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Atlas-Based Computational Analysis of Heart Shape and Function in Congenital Heart Disease

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Abstract

Approximately 1% of all babies are born with some form of congenital heart defect. Many serious forms of CHD can now be surgically corrected after birth, which has led to improved survival into adulthood. However, many patients require serial monitoring to evaluate progression of heart failure and determine timing of interventions. Accurate multidimensional quantification of regional heart shape and function is required for characterizing these patients. A computational atlas of single ventricle and biventricular heart shape and function enables quantification of remodeling in terms of z scores in relation to specific reference populations. Progression of disease can then be monitored effectively by longitudinal evaluation of z scores. A biomechanical analysis of cardiac function in relation to population variation enables investigation of the underlying mechanisms for developing pathology. Here, we summarize recent progress in this field, with examples in single ventricle and biventricular congenital pathologies.

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References

  1. Marelli, A. J., Mackie, A. S., Ionescu-Ittu, R., Rahme, E., & Pilote, L. (2007). Congenital heart disease in the general population: changing prevalence and age distribution. Circulation, 115(2), 163–172.

    Article  PubMed  Google Scholar 

  2. Roest, A. A., & de Roos, A. (2012). Imaging of patients with congenital heart disease. [review]. Nature Reviews. Cardiology, 9(2), 101–115. https://doi.org/10.1038/nrcardio.2011.162.

    Article  CAS  Google Scholar 

  3. Geva, T. (2011). Repaired tetralogy of Fallot: the roles of cardiovascular magnetic resonance in evaluating pathophysiology and for pulmonary valve replacement decision support. Journal of Cardiovascular Magnetic Resonance, 13, 9.

    Article  PubMed  PubMed Central  Google Scholar 

  4. McKenzie, E. D., Khan, M. S., Dietzman, T. W., Guzman-Pruneda, F. A., Samayoa, A. X., Liou, A., et al. (2014). Surgical pulmonary valve replacement: a benchmark for outcomes comparisons. The Journal of Thoracic and Cardiovascular Surgery, 148(4), 1450–1453. https://doi.org/10.1016/j.jtcvs.2014.02.060.

    Article  PubMed  Google Scholar 

  5. Coats, L., O'Connor, S., Wren, C., & O'Sullivan, J. (2014). The single-ventricle patient population: a current and future concern a population-based study in the north of England. [research support, non-U.S. gov't]. Heart, 100(17), 1348–1353. https://doi.org/10.1136/heartjnl-2013-305336.

    Article  PubMed  Google Scholar 

  6. Iyengar, A. J., Winlaw, D. S., Galati, J. C., Gentles, T. L., Weintraub, R. G., Justo, R. N., et al. (2014). The Australia and New Zealand Fontan Registry: description and initial results from the first population-based Fontan registry. Internal Medicine Journal, 44(2), 148–155. https://doi.org/10.1111/imj.12318.

    Article  CAS  PubMed  Google Scholar 

  7. Kalb, B., Indik, J. H., Ott, P., & Martin, D. R. (2017). MRI of patients with implanted cardiac devices. [review]. Journal of Magnetic Resonance Imaging. https://doi.org/10.1002/jmri.25824.

  8. Kharabish, A., Mkrtchyan, N., Meierhofer, C., Martinoff, S., Ewert, P., Stern, H., et al. (2016). Cardiovascular magnetic resonance is successfully feasible in many patients aged 3 to 8 years without general anesthesia or sedation. [comparative study]. Journal of Clinical Anesthesia, 34, 11–14. https://doi.org/10.1016/j.jclinane.2016.02.048.

    Article  PubMed  Google Scholar 

  9. Olivieri, L., Cross, R., O'Brien, K. J., Xue, H., Kellman, P., & Hansen, M. S. (2016). Free-breathing motion-corrected late-gadolinium-enhancement imaging improves image quality in children. Pediatric Radiology, 46(7), 983–990. https://doi.org/10.1007/s00247-016-3553-7.

    Article  PubMed  Google Scholar 

  10. Schulz-Menger, J., Bluemke, D. A., Bremerich, J., Flamm, S. D., Fogel, M. A., Friedrich, M. G., et al. (2013). 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. Journal of Cardiovascular Magnetic Resonance, 15, 35.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Hudsmith, L. E., Petersen, S. E., Francis, J. M., Robson, M. D., & Neubauer, S. (2005). Normal human left and right ventricular and left atrial dimensions using steady state free precession magnetic resonance imaging. Journal of Cardiovascular Magnetic Resonance, 7(5), 775–782.

    Article  PubMed  Google Scholar 

  12. Pattynama, P. M., Lamb, H. J., Van der Velde, E. A., Van der Geest, R. J., Van der Wall, E. E., & De Roos, A. (1995). Reproducibility of MRI-derived measurements of right ventricular volumes and myocardial mass. Magnetic Resonance Imaging, 13(1), 53–63.

    Article  CAS  PubMed  Google Scholar 

  13. Mooij, C. F., de Wit, C. J., Graham, D. A., Powell, A. J., & Geva, T. (2008). Reproducibility of MRI measurements of right ventricular size and function in patients with normal and dilated ventricles. Journal of Magnetic Resonance Imaging, 28(1), 67–73. https://doi.org/10.1002/jmri.21407.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Nguyen, K. L., Han, F., Zhou, Z., Brunengraber, D. Z., Ayad, I., Levi, D. S., et al. (2017). 4D MUSIC CMR: value-based imaging of neonates and infants with congenital heart disease. Journal of Cardiovascular Magnetic Resonance, 19(1), 40. https://doi.org/10.1186/s12968-017-0352-8.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Feng, L., Coppo, S., Piccini, D., Yerly, J., Lim, R. P., Masci, P. G., et al. (2017). 5D whole-heart sparse MRI. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.26745.

  16. Peng, P., Lekadir, K., Gooya, A., Shao, L., Petersen, S. E., & Frangi, A. F. (2016). A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magma, 29(2), 155–195. https://doi.org/10.1007/s10334-015-0521-4.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Petitjean, C., & Dacher, J. N. (2011). A review of segmentation methods in short axis cardiac MR images. Medical Image Analysis, 15(2), 169–184.

    Article  PubMed  Google Scholar 

  18. Avendi, M. R., Kheradvar, A., & Jafarkhani, H. (2016). A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. [research support, non-U.S. gov't]. Medical Image Analysis, 30, 108–119. https://doi.org/10.1016/j.media.2016.01.005.

    Article  CAS  PubMed  Google Scholar 

  19. Tan, L. K., Liew, Y. M., Lim, E., & McLaughlin, R. A. (2017). Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences. Medical Image Analysis, 39, 78–86. https://doi.org/10.1016/j.media.2017.04.002.

    Article  PubMed  Google Scholar 

  20. Avendi, M. R., Kheradvar, A., & Jafarkhani, H. (2017). Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.26631.

  21. Suinesiaputra, A., Bluemke, D. A., Cowan, B. R., Friedrich, M. G., Kramer, C. M., Kwong, R., et al. (2015). Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours. [research support, N.I.H., extramural]. Journal of Cardiovascular Magnetic Resonance, 17(1), 63. https://doi.org/10.1186/s12968-015-0170-9.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Frangi, A. F., Niessen, W. J., & Viergever, M. A. (2001). Three-dimensional modeling for functional analysis of cardiac images: a review. IEEE Transactions on Medical Imaging, 20(1), 2–25. https://doi.org/10.1109/42.906421.

    Article  CAS  PubMed  Google Scholar 

  23. Young, A. A., & Frangi, A. F. (2009). Computational cardiac atlases: from patient to population and back. Experimental Physiology, 94(5), 578–596.

    Article  PubMed  Google Scholar 

  24. Medrano-Gracia, P., Cowan, B. R., Ambale-Venkatesh, B., Bluemke, D. A., Eng, J., Finn, J. P., et al. (2014). Left ventricular shape variation in asymptomatic populations: the multi-ethnic study of atherosclerosis. Journal of Cardiovascular Magnetic Resonance, 16, 56.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Lamata, P., Niederer, S., Nordsletten, D., Barber, D. C., Roy, I., Hose, D. R., et al. (2011). An accurate, fast and robust method to generate patient-specific cubic Hermite meshes. Medical Image Analysis, 15(6), 801–813.

    Article  PubMed  Google Scholar 

  26. Sheehan, F. H., Bolson, E. L., Martin, R. W., Bashein, G., & McDonald, J. (1998). Quantitative three dimensional echocardiography: Methodology, validation, and clinical applications. In W. M. Wells, A. Colchester, & S. Delp (Eds.), Medical Image Computing and Computer-Assisted Intervention—MICCAI’98 Springer.

  27. Chandrashekara, R., Mohiaddin, R., Razavi, R., & Rueckert, D. (2007). Nonrigid image registration with subdivision lattices: application to cardiac MR image analysis. Med Image Comput Comput Assist Interv, 10(Pt 1), 335–342.

    CAS  PubMed  Google Scholar 

  28. Stebbing, R. V., Namburete, A. I., Upton, R., Leeson, P., & Noble, J. A. (2015). Data-driven shape parameterization for segmentation of the right ventricle from 3D+t echocardiography. Medical Image Analysis, 21(1), 29–39. https://doi.org/10.1016/j.media.2014.12.002.

    Article  PubMed  Google Scholar 

  29. Morcos, P., Vick 3rd, G. W., Sahn, D. J., Jerosch-Herold, M., Shurman, A., & Sheehan, F. H. (2009). Correlation of right ventricular ejection fraction and tricuspid annular plane systolic excursion in tetralogy of Fallot by magnetic resonance imaging. The International Journal of Cardiovascular Imaging, 25(3), 263–270. https://doi.org/10.1007/s10554-008-9387-0.

    Article  PubMed  Google Scholar 

  30. Morcos, M., & Sheehan, F. H. (2013). Regional right ventricular wall motion in tetralogy of Fallot: a three dimensional analysis. The International Journal of Cardiovascular Imaging, 29(5), 1051–1058. https://doi.org/10.1007/s10554-012-0178-2.

    Article  PubMed  Google Scholar 

  31. Moroseos, T., Mitsumori, L., Kerwin, W. S., Sahn, D. J., Helbing, W. A., Kilner, P. J., et al. (2010). Comparison of Simpson's method and three-dimensional reconstruction for measurement of right ventricular volume in patients with complete or corrected transposition of the great arteries. The American Journal of Cardiology, 105(11), 1603–1609. https://doi.org/10.1016/j.amjcard.2010.01.025.

    Article  PubMed  Google Scholar 

  32. Lee, C. M., Sheehan, F. H., Bouzas, B., Chen, S. S., Gatzoulis, M. A., & Kilner, P. J. (2013). The shape and function of the right ventricle in Ebstein's anomaly. International Journal of Cardiology, 167(3), 704–710. https://doi.org/10.1016/j.ijcard.2012.03.062.

    Article  PubMed  Google Scholar 

  33. Chabiniok, R., Wang, V. Y., Hadjicharalambous, M., Asner, L., Lee, J., Sermesant, M., et al. (2016). Multiphysics and multiscale modelling, data–model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics. Interface Focus, 6, 20150083.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Young, A. A., Hunter, P. J., & Smaill, B. H. (1992). Estimation of epicardial strain using the motions of coronary bifurcations in biplane cineangiography. IEEE Transactions on Biomedical Engineering, 39(5), 526–531. https://doi.org/10.1109/10.135547.

    Article  CAS  PubMed  Google Scholar 

  35. Young, A. A., Orr, R., Smaill, B. H., & Dell'Italia, L. J. (1996). Three-dimensional changes in left and right ventricular geometry in chronic mitral regurgitation. The American Journal of Physiology, 271(6 Pt 2), H2689–H2700.

    CAS  PubMed  Google Scholar 

  36. Li, B., Liu, Y., Occleshaw, C. J., Cowan, B. R., & Young, A. A. (2010). In-line automated tracking for ventricular function with magnetic resonance imaging. JACC. Cardiovascular Imaging, 3(8), 860–866.

    Article  PubMed  Google Scholar 

  37. Young, A. A., Cowan, B. R., Thrupp, S. F., Hedley, W. J., & Dell'Italia, L. J. (2000). Left ventricular mass and volume: fast calculation with guide-point modeling on MR images. Radiology, 216(2), 597–602.

    Article  CAS  PubMed  Google Scholar 

  38. Gilbert, K., Lam, H. I., Pontre, B., Cowan, B. R., Occleshaw, C. J., Liu, J. Y., et al. (2017). An interactive tool for rapid biventricular analysis of congenital heart disease. Clinical Physiology and Functional Imaging, 37(4), 413–420. https://doi.org/10.1111/cpf.12319.

    Article  CAS  PubMed  Google Scholar 

  39. Peyrat, J. M., Delingette, H., Sermesant, M., Xu, C., & Ayache, N. (2010). Registration of 4D cardiac CT sequences under trajectory constraints with multichannel diffeomorphic demons. [research support, non-U.S. gov't]. IEEE Transactions on Medical Imaging, 29(7), 1351–1368. https://doi.org/10.1109/TMI.2009.2038908.

    Article  PubMed  Google Scholar 

  40. Bai, W., Shi, W., de Marvao, A., Dawes, T. J., O'Regan, D. P., Cook, S. A., et al. (2015). A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion. [research support, non-U.S. gov't]. Medical Image Analysis, 26(1), 133–145. https://doi.org/10.1016/j.media.2015.08.009.

    Article  PubMed  Google Scholar 

  41. Chandrashekara, R., Rao, A., Sanchez-Ortiz, G. I., Mohiaddin, R. H., & Rueckert, D. (2003). Construction of a statistical model for cardiac motion analysis using nonrigid image registration. Inf Process Med Imaging, 18, 599–610.

    Article  PubMed  Google Scholar 

  42. Cootes, T. F., Hill, A., Taylor, C. F., & Halsam, J. (1994). The use of active shape models for locating structures in medical images. Image and Vision Computing, 12(6), 355–366.

    Article  Google Scholar 

  43. Mitchell, S. C., Bosch, J. G., Lelieveldt, B. P., van der Geest, R. J., Reiber, J. H., & Sonka, M. (2002). 3-D active appearance models: segmentation of cardiac MR and ultrasound images. IEEE Transactions on Medical Imaging, 21(9), 1167–1178. https://doi.org/10.1109/TMI.2002.804425.

    Article  PubMed  Google Scholar 

  44. Bai, W., Shi, W., Ledig, C., & Rueckert, D. (2015). Multi-atlas segmentation with augmented features for cardiac MR images. Medical Image Analysis, 19(1), 98–109. https://doi.org/10.1016/j.media.2014.09.005.

    Article  PubMed  Google Scholar 

  45. Sheehan, F. H., Kilner, P. J., Sahn, D. J., Vick 3rd, G. W., Stout, K. K., Ge, S., et al. (2010). Accuracy of knowledge-based reconstruction for measurement of right ventricular volume and function in patients with tetralogy of Fallot. The American Journal of Cardiology, 105(7), 993–999. https://doi.org/10.1016/j.amjcard.2009.11.032.

    Article  PubMed  Google Scholar 

  46. Nyns, E. C., Dragulescu, A., Yoo, S. J., & Grosse-Wortmann, L. (2014). Evaluation of knowledge-based reconstruction for magnetic resonance volumetry of the right ventricle in tetralogy of Fallot. [observational study]. Pediatric Radiology, 44(12), 1532–1540. https://doi.org/10.1007/s00247-014-3042-9.

    Article  PubMed  Google Scholar 

  47. Nyns, E. C., Dragulescu, A., Yoo, S. J., & Grosse-Wortmann, L. (2016). Evaluation of knowledge-based reconstruction for magnetic resonance volumetry of the right ventricle after arterial switch operation for dextro-transposition of the great arteries. The International Journal of Cardiovascular Imaging, 32(9), 1415–1423. https://doi.org/10.1007/s10554-016-0921-1.

    Article  PubMed  Google Scholar 

  48. Morcos, M., Kilner, P. J., Sahn, D. J., Litt, H. I., Valsangiacomo-Buechel, E. R., & Sheehan, F. H. (2017). Comparison of systemic right ventricular function in transposition of the great arteries after atrial switch and congenitally corrected transposition of the great arteries. The International Journal of Cardiovascular Imaging. https://doi.org/10.1007/s10554-017-1201-4.

  49. Zhang, X., Cowan, B. R., Bluemcke, D. A., Finn, J. P., Fonseca, C. G., Kadish, A. H., et al. (2014). Atlas-based quantification of cardiac remodeling due to myocardial infarction. PLoS One, 9(10), e110243.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Zhang, X., Ambale-Venkatesh, B., Bluemcke, D. A., Cowan, B. R., Finn, J. P., Fonseca, C. G., et al. (2015). Information maximizing component analysis of left ventricular remodeling due to myocardial infarction. Journal of Translational Medicine, 13(343). https://doi.org/10.1186/s12967-015-0709-4.

  51. Zhang, X., Medrano-Gracia, P., Ambale-Venkatesh, B., Bluemke, D. A., Cowan, B. R., Finn, J. P., et al. (2017). Orthogonal decomposition of left ventricular remodelling in myocardial infarction. Gigascience. https://doi.org/10.1093/gigascience/gix005.

  52. de Marvao, A., Dawes, T. J., Shi, W., Durighel, G., Rueckert, D., Cook, S. A., et al. (2015). Precursors of hypertensive heart phenotype develop in healthy adults: a high-resolution 3D MRI study. [research support, non-U.S. gov't]. JACC. Cardiovascular Imaging, 8(11), 1260–1269. https://doi.org/10.1016/j.jcmg.2015.08.007.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Corden, B., de Marvao, A., Dawes, T. J., Shi, W., Rueckert, D., Cook, S. A., et al. (2016). Relationship between body composition and left ventricular geometry using three dimensional cardiovascular magnetic resonance. Journal of Cardiovascular Magnetic Resonance, 18(1), 32. https://doi.org/10.1186/s12968-016-0251-4.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Biffi, C., de Marvao, A., Attard, M. I., Dawes, T. J. W., Whiffin, N., Bai, W., et al. (2017). Three-dimensional cardiovascular imaging-genetics: a mass univariate framework. Bioinformatics. https://doi.org/10.1093/bioinformatics/btx552.

  55. Schafer, S., de Marvao, A., Adami, E., Fiedler, L. R., Ng, B., Khin, E., et al. (2017). Titin-truncating variants affect heart function in disease cohorts and the general population. [comparative study]. Nature Genetics, 49(1), 46–53. https://doi.org/10.1038/ng.3719.

    Article  CAS  PubMed  Google Scholar 

  56. Marchesseau, S., Delingette, H., Sermesant, M., Cabrera-Lozoya, R., Tobon-Gomez, C., Moireau, P., et al. (2013). Personalization of a cardiac electromechanical model using reduced order unscented Kalman filtering from regional volumes. [research support, non-U.S. gov't]. Medical Image Analysis, 17(7), 816–829. https://doi.org/10.1016/j.media.2013.04.012.

    Article  CAS  PubMed  Google Scholar 

  57. Wang, V. Y., Lam, H. I., Ennis, D. B., Cowan, B. R., Young, A. A., & Nash, M. P. (2009). Modelling passive diastolic mechanics with quantitative MRI of cardiac structure and function. Medical Image Analysis, 13(5), 773–784.

    Article  PubMed  Google Scholar 

  58. Land, S., Gurev, V., Arens, S., Augustin, C. M., Baron, L., Blake, R., et al. (2015). Verification of cardiac mechanics software: benchmark problems and solutions for testing active and passive material behaviour. Proc. R. Soc. A, 471(2184).

  59. Niederer, S. A., Kerfoot, E., Benson, A. P., Bernabeu, M. O., Bernus, O., Bradley, C., et al. (2011). Verification of cardiac tissue electrophysiology simulators using an N-version benchmark. Philos Trans A Math Phys Eng Sci, 369(1954), 4331–4351. https://doi.org/10.1098/rsta.2011.0139.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Wang, V. Y., Nielsen, P. M., & Nash, M. P. (2015). Image-based predictive modeling of heart mechanics. Annual Review of Biomedical Engineering, 17, 351–383. https://doi.org/10.1146/annurev-bioeng-071114-040609.

    Article  CAS  PubMed  Google Scholar 

  61. Tang, D., Yang, C., Del Nido, P. J., Zuo, H., Rathod, R. H., Huang, X., et al. (2016). Mechanical stress is associated with right ventricular response to pulmonary valve replacement in patients with repaired tetralogy of Fallot. The Journal of Thoracic and Cardiovascular Surgery, 151(3), 687–694 e683. https://doi.org/10.1016/j.jtcvs.2015.09.106.

    Article  PubMed  Google Scholar 

  62. Gilbert, K., Pontre, B., Occleshaw, C. J., Cowan, B. R., Suinesiaputra, A., & Young, A. A. (2017). 4D modelling for rapid assessment of biventricular function in congenital heart disease. The International Journal of Cardiovascular Imaging. https://doi.org/10.1007/s10554-017-1236-6.

  63. Gonzales, M. J., Sturgeon, G., Krishnamurthy, A., Hake, J., Jonas, R., Stark, P., et al. (2013). A three-dimensional finite element model of human atrial anatomy: new methods for cubic Hermite meshes with extraordinary vertices. Medical Image Analysis, 17(5), 525–537.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Contijoch, F., Witschey, W. R., Rogers, K., Rears, H., Hansen, M., Yushkevich, P., et al. (2015). User-initialized active contour segmentation and golden-angle real-time cardiovascular magnetic resonance enable accurate assessment of LV function in patients with sinus rhythm and arrhythmias. Journal of Cardiovascular Magnetic Resonance, 17, 37. https://doi.org/10.1186/s12968-015-0146-9.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Lumens, J., Delhaas, T., Kirn, B., & Arts, T. (2009). Three-wall segment (TriSeg) model describing mechanics and hemodynamics of ventricular interaction. [research support, non-U.S. gov't]. Annals of Biomedical Engineering, 37(11), 2234–2255. https://doi.org/10.1007/s10439-009-9774-2.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Bluemke, D. A., Kronmal, R. A., Lima, J. A., Liu, K., Olson, J., Burke, G. L., et al. (2008). The relationship of left ventricular mass and geometry to incident cardiovascular events: the MESA (Multi-Ethnic Study of Atherosclerosis) study. Journal of the American College of Cardiology, 52(25), 2148–2155.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Ambale-Venkatesh, B., Yoneyama, K., Sharma, R. K., Ohyama, Y., Wu, C. O., Burke, G. L., et al. (2016). Left ventricular shape predicts different types of cardiovascular events in the general population. Heart. https://doi.org/10.1136/heartjnl-2016-310052.

  68. Villongco, C. T., Krummen, D. E., Omens, J. H., & McCulloch, A. D. (2016). Non-invasive, model-based measures of ventricular electrical dyssynchrony for predicting CRT outcomes. Europace, 18(suppl 4), iv104–iv112. https://doi.org/10.1093/europace/euw356.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was funded by NIH grants 1R01HL121754 to ADM, JHO, and AAY and 8P41GM103426 (the Biomedical Computation Resource) to ADM. KG received funding from the Greenlane Research and Education fund and AS received funding from the National Heart Foundation of New Zealand.

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Authors and Affiliations

  1. Department of Anatomy and Medical Imaging, University of Auckland, 85 Park Rd, Grafton, Auckland, 1142, New Zealand

    Kathleen Gilbert, Charlene Mauger, Beau Pontré, Avan Suinesiaputra & Alistair A. Young

  2. Department of Bioengineering, University of California, San Diego, USA

    Nickolas Forsch, James C. Perry & Andrew D. McCulloch

  3. Department of Paediatrics, University of California San Diego, San Diego, USA

    Sanjeet Hegde & James C. Perry

  4. Department of Medicine, University of California San Diego, San Diego, USA

    Jeffrey H. Omens & Andrew D. McCulloch

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Competing Interests

ADM and JHO are a co-founders of and have an equity interest in Insilicomed, Inc., and serve on the scientific advisory board. ADM is a co-founder of Vektor Medical, Inc., and serves on the scientific advisory board. Some of their research grants, including those acknowledged here, have been identified for conflict of interest management based on the overall scope of the project and its potential benefit to Insilicomed, Inc. The authors are required to disclose this relationship in publications acknowledging the grant support; however, the research subject and findings reported here did not involve the company in any way and have no relationship whatsoever to the business activities or scientific interests of the company. The terms of this arrangement have been reviewed and approved by the University of California San Diego in accordance with its conflict of interest policies. The other authors have no competing interests to declare.

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All human studies were approved by the appropriate ethics committees and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. All persons gave their informed consent prior to their inclusion in the study. Details that might disclose the identity of the subjects under study have been omitted.

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Gilbert, K., Forsch, N., Hegde, S. et al. Atlas-Based Computational Analysis of Heart Shape and Function in Congenital Heart Disease. J. of Cardiovasc. Trans. Res. 11, 123–132 (2018). https://doi.org/10.1007/s12265-017-9778-5

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