, JACC. Cardiovascular imaging.

[60]  Vijay K. Devabhaktuni,et al.  Accurate reconstruction of 3D cardiac geometry from coarsely-sliced MRI , 2014, Comput. Methods Programs Biomed..

[61]  Daniel J. Perry,et al.  Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge , 2013, Journal of Cardiovascular Magnetic Resonance.

[62]  Gernot Plank,et al.  Tachycardia in Post-Infarction Hearts: Insights from 3D Image-Based Ventricular Models , 2013, PloS one.

[63]  John F. Hughes,et al.  Scheduled Fourier volume morphing , 1992, SIGGRAPH.

[64]  James F. O'Brien,et al.  Shape transformation using variational implicit functions , 1999, SIGGRAPH 1999.

[65]  Natalia A. Trayanova,et al.  Computational techniques for solving the bidomain equations in three dimensions , 2002, IEEE Transactions on Biomedical Engineering.

[66]  D. Noble,et al.  A model for human ventricular tissue. , 2004, American journal of physiology. Heart and circulatory physiology.

[67]  James F. O'Brien,et al.  Shape transformation using variational implicit functions , 1999, SIGGRAPH Courses.

[68]  R. Kociol,et al.  Relative Merits of Left Ventricular Dyssynchrony , Left Ventricular Lead Position , and Myocardial Scar to Predict Long-Term Survival of Ischemic Heart Failure Patients Undergoing Cardiac Resynchronization Therapy , 2012 .

[69]  C. Cabo,et al.  Delayed rectifier K currents have reduced amplitudes and altered kinetics in myocytes from infarcted canine ventricle. , 2000, Cardiovascular research.