{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T21:05:56Z","timestamp":1768511156393,"version":"3.49.0"},"posted":{"date-parts":[[2024,6,3]]},"group-title":"Biophysics","reference-count":67,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2024,6,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, we develop a completely non-invasive methodology that identifies the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this transformative approach, we demonstrate the remarkable performance of a multi-fidelity ML model that combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. Our results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in-vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited.<\/jats:p>","DOI":"10.1101\/2024.05.31.596513","type":"posted-content","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T13:15:23Z","timestamp":1717420523000},"source":"Crossref","is-referenced-by-count":1,"title":["Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9008-9058","authenticated-orcid":false,"given":"Rana Raza","family":"Mehdi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikhil","family":"Kadivar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanmay","family":"Mukherjee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emilio A.","family":"Mendiola","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dipan J.","family":"Shah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Karniadakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9787-1117","authenticated-orcid":false,"given":"Reza","family":"Avazmohammadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2024060513301614000_2024.05.31.596513v1.1","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0000000000000659"},{"key":"2024060513301614000_2024.05.31.596513v1.2","doi-asserted-by":"crossref","first-page":"e021682","DOI":"10.1161\/JAHA.121.021682","article-title":"Trends in premature mortality from acute myocardial infarction in the united states, 1999 to 2019","volume":"11","year":"2022","journal-title":"J. Am. Hear. Assoc"},{"key":"2024060513301614000_2024.05.31.596513v1.3","doi-asserted-by":"crossref","first-page":"407","DOI":"10.4330\/wjc.v9.i5.407","article-title":"Heart failure after myocardial infarction in the era of primary percutaneous coronary intervention: Mechanisms, incidence and identification of patients at risk","volume":"9","year":"2017","journal-title":"World journal cardiology"},{"key":"2024060513301614000_2024.05.31.596513v1.4","unstructured":"Mechanic, O. J. , Gavin, M. & Grossman, S. A. Acute myocardial infarction. (2017)."},{"key":"2024060513301614000_2024.05.31.596513v1.5","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1002\/ehf2.13144","article-title":"Heart failure after myocardial infarction: incidence and predictors","volume":"8","year":"2021","journal-title":"ESC heart failure"},{"key":"2024060513301614000_2024.05.31.596513v1.6","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA"},{"key":"2024060513301614000_2024.05.31.596513v1.7","doi-asserted-by":"crossref","first-page":"e003901","DOI":"10.1161\/CIRCEP.116.003901","article-title":"Impact of new technologies and approaches for post\u2013myocardial infarction ventricular tachycardia ablation during long-term follow-up","volume":"9","year":"2016","journal-title":"Circ. Arrhythmia Electrophysiol"},{"key":"2024060513301614000_2024.05.31.596513v1.8","first-page":"e002460","article-title":"Mortality associated with heart failure after myocardial infarction: a contemporary community perspective","volume":"9","year":"2016","journal-title":"Circ. Hear. Fail"},{"key":"2024060513301614000_2024.05.31.596513v1.9","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehm102"},{"key":"2024060513301614000_2024.05.31.596513v1.10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2016.01.069"},{"key":"2024060513301614000_2024.05.31.596513v1.11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2014.06.1194"},{"key":"2024060513301614000_2024.05.31.596513v1.12","first-page":"1","article-title":"T1 mapping and t2 mapping at 3t for quantifying the area-at-risk in reperfused stemi patients","volume":"17","year":"2015","journal-title":"J. Cardiovasc. Magn. Reson"},{"key":"2024060513301614000_2024.05.31.596513v1.13","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.asoc.2017.12.001","article-title":"Detection of myocardial infarction in 12 lead ecg using support vector machine","volume":"64","year":"2018","journal-title":"Appl. Soft Comput"},{"key":"2024060513301614000_2024.05.31.596513v1.14","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"2024060513301614000_2024.05.31.596513v1.15","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.cogsys.2019.09.001","article-title":"Application of multi-feature fusion and random forests to the automated detection of myocardial infarction","volume":"59","year":"2020","journal-title":"Cogn. Syst. Res"},{"key":"2024060513301614000_2024.05.31.596513v1.16","doi-asserted-by":"crossref","unstructured":"Pan, J. & Tompkins, W. J. A real-time qrs detection algorithm. IEEE transactions on biomedical engineering 230\u2013236 (1985).","DOI":"10.1109\/TBME.1985.325532"},{"key":"2024060513301614000_2024.05.31.596513v1.17","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.patrec.2019.02.016","article-title":"Classification of myocardial infarction with multi-lead ecg signals and deep cnn","volume":"122","year":"2019","journal-title":"Pattern recognition letters"},{"key":"2024060513301614000_2024.05.31.596513v1.18","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1109\/TBME.2015.2405134","article-title":"Multiscale energy and eigenspace approach to detection and localization of myocardial infarction","volume":"62","year":"2015","journal-title":"IEEE transactions on biomedical engineering"},{"key":"2024060513301614000_2024.05.31.596513v1.19","doi-asserted-by":"crossref","first-page":"3348","DOI":"10.1109\/TBME.2012.2213597","article-title":"Ecg analysis using multiple instance learning for myocardial infarction detection","volume":"59","year":"2012","journal-title":"IEEE transactions on biomedical engineering"},{"key":"2024060513301614000_2024.05.31.596513v1.20","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s10916-010-9474-3","article-title":"Detection and localization of myocardial infarction using k-nearest neighbor classifier","volume":"36","year":"2012","journal-title":"J. medical systems"},{"key":"2024060513301614000_2024.05.31.596513v1.21","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0b013e31826e1058"},{"key":"2024060513301614000_2024.05.31.596513v1.22","doi-asserted-by":"crossref","first-page":"248","DOI":"10.4330\/wjc.v12.i6.248","article-title":"Cardiovascular magnetic resonance in myocardial infarction with non-obstructive coronary arteries patients: A review","volume":"12","year":"2020","journal-title":"World J. Cardiol"},{"key":"2024060513301614000_2024.05.31.596513v1.23","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.jmst.2021.03.062","article-title":"Biomaterials based cardiac patches for the treatment of myocardial infarction","volume":"94","year":"2021","journal-title":"J. Mater. Sci. & Technol"},{"key":"2024060513301614000_2024.05.31.596513v1.24","doi-asserted-by":"crossref","first-page":"3271","DOI":"10.1021\/acsbiomaterials.2c00454","article-title":"Role of biomaterials in cardiac repair and regeneration: therapeutic intervention for myocardial infarction","volume":"8","year":"2022","journal-title":"ACS Biomater. Sci. & Eng"},{"key":"2024060513301614000_2024.05.31.596513v1.25","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.109.865352"},{"key":"2024060513301614000_2024.05.31.596513v1.26","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCIMAGING.113.001541"},{"key":"2024060513301614000_2024.05.31.596513v1.27","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.11091882"},{"key":"2024060513301614000_2024.05.31.596513v1.28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2008.02.006"},{"key":"2024060513301614000_2024.05.31.596513v1.29","doi-asserted-by":"publisher","DOI":"10.1186\/1532-429X-16-1"},{"key":"2024060513301614000_2024.05.31.596513v1.30","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCIMAGING.108.789032"},{"key":"2024060513301614000_2024.05.31.596513v1.31","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1093\/ehjci\/jez041","article-title":"Myocardial strain imaging: review of general principles, validation, and sources of discrepancies","volume":"20","year":"2019","journal-title":"Eur. Hear. Journal-Cardiovascular Imaging"},{"key":"2024060513301614000_2024.05.31.596513v1.32","first-page":"1","article-title":"Reproducibility of cine displacement encoding with stimulated echoes (dense) cardiovascular magnetic resonance for measuring left ventricular strains, torsion, and synchrony in mice","volume":"15","year":"2013","journal-title":"J. cardiovascular magnetic resonance"},{"key":"2024060513301614000_2024.05.31.596513v1.33","first-page":"234","volume-title":"In Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III","volume":"18","year":"2015"},{"key":"2024060513301614000_2024.05.31.596513v1.34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2019.109020"},{"key":"2024060513301614000_2024.05.31.596513v1.35","doi-asserted-by":"crossref","first-page":"3251","DOI":"10.1098\/rspa.2007.1900","article-title":"Multi-fidelity optimization via surrogate modelling","volume":"463","year":"2007","journal-title":"Proc. royal society a: mathematical, physical engineering sciences"},{"key":"2024060513301614000_2024.05.31.596513v1.36","article-title":"Deep multi-fidelity gaussian processes","year":"2016","journal-title":"arXiv preprint"},{"key":"2024060513301614000_2024.05.31.596513v1.37","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.jcp.2017.01.060","article-title":"Inferring solutions of differential equations using noisy multi-fidelity data","volume":"335","year":"2017","journal-title":"J. Comput. Phys"},{"key":"2024060513301614000_2024.05.31.596513v1.38","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/87.1.1"},{"key":"2024060513301614000_2024.05.31.596513v1.39","doi-asserted-by":"crossref","first-page":"20160751","DOI":"10.1098\/rspa.2016.0751","article-title":"Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling","volume":"473","year":"2017","journal-title":"Proc. Royal Soc. A: Math. Phys. Eng. Sci"},{"key":"2024060513301614000_2024.05.31.596513v1.40","doi-asserted-by":"crossref","first-page":"223","DOI":"10.5957\/JOSR.11170069","article-title":"Improving swath seakeeping performance using multi-fidelity gaussian process and bayesian optimization","volume":"62","year":"2018","journal-title":"J. Ship Res"},{"key":"2024060513301614000_2024.05.31.596513v1.41","doi-asserted-by":"crossref","first-page":"114212","DOI":"10.1016\/j.cma.2021.114212","article-title":"A fast multi-fidelity method with uncertainty quantification for complex data correlations: Application to vortex-induced vibrations of marine risers","volume":"386","year":"2021","journal-title":"Comput. Methods Appl. Mech. Eng"},{"key":"2024060513301614000_2024.05.31.596513v1.42","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1007\/s10439-022-03102-z","article-title":"Contractile adaptation of the left ventricle post-myocardial infarction: predictions by rodent-specific computational modeling","volume":"51","year":"2023","journal-title":"Annals biomedical engineering"},{"key":"2024060513301614000_2024.05.31.596513v1.43","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.actbio.2023.10.027","article-title":"An image-driven micromechanical approach to characterize multiscale remodeling in infarcted myocardium","volume":"173","year":"2024","journal-title":"Acta Biomater"},{"key":"2024060513301614000_2024.05.31.596513v1.44","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1007\/s10439-018-02130-y","article-title":"A computational cardiac model for the adaptation to pulmonary arterial hypertension in the rat","volume":"47","year":"2019","journal-title":"Annals biomedical engineering"},{"key":"2024060513301614000_2024.05.31.596513v1.45","doi-asserted-by":"crossref","unstructured":"Guccione, J. M. & McCulloch, A. D. Finite element modeling of ventricular mechanics. In Theory of Heart: biomechanics, biophysics, and nonlinear dynamics of cardiac function, 121\u2013144 (Springer, 1991).","DOI":"10.1007\/978-1-4612-3118-9_6"},{"key":"2024060513301614000_2024.05.31.596513v1.46","first-page":"791","article-title":"A deep learning based dual encoder\u2013decoder framework for anatomical structure segmentation in chest x-ray images","volume":"13","year":"2023","journal-title":"Sci. Reports"},{"key":"2024060513301614000_2024.05.31.596513v1.47","article-title":"Review of multi-fidelity models","year":"2016","journal-title":"arXiv preprint"},{"key":"2024060513301614000_2024.05.31.596513v1.48","article-title":"Attention u-net: Learning where to look for the pancreas","year":"2018","journal-title":"arXiv preprint"},{"key":"2024060513301614000_2024.05.31.596513v1.49","doi-asserted-by":"crossref","unstructured":"Wang, F. et al. Residual attention network for image classification. In Proceedings of the IEEE conference on computer vision and pattern recognition, 3156\u20133164 (2017).","DOI":"10.1109\/CVPR.2017.683"},{"key":"2024060513301614000_2024.05.31.596513v1.50","article-title":"Neural machine translation by jointly learning to align and translate","year":"2014","journal-title":"arXiv preprint"},{"key":"2024060513301614000_2024.05.31.596513v1.51","article-title":"Effective approaches to attention-based neural machine translation","year":"2015","journal-title":"arXiv preprint"},{"key":"2024060513301614000_2024.05.31.596513v1.52","doi-asserted-by":"crossref","unstructured":"Zhu, Y. & Newsam, S. Densenet for dense flow. In 2017 IEEE international conference on image processing (ICIP), 790\u2013794 (IEEE, 2017).","DOI":"10.1109\/ICIP.2017.8296389"},{"key":"2024060513301614000_2024.05.31.596513v1.53","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/s20092661","article-title":"Lensless computational imaging technology using deep convolutional network","volume":"20","year":"2020","journal-title":"Sensors"},{"key":"2024060513301614000_2024.05.31.596513v1.54","first-page":"014006","article-title":"Recurrent residual u-net for medical image segmentation","volume":"6","year":"2019","journal-title":"J. Med. Imaging"},{"key":"2024060513301614000_2024.05.31.596513v1.55","doi-asserted-by":"crossref","unstructured":"Milletari, F. , Navab, N. & Ahmadi, S.-A. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV), 565\u2013571 (Ieee, 2016).","DOI":"10.1109\/3DV.2016.79"},{"key":"2024060513301614000_2024.05.31.596513v1.56","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ins.2020.12.013","article-title":"Target attack on biomedical image segmentation model based on multi-scale gradients","volume":"554","year":"2021","journal-title":"Inf. sciences"},{"key":"2024060513301614000_2024.05.31.596513v1.57","first-page":"5433","article-title":"A machine learning model to estimate myocardial stiffness from edpvr","volume":"12","year":"2022","journal-title":"Sci. Reports"},{"key":"2024060513301614000_2024.05.31.596513v1.58","doi-asserted-by":"crossref","first-page":"e009535","DOI":"10.1161\/CIRCIMAGING.119.009535","article-title":"Myocardial extracellular volume fraction adds prognostic information beyond myocardial replacement fibrosis","volume":"12","year":"2019","journal-title":"Circ. Cardiovasc. Imaging"},{"key":"2024060513301614000_2024.05.31.596513v1.59","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2008.10.040"},{"key":"2024060513301614000_2024.05.31.596513v1.60","doi-asserted-by":"crossref","unstructured":"Keshavarzian, M. et al. An image registration framework to estimate 3d myocardial strains from cine cardiac mri in mice. In International Conference on Functional Imaging and Modeling of the Heart, 273\u2013284 (Springer, 2021).","DOI":"10.1007\/978-3-030-78710-3_27"},{"key":"2024060513301614000_2024.05.31.596513v1.61","unstructured":"Mukherjee, T. et al. In-silico heart model phantom to validate cardiac strain imaging."},{"key":"2024060513301614000_2024.05.31.596513v1.62","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2903562"},{"key":"2024060513301614000_2024.05.31.596513v1.63","first-page":"3","volume-title":"In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings","volume":"4","year":"2018"},{"key":"2024060513301614000_2024.05.31.596513v1.64","first-page":"326","volume-title":"In Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I","volume":"24","year":"2021"},{"key":"2024060513301614000_2024.05.31.596513v1.65","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","article-title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","volume":"378","year":"2019","journal-title":"J. Comput. physics"},{"key":"2024060513301614000_2024.05.31.596513v1.66","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1922210117"},{"key":"2024060513301614000_2024.05.31.596513v1.67","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/s41746-019-0193-y","article-title":"Integrating machine learning and multiscale modeling\u2014perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences","volume":"2","year":"2019","journal-title":"NPJ digital medicine"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2024.05.31.596513","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T06:30:21Z","timestamp":1768458621000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.1101\/2024.05.31.596513"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":67,"URL":"https:\/\/doi.org\/10.1101\/2024.05.31.596513","relation":{},"subject":[],"published":{"date-parts":[[2024,6,3]]},"subtype":"preprint"}}