Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of scars provide important information of the pathophysiology and progression of atrial fibrillation (AF). Hence, LA scar segmentation and quantification from LGE MRI can be useful in computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineation can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail, and summarize the validation strategies applied in each task. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review shows that the research into this topic is still in early stages. Although several methods have been proposed, especially for LA segmentation, there is still large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.Comment: 23 page

Automatic segmentation of wall structures from cardiac images

One important topic in medical image analysis is segmenting wall structures from different cardiac medical imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). This task is typically done by radiologists either manually or semi-automatically, which is a very time-consuming process. To reduce the laborious human efforts, automatic methods have become popular in this research. In this thesis, features insensitive to data variations are explored to segment the ventricles from CT images and extract the left atrium from MR images. As applications, the segmentation results are used to facilitate cardiac disease analysis. Specifically, 1. An automatic method is proposed to extract the ventricles from CT images by integrating surface decomposition with contour evolution techniques. In particular, the ventricles are first identified on a surface extracted from patient-specific image data. Then, the contour evolution is employed to refine the identified ventricles. The proposed method is robust to variations of ventricle shapes, volume coverages, and image quality. 2. A variational region-growing method is proposed to segment the left atrium from MR images. Because of the localized property of this formulation, the proposed method is insensitive to data variabilities that are hard to handle by globalized methods. 3. In applications, a geometrical computational framework is proposed to estimate the myocardial mass at risk caused by stenoses. In addition, the segmentation of the left atrium is used to identify scars for MR images of post-ablation.PhDCommittee Chair: Yezzi, Anthony; Committee Co-Chair: Tannenbaum, Allen; Committee Member: Egerstedt, Magnus ; Committee Member: Fedele, Francesco ; Committee Member: Stillman, Arthur; Committee Member: Vela,Patrici

Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms

Patienten mit Vorhofflimmern sind einem fünffach erhöhten Risiko für einen ischämischen Schlaganfall ausgesetzt. Eine frühzeitige Erkennung und Diagnose der Arrhythmie würde ein rechtzeitiges Eingreifen ermöglichen, um möglicherweise auftretende Begleiterkrankungen zu verhindern. Eine Vergrößerung des linken Vorhofs sowie fibrotisches Vorhofgewebe sind Risikomarker für Vorhofflimmern, da sie die notwendigen Voraussetzungen für die Aufrechterhaltung der chaotischen elektrischen Depolarisation im Vorhof erfüllen. Mithilfe von Techniken des maschinellen Lernens könnten Fibrose und eine Vergrößerung des linken Vorhofs basierend auf P Wellen des 12-Kanal Elektrokardiogramms im Sinusrhythmus automatisiert identifiziert werden. Dies könnte die Basis für eine nicht-invasive Risikostrat- ifizierung neu auftretender Vorhofflimmerepisoden bilden, um anfällige Patienten für ein präventives Screening auszuwählen. Zu diesem Zweck wurde untersucht, ob simulierte Vorhof-Elektrokardiogrammdaten, die dem klinischen Trainingssatz eines maschinellen Lernmodells hinzugefügt wurden, zu einer verbesserten Klassifizierung der oben genannten Krankheiten bei klinischen Daten beitra- gen könnten. Zwei virtuelle Kohorten, die durch anatomische und funktionelle Variabilität gekennzeichnet sind, wurden generiert und dienten als Grundlage für die Simulation großer P Wellen-Datensätze mit genau bestimmbaren Annotationen der zugrunde liegenden Patholo- gie. Auf diese Weise erfüllen die simulierten Daten die notwendigen Voraussetzungen für die Entwicklung eines Algorithmus für maschinelles Lernen, was sie von klinischen Daten unterscheidet, die normalerweise nicht in großer Zahl und in gleichmäßig verteilten Klassen vorliegen und deren Annotationen möglicherweise durch unzureichende Expertenannotierung beeinträchtigt sind. Für die Schätzung des Volumenanteils von linksatrialem fibrotischen Gewebe wurde ein merkmalsbasiertes neuronales Netz entwickelt. Im Vergleich zum Training des Modells mit nur klinischen Daten, führte das Training mit einem hybriden Datensatz zu einer Reduzierung des Fehlers von durchschnittlich 17,5 % fibrotischem Volumen auf 16,5 %, ausgewertet auf einem rein klinischen Testsatz. Ein Long Short-Term Memory Netzwerk, das für die Unterscheidung zwischen gesunden und P Wellen von vergrößerten linken Vorhöfen entwickelt wurde, lieferte eine Genauigkeit von 0,95 wenn es auf einem hybriden Datensatz trainiert wurde, von 0,91 wenn es nur auf klinischen Daten trainiert wurde, die alle mit 100 % Sicherheit annotiert wurden, und von 0,83 wenn es auf einem klinischen Datensatz trainiert wurde, der alle Signale unabhängig von der Sicherheit der Expertenannotation enthielt. In Anbetracht der Ergebnisse dieser Arbeit können Elektrokardiogrammdaten, die aus elektrophysiologischer Modellierung und Simulationen an virtuellen Patientenkohorten resul- tieren und relevante Variabilitätsaspekte abdecken, die mit realen Beobachtungen übereinstim- men, eine wertvolle Datenquelle zur Verbesserung der automatisierten Risikostratifizierung von Vorhofflimmern sein. Auf diese Weise kann den Nachteilen klinischer Datensätze für die Entwicklung von Modellen des maschinellen Lernens entgegengewirkt werden. Dies trägt letztendlich zu einer frühzeitigen Erkennung der Arrhythmie bei, was eine rechtzeitige Auswahl geeigneter Behandlungsstrategien ermöglicht und somit das Schlaganfallrisiko der betroffenen Patienten verringert

Real-time whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

Cardiac digital twins provide a physics and physiology informed framework to deliver predictive and personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs and the high number of model evaluations needed for patient-specific personalization. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. In this work, we use Latent Neural Ordinary Differential Equations (LNODEs) to learn the temporal pressure-volume dynamics of a heart failure patient. Our surrogate model based on LNODEs is trained from 400 3D-0D whole-heart closed-loop electromechanical simulations while accounting for 43 model parameters, describing single cell through to whole organ and cardiovascular hemodynamics. The trained LNODEs provides a compact and efficient representation of the 3D-0D model in a latent space by means of a feedforward fully-connected Artificial Neural Network that retains 3 hidden layers with 13 neurons per layer and allows for 300x real-time numerical simulations of the cardiac function on a single processor of a standard laptop. This surrogate model is employed to perform global sensitivity analysis and robust parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor. We match pressure and volume time traces unseen by the LNODEs during the training phase and we calibrate 4 to 11 model parameters while also providing their posterior distribution. This paper introduces the most advanced surrogate model of cardiac function available in the literature and opens new important venues for parameter calibration in cardiac digital twins

Design and development of optical reflectance spectroscopy and optical coherence tomography catheters for myocardial tissue characterization

Catheter ablation therapy attempts to restore sinus rhythm in arrhythmia patients by producing site-specific tissue modification along regions which cause abnormal electrical activity. This treatment, though widely used, often requires repeat procedures to observe long-term therapeutic benefits. This limitation is driven in part by challenges faced by conventional schemes in validating lesion adequacy at the time of the procedure. Optical techniques are well-suited for the interrogation and characterization of biological tissues. In particular, optical coherence tomography (OCT) relies on coherence gating of singly-scattered light to enable high-resolution structural imaging for tissue diagnostics and procedural guidance. Alternatively, optical reflectance spectroscopy (ORS) is a point measurement technique which makes use of incoherent, multiply-scattered light to probe tissue volumes and derive important data from its optical signature. ORS relies on the fact that light-tissue interactions are regulated by absorption and scattering, which directly relate to the intrinsic tissue biochemistry and cellular organization. In this thesis, we explore the integration of these modalities into ablation catheters for obtaining procedural metrics which could be utilized to guide catheter ablation therapy. We first present the development of an accelerated computational light transport model and its application for guiding ORS catheter design. A custom ORS-integrated ablation catheter is then implemented and tested within porcine specimens in vitro. A model is proposed for real-time estimation of lesion size based on changes in spectral morphology acquired during ablation. We then fabricated custom integrated OCT M-mode RF catheters and present a model for detecting contact status based on deep convolutional neural networks trained on endomyocardial images. Additionally, we demonstrate for the first time, tracking of RF-induced lesion formation employing OCT Doppler micro-velocimetry; this response is shown to be commensurate with the degree of treatment. We further demonstrate for the first time spectroscopic tracking of kinetics related to the heme oxidation cascade during thermal treatment, which are linked to tissue denaturation. The pairing of these modalities into a single RF catheter was also validated for guiding lesion delivery in vitro and within live pigs. Finally, we conclude with a proof-of-concept demonstration of ORS as a mapping tool to guide epicardial ablation in human donor hearts. These results showcase the vast potential of ORS and OCT empowered RF catheters for aiding intraprocedural guidance of catheter ablation procedures which could be utilized alongside current practices

Computer-Assisted Electroanatomical Guidance for Cardiac Electrophysiology Procedures

Cardiac arrhythmias are serious life-threatening episodes affecting both the aging population and younger patients with pre-existing heart conditions. One of the most effective therapeutic procedures is the minimally-invasive catheter-driven endovascular electrophysiology study, whereby electrical potentials and activation patterns in the affected cardiac chambers are measured and subsequent ablation of arrhythmogenic tissue is performed. Despite emerging technologies such as electroanatomical mapping and remote intraoperative navigation systems for improved catheter manipulation and stability, successful ablation of arrhythmias is still highly-dependent on the operator’s skills and experience. This thesis proposes a framework towards standardisation in the electroanatomical mapping and ablation planning by merging knowledge transfer from previous cases and patient-specific data. In particular, contributions towards four different procedural aspects were made: optimal electroanatomical mapping, arrhythmia path computation, catheter tip stability analysis, and ablation simulation and optimisation. In order to improve the intraoperative electroanatomical map, anatomical areas of high mapping interest were proposed, as learned from previous electrophysiology studies. Subsequently, the arrhythmic wave propagation on the endocardial surface and potential ablation points were computed. The ablation planning is further enhanced, firstly by the analysis of the catheter tip stability and the probability of slippage at sparse locations on the endocardium and, secondly, by the simulation of the ablation result from the computation of convolutional matrices which model mathematically the ablation process. The methods proposed by this thesis were validated on data from patients with complex congenital heart disease, who present unusual cardiac anatomy and consequently atypical arrhythmias. The proposed methods also build a generic framework for computer guidance of electrophysiology, with results showing complementary information that can be easily integrated into the clinical workflow.Open Acces

Three-dimensional cardiac computational modelling: methods, features and applications

[EN] The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.This work was partially supported by the "VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica" from the Ministerio de Economia y Competitividad of Spain (TIN2012-37546-C03-01 and TIN2011-28067) and the European Commission (European Regional Development Funds - ERDF - FEDER) and by "eTorso project" (GVA/2013-001404) from the Generalitat Valenciana (Spain). ALP is financially supported by the program "Ayudas para contratos predoctorales para la formacion de doctores" from the Ministerio de Economia y Competitividad of Spain (BES-2013-064089).López Pérez, AD.; Sebastián Aguilar, R.; Ferrero De Loma-Osorio, JM. (2015). Three-dimensional cardiac computational modelling: methods, features and applications. BioMedical Engineering OnLine. 14(35):1-31. https://doi.org/10.1186/s12938-015-0033-5S1311435Koushanpour E, Collings W: Validation and dynamic applications of an ellipsoid model of the left ventricle. J Appl Physiol 1966, 21: 1655–61.Ghista D, Sandler H: An analytic elastic-viscoelastic model for the shape and the forces in the left ventricle. J Biomech 1969, 2: 35–47.Janz RF, Grimm AF: Finite-Element Model for the Mechanical Behavior of the Left Ventricle: prediction of deformation in the potassium-arrested rat heart. Circ Res 1972, 30: 244–52.Van den Broek JHJM, Van den Broek MHLM: Application of an ellipsoidal heart model in studying left ventricular contractions. J Biomech 1980, 13: 493–503.Colli Franzone P, Guerri L, Pennacchio M, Taccardi B: Spread of excitation in 3-D models of the anisotropic cardiac tissue. II. Effects of fiber architecture and ventricular geometry. Math Biosci 1998, 147: 131–71.Kerckhoffs RCP, Bovendeerd PHM, Kotte JCS, Prinzen FW, Smits K, Arts T: Homogeneity of cardiac contraction despite physiological asynchrony of depolarization: a model study. Ann Biomed Eng 2003, 31: 536–47.Sermesant M, Moireau P, Camara O, Sainte-Marie J, Andriantsimiavona R, Cimrman R, et al.: Cardiac function estimation from MRI using a heart model and data assimilation: advances and difficulties. Med Image Anal 2006, 10: 642–56.Okajima M, Fujino T, Kobayashi T, Yamada K: Computer simulation of the propagation process in excitation of the ventricles. Circ Res 1968, 23: 203–11.Horan LG, Hand RC, Johnson JC, Sridharan MR, Rankin TB, Flowers NC: A theoretical examination of ventricular repolarization and the secondary T wave. Circ Res 1978, 42: 750–7.Miller WT, Geselowitz DB: Simulation studies of the electrocardiogram. I. The normal heart. Circ Res 1978, 43: 301–15.Vetter FJ, McCulloch AD: Three-dimensional analysis of regional cardiac function: a model of rabbit ventricular anatomy. Prog Biophys Mol Biol 1998, 69: 157–83.Nielsen PMF, LeGrice IJ, Smaill BH, Hunter PJ: Mathematical model of geometry and fibrous structure of the heart. Am J Physiol Heart Circ Physiol 1991, 260: H1365–78.Stevens C, Remme E, LeGrice I, Hunter P: Ventricular mechanics in diastole: material parameter sensitivity. J Biomech 2003, 36: 737–48.Aoki M, Okamoto Y, Musha T, Harumi KI: Three-dimensional simulation of the ventricular depolarization and repolarization processes and body surface potentials: normal heart and bundle branch block. IEEE Trans Biomed Eng 1987, 34: 454–62.Thakor NV, Eisenman LN: Three-dimensional computer model of the heart: fibrillation induced by extrastimulation. Comput Biomed Res 1989, 22: 532–45.Freudenberg J, Schiemann T, Tiede U, Höhne KH: Simulation of cardiac excitation patterns in a three-dimensional anatomical heart atlas. Comput Biol Med 2000, 30: 191–205.Trunk P, Mocnik J, Trobec R, Gersak B: 3D heart model for computer simulations in cardiac surgery. Comput Biol Med 2007, 37: 1398–403.Siregar P, Sinteff JP, Julen N, Le Beux P: An interactive 3D anisotropic cellular automata model of the heart. Comput Biomed Res 1998, 31: 323–47.Harrild DM, Henriquez CS: A computer model of normal conduction in the human atria. Circ Res 2000, 87: e25–36.Bodin ON, Kuz’min AV: Synthesis of a realistic model of the surface of the heart. Biomed Eng (NY) 2006, 40: 280–3.Ruiz-Villa CA, Tobón C, Rodríguez JF, Ferrero JM, Hornero F, Saíz J: Influence of atrial dilatation in the generation of re-entries caused by ectopic activity in the left atrium. Comput Cardiol 2009, 36: 457–60.Blanc O, Virag N, Vesin JM, Kappenberger L: A computer model of human atria with reasonable computation load and realistic anatomical properties. IEEE Trans Biomed Eng 2001, 48: 1229–37.Zemlin CW, Herzel H, Ho SY, Panfilov AV: A realistic and efficient model of excitation propagation in the human atria. In Comput Simul Exp Assess Card Electrophysiol. Edited by: Virag N, Kappenberger L, Blanc O. Futura Publishing Company, Inc, Arkmonk, New York; 2001:29–34.Seemann G, Höper C, Sachse FB, Dössel O, Holden AV, Zhang H: Heterogeneous three-dimensional anatomical and electrophysiological model of human atria. Philos Trans R Soc A Math Phys Eng Sci 2006, 364: 1465–81.Zhao J, Butters TD, Zhang H, LeGrice IJ, Sands GB, Smaill BH: Image-based model of atrial anatomy and electrical activation: a computational platform for investigating atrial arrhythmia. IEEE Trans Med Imaging 2013, 32: 18–27.Creswell LL, Wyers SG, Pirolo JS, Perman WH, Vannier MW, Pasque MK: Mathematical modeling of the heart using magnetic resonance imaging. IEEE Trans Med Imaging 1992, 11: 581–9.Lorange M, Gulrajani RM: A computer heart model incorporating anisotropic propagation: I. Model construction and simulation of normal activation. J Electrocardiol 1993, 26: 245–61.Winslow RL, Scollan DF, Holmes A, Yung CK, Zhang J, Jafri MS: Electrophysiological modeling of cardiac ventricular function: from cell to organ. Annu Rev Biomed Eng 2000, 2: 119–55.Virag N, Jacquemet V, Henriquez CS, Zozor S, Blanc O, Vesin JM, et al.: Study of atrial arrhythmias in a computer model based on magnetic resonance images of human atria. Chaos 2002, 12: 754–63.Helm PA, Tseng HJ, Younes L, McVeigh ER, Winslow RL: Ex vivo 3D diffusion tensor imaging and quantification of cardiac laminar structure. Magn Reson Med 2005, 54: 850–9.Arevalo HJ, Helm PA, Trayanova NA: Development of a model of the infarcted canine heart that predicts arrhythmia generation from specific cardiac geometry and scar distribution. Comput Cardiol 2008, 35: 497–500.Plotkowiak M, Rodriguez B, Plank G, Schneider JE, Gavaghan D, Kohl P, et al.: High performance computer simulations of cardiac electrical function based on high resolution MRI datasets. In Int Conf Comput Sci 2008, LNCS 5101. Springer–Verlag, Berlin Heidelberg; 2008:571–80.Heidenreich EA, Ferrero JM, Doblaré M, Rodríguez JF: Adaptive macro finite elements for the numerical solution of monodomain equations in cardiac electrophysiology. Ann Biomed Eng 2010, 38: 2331–45.Gurev V, Lee T, Constantino J, Arevalo H, Trayanova NA: Models of cardiac electromechanics based on individual hearts imaging data: Image-based electromechanical models of the heart. Biomech Model Mechanobiol 2011, 10: 295–306.Deng D, Jiao P, Ye X, Xia L: An image-based model of the whole human heart with detailed anatomical structure and fiber orientation. Comput Math Methods Med 2012, 2012: 16.Aslanidi OV, Nikolaidou T, Zhao J, Smaill BH, Gilbert SH, Holden AV, et al.: Application of micro-computed tomography with iodine staining to cardiac imaging, segmentation, and computational model development. IEEE Trans Med Imaging 2013, 32: 8–17.Haddad R, Clarysse P, Orkisz M, Croisille P, Revel D, Magnin IE: A realistic anthropomorphic numerical model of the beating heart. In Funct Imaging Model Heart 2005, LNCS 3504. Springer–Verlag, Berlin Heidelberg; 2005:384–93.Appleton B, Wei Q, Liu N, Xia L, Crozier S, Liu F, et al.: An electrical heart model incorporating real geometry and motion. In 27th Annu Int Conf Eng Med Biol Soc (IEEE-EMBS 2005). IEEE, Shanghai, China; 2006:345–8.Niederer S, Rhode K, Razavi R, Smith N: The importance of model parameters and boundary conditions in whole organ models of cardiac contraction. In Funct Imaging Model Heart 2009, LNCS 5528. Springer–Verlag, Berlin Heidelberg; 2009:348–56.Yang G, Toumoulin C, Coatrieux JL, Shu H, Luo L, Boulmier D: A 3D static heart model from a MSCT data set. In 27th Annu Int Conf IEEE Eng Med Biol Soc (IEEE-EMBS 2005). IEEE, Shangai, China; 2006:5499–502.Romero D, Sebastian R, Bijnens BH, Zimmerman V, Boyle PM, Vigmond EJ, et al.: Effects of the purkinje system and cardiac geometry on biventricular pacing: a model study. Ann Biomed Eng 2010, 38: 1388–98.Lorenzo-Valdés M, Sanchez-Ortiz GI, Mohiaddin R, Rueckert D: Atlas-based segmentation and tracking of 3D cardiac MR images using non-rigid registration. In Med Image Comput Comput Assist Interv 2002, LNCS 2488. Springer–Verlag, Berlin Heidelberg; 2002:642–50.Ordas S, Oubel E, Sebastian R, Frangi AF: Computational anatomy atlas of the heart. In 5th Int Symp Image Signal Process Anal (ISPA 2007). IEEE, Istanbul, Turkey; 2007:338–42.Burton RAB, Plank G, Schneider JE, Grau V, Ahammer H, Keeling SL, et al.: Three-dimensional models of individual cardiac histoanatomy: tools and challenges. Ann N Y Acad Sci 2006, 1080: 301–19.Plank G, Burton RAB, Hales P, Bishop M, Mansoori T, Bernabeu MO, et al.: Generation of histo-anatomically representative models of the individual heart: tools and application. Philos Trans R Soc A Math Phys Eng Sci 2009, 367: 2257–92.Bishop MJ, Plank G, Burton RAB, Schneider JE, Gavaghan DJ, Grau V, et al.: Development of an anatomically detailed MRI-derived rabbit ventricular model and assessment of its impact on simulations of electrophysiological function. Am J Physiol - Heart Circ Physiol 2010, 298: H699–718.Ecabert O, Peters J, Schramm H, Lorenz C, von Berg J, Walker MJ, et al.: Automatic model-based segmentation of the heart in CT images. IEEE Trans Med Imaging 2008, 27: 1189–201.Ecabert O, Peters J, Walker MJ, Ivanc T, Lorenz C, von Berg J, et al.: Segmentation of the heart and great vessels in CT images using a model-based adaptation framework. Med Image Anal 2011, 15: 863–76.Schulte RF, Sands GB, Sachse FB, Dössel O, Pullan AJ: Creation of a human heart model and its customisation using ultrasound images. Biomed Tech Eng 2001, 46: 26–8.Wenk JF, Zhang Z, Cheng G, Malhotra D, Acevedo-Bolton G, Burger M, et al.: First finite element model of the left ventricle with mitral valve: insights into ischemic mitral regurgitation. Ann Thorac Surg 2010, 89: 1546–53.Frangi AF, Rueckert D, Schnabel JA, Niessen WJ: Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling. IEEE Trans Med Imaging 2002, 21: 1151–66.Hoogendoorn C, Duchateau N, Sánchez-Quintana D, Whitmarsh T, Sukno FM, De Craene M, et al.: A high-resolution atlas and statistical model of the human heart from multislice CT. IEEE Trans Med Imaging 2013, 32: 28–44.Vadakkumpadan F, Rantner LJ, Tice B, Boyle P, Prassl AJ, Vigmond E, et al.: Image-based models of cardiac structure with applications in arrhythmia and defibrillation studies. J Electrocardiol 2009, 42: 157.Perperidis D, Mohiaddin R, Rueckert D: Construction of a 4D statistical atlas of the cardiac anatomy and its use in classification. In Med Image Comput Comput Interv 2005, LNCS 3750. Springer–Verlag, Berlin Heidelberg; 2005:402–10.Lötjönen J, Kivistö S, Koikkalainen J, Smutek D, Lauerma K: Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images. Med Image Anal 2004, 8: 371–86.Lorenz C, von Berg J: A comprehensive shape model of the heart. Med Image Anal 2006, 10: 657–70.Mansoori T, Plank G, Burton R, Schneider J, Khol P, Gavaghan D, et al.: An iterative method for registration of high-resolution cardiac histoanatomical and MRI images. In 4th IEEE Int Symp Biomed Imaging: From Nano to Macro (ISBI 2007). IEEE, Arlington, VA (USA); 2007:572–5.Gibb M, Burton RAB, Bollensdorff C, Afonso C, Mansoori T, Schotten U, et al.: Resolving the three-dimensional histology of the heart. In Comput Methods Syst Biol - Lect Notes Comput Sci 7605. Springer, Berlin Heidelberg; 2012:2–16.Burton RAB, Lee P, Casero R, Garny A, Siedlecka U, Schneider JE, et al.: Three-dimensional histology: tools and application to quantitative assessment of cell-type distribution in rabbit heart. Europace 2014,16(Suppl 4):iv86–95.Niederer SA, Shetty AK, Plank G, Bostock J, Razavi R, Smith NP, et al.: Biophysical modeling to simulate the response to multisite left ventricular stimulation using a quadripolar pacing lead. Pacing Clin Electrophysiol 2012, 35: 204–14.Weese J, Groth A, Nickisch H, Barschdorf H, Weber FM, Velut J, et al.: Generating anatomical models of the heart and the aorta from medical images for personalized physiological simulations. Med Biol Eng Comput 2013, 51: 1209–19.Gibb M, Bishop M, Burton R, Kohl P, Grau V, Plank G, et al.: The role of blood vessels in rabbit propagation dynamics and cardiac arrhythmias. In Funct Imaging Model Heart - FIMH 2009, LNCS 5528. Springer, Berlin Heidelberg; 2009:268–76.Prassl AJ, Kickinger F, Ahammer H, Grau V, Schneider JE, Hofer E, et al.: Automatically generated, anatomically accurate meshes for cardiac electrophysiology problems. IEEE Trans Biomed Eng 2009, 56: 1318–30.Dux-Santoy L, Sebastian R, Felix-Rodriguez J, Ferrero JM, Saiz J: Interaction of specialized cardiac conduction system with antiarrhythmic drugs: a simulation study. IEEE Trans Biomed Eng 2011, 58: 3475–8.Lamata P, Niederer S, Nordsletten D, Barber DC, Roy I, Hose DR, et al.: An accurate, fast and robust method to generate patient-specific cubic Hermite meshes. Med Image Anal 2011, 15: 801–13.Pathmanathan P, Cooper J, Fletcher A, Mirams G, Murray P, Osborne J, et al.: A computational study of discrete mechanical tissue models. Phys Biol 2009, 6: 036001.Niederer SA, Kerfoot E, Benson AP, Bernabeu MO, Bernus O, Bradley C, et al.: Verification of cardiac tissue electrophysiology simulators using an N-version benchmark. Philos Trans R Soc A Math Phys Eng Sci 2011, 369: 4331–51.Ten Tusscher KHWJ, Panfilov AV: Cell model for efficient simulation of wave propagation in human ventricular tissue under normal and pathological conditions. Phys Med Biol 2006, 51: 6141–56.LeGrice I, Smaill B, Chai L, Edgar S, Gavin J, Hunter P: Laminar structure of the heart: ventricular myocyte arrangement and connective tissue architecture in the dog. Am J Physiol Heart Circ Physiol 1995, 269: H571–82.Anderson RH, Smerup M, Sanchez-Quintana D, Loukas M, Lunkenheimer PP: The three-dimensional arrangement of the myocytes in the ventricular walls. Clin Anat 2009, 22: 64–76.Clerc L: Directional differences of impulse spread in trabecular muscle from mammalian heart. J Physiol 1976, 255: 335–46.Streeter DD Jr, Spotnitz HM, Patel DP, Ross J Jr, Sonnenblick EH: Fiber orientation in the canine left ventricle during diastole and systole. Circ Res 1969, 24: 339–47.Scollan D, Holmes A, Winslow R, Forder J: Histological validation of myocardial microstructure obtained from diffusion tensor magnetic resonance imaging. Am J Physiol Heart Circ Physiol 1998, 275: H2308–18.Hsu EW, Muzikant AL, Matulevicius SA, Penland RC, Henriquez CS: Magnetic resonance myocardial fiber-orientation mapping with direct histological correlation. Am J Physiol Heart Circ Physiol 1998, 274: H1627–34.Holmes AA, Scollan DF, Winslow RL: Direct histological validation of diffusion tensor MRI in formaldehyde-fixed myocardium. Magn Reson Med 2000, 44: 157–61.Sermesant M, Forest C, Pennec X, Delingette H, Ayache N: Deformable biomechanical models: application to 4D cardiac image analysis. Med Image Anal 2003, 7: 475–88.Peyrat JM, Sermesant M, Pennec X, Delingette H, Xu C, McVeigh ER, et al.: A computational framework for the statistical analysis of cardiac diffusion tensors: application to a small database of canine hearts. IEEE Trans Med Imaging 2007, 26: 1500–14.Toussaint N, Sermesant M, Stoeck CT, Kozerke S, Batchelor PG: In vivo human 3D cardiac fibre architecture: reconstruction using curvilinear interpolation of diffusion tensor images. Med Image Comput Comput Assist Interv 2010,13(Pt 1):418–25.Toussaint N, Stoeck CT, Schaeffter T, Kozerke S, Sermesant M, Batchelor PG: In vivo human cardiac fibre architecture estimation using shape-based diffusion tensor processing. Med Image Anal 2013, 17: 1243–55.Bishop MJ, Hales P, Plank G, Gavaghan DJ, Scheider J, Grau V: Comparison of rule-based and DTMRI-derived fibre architecture in a whole rat ventricular computational model. In Funct Imaging Model Heart 2009, LNCS 5528. Springer–Verlag, Berlin Heidelberg; 2009:87–96.Bayer JD, Blake RC, Plank G, Trayanova NA: A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models. Ann Biomed Eng 2012, 40: 2243–54.Dobrzynski H, Anderson RH, Atkinson A, Borbas Z, D’Souza A, Fraser JF, et al.: Structure, function and clinical relevance of the cardiac conduction system, including the atrioventricular ring and outflow tract tissues. Pharmacol Ther 2013, 139: 260–88.Tranum-Jensen J, Wilde AA, Vermeulen JT, Janse MJ: Morphology of electrophysiologically identified junctions between Purkinje fibers and ventricular muscle in rabbit and pig hearts. Circ Res 1991, 69: 429–37.Boyle PM, Deo M, Plank G, Vigmond EJ: Purkinje-mediated effects in the response of quiescent ventricles to defibrillation shocks. Ann Biomed Eng 2010, 38: 456–68.Behradfar E, Nygren A, Vigmond EJ: The role of Purkinje-myocardial coupling during ventricular arrhythmia: a modeling study. PLoS One 2014., 9: Article ID e88000DiFrancesco D, Noble D: A model of cardiac electrical activity incorporating ionic pumps and concentration changes. Philos Trans R Soc B Biol Sci 1985, 307: 353–98.Stewart P, Aslanidi OV, Noble D, Noble PJ, Boyett MR, Zhang H: Mathematical models of the electrical action potential of Purkinje fibre cells. Philos Trans R Soc A Math Phys Eng Sci 2009, 367: 2225–55.Li P, Rudy Y: A model of canine purkinje cell electrophysiology and Ca(2+) cycling: rate dependence, triggered activity, and comparison to ventricular myocytes. Circ Res 2011, 109: 71–9.Chinchapatnam P, Rhode KS, Ginks M, Mansi T, Peyrat JM, Lambiase P, et al.: Estimation of volumetric myocardial apparent conductivity from endocardial electro-anatomical mapping. In 31st Annu Int Conf IEEE Eng Med Biol Soc (EMBC 2009). IEEE, Minneapolis, MN (USA); 2009:2907–10.Durrer D, Van Dam RT, Freud GE, Janse MJ, Meijler FL, Arzbaecher RC: Total excitation of the isolated human heart. Circulation 1970, 41: 899–912.Pollard AE, Barr RC: Computer simulations of activation in an anatomically based model of the human ventricular conduction system. IEEE Trans Biomed Eng 1991, 38: 982–96.Abboud S, Berenfeld O, Sadeh D: Simulation of high-resolution QRS complex using a ventricular model with a fractal conduction system. Effects of ischemia on high-frequency QRS potentials. Circ Res 1991, 68: 1751–60.Sebastian R, Zimmerman V, Romero D, Sanchez-Quintana D, Frangi AF: Characterization and modeling of the peripheral cardiac conduction system. IEEE Trans Med Imaging 2013, 32: 45–55.Bordas R, Gillow K, Lou Q, Efimov IR, Gavaghan D, Kohl P, et al.: Rabbit-specific ventricular model of cardiac electrophysiological function including specialized conduction system. Prog Biophys Mol Biol 2011, 107: 90–100.Stephenson RS, Boyett MR, Hart G, Nikolaidou T, Cai X, Corno AF, et al.: Contrast enhanced micro-computed tomography resolves the 3-dimensional morphology of the cardiac conduction system in mammalian hearts. PLoS One 2012., 7: Article ID e35299Berenfeld O, Jalife J: Purkinje-Muscle reentry as a mechanism of polymorphic ventricular arrhythmias in a 3-dimensional model of the ventricles. Circ Res 1998, 82: 1063–77.Azzouzi A, Coudière Y, Turpault R, Zemzemi N: A mathematical model of the Purkinje-muscle junctions. Math Biosci Eng MBE 2011, 8: 915–30.Dux-Santoy L, Sebastian R, Rodriguez JF, Ferrero JM: Modeling the different sections of the cardiac conduction system to obtain realistic electrocardiograms. In 35th Annu Int Conf IEEE Eng Med Biol Soc (EMBC 2013). IEEE, Osaka, Japan; 2013:6846–9.Cardenes R, Sebastian R, Berruezo A, Camara O: Inverse

Multi-modality cardiac image computing: a survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future