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1. Multiphysics and multiscale modelling, data-model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics

Author

Radomir, Chabiniok, Vicky Y, Wang, Myrianthi, Hadjicharalambous, Liya, Asner, Jack, Lee, Maxime, Sermesant, Ellen, Kuhl, Alistair A, Young, Philippe, Moireau, Martyn P, Nash, Dominique, Chapelle, and David A, Nordsletten

Subjects
translational cardiac modelling, patient-specific modelling, heart mechanics, Review Article, data–model fusion, cardiac mechanics, Part II: Methodology Needed for Establishing a Clinically Relevant Human Physiome
Abstract

With heart and cardiovascular diseases continually challenging healthcare systems worldwide, translating basic research on cardiac (patho)physiology into clinical care is essential. Exacerbating this already extensive challenge is the complexity of the heart, relying on its hierarchical structure and function to maintain cardiovascular flow. Computational modelling has been proposed and actively pursued as a tool for accelerating research and translation. Allowing exploration of the relationships between physics, multiscale mechanisms and function, computational modelling provides a platform for improving our understanding of the heart. Further integration of experimental and clinical data through data assimilation and parameter estimation techniques is bringing computational models closer to use in routine clinical practice. This article reviews developments in computational cardiac modelling and how their integration with medical imaging data is providing new pathways for translational cardiac modelling.

Published
2016

2. euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling

Author

Walther H. W. Schulze, Jatin Relan, Dominique Chapelle, Matt McCormick, Helko Lehmann, Philipp Beerbaum, Maxime Sermesant, David Nordsletten, Jos A. E. Spaan, Alejandro F. Frangi, Israel Valverde, Peter Hunter, Reza Rezavi, Rod Hose, Nicholas Ayache, Juergen Weese, Cristina Staicu, Maria Siebes, Hervé Delingette, Oscar Camara, Nic Smith, Adelaide de Vecchi, Martin W. Krueger, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Computing Laboratory (OUCL), University of Oxford, Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Universitat Pompeu Fabra [Barcelona] (UPF), Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III [Madrid] (ISC)-ministerio de ciencia e innovacion, Institució Catalana de Recerca i Estudis Avançats (ICREA), Institute of Simulation and Graphics [Magdeburg], Otto-von-Guericke-Universität Magdeburg = Otto-von-Guericke University [Magdeburg] (OVGU), Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute of Biomedical Engineering [Karlsruhe], Karlsruhe Institute of Technology (KIT), Department of Cardiovascular Science [Sheffield], University of Sheffield [Sheffield], Academic Medical Center - Academisch Medisch Centrum [Amsterdam] (AMC), University of Amsterdam [Amsterdam] (UvA), Auckland Bioengineering Institute, University of Auckland [Auckland], Philips Research Laboratories [Eindhoven], Philips Research [Nederlands], Philips Research, Modeling, analysis and control in computational structural dynamics (MACS), Inria Paris-Rocquencourt, Division of Imaging Sciences, King‘s College London, NIHR Biomedical Research Centre [London], Guy's and St Thomas' NHS Foundation Trust-King‘s College London, University of Oxford [Oxford], Otto-von-Guericke University [Magdeburg] (OVGU), ACS - Amsterdam Cardiovascular Sciences, Biomedical Engineering and Physics, and Other Research

Subjects
Cardiac function curve, medicine.medical_specialty, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, 0206 medical engineering, Population, Biomedical Engineering, Biophysics, Pump function, Bioengineering, 02 engineering and technology, Disease, 030204 cardiovascular system & hematology, Bioinformatics, Biochemistry, Biomaterials, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Medicine, Clinical imaging, education, Intensive care medicine, education.field_of_study, business.industry, Virtual Physiological Human, Articles, Patient specific, 020601 biomedical engineering, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 3. Good health, Lifetime risk, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Biotechnology
Abstract

International audience; The loss of cardiac pump function accounts for a significant increase in both mortality and morbidity in Western society, where there is currently a one in four lifetime risk, and costs associated with acute and long-term hospital treatments are accelerating. The significance of cardiac disease has motivated the application of state-of-the-art clinical imaging techniques and functional signal analysis to aid diagnosis and clinical planning. Measurements of cardiac function currently provide high-resolution datasets for characterizing cardiac patients. However, the clinical practice of using population-based metrics derived from separate image or signal-based datasets often indicates contradictory treatments plans owing to inter-individual variability in pathophysiology. To address this issue, the goal of our work, demonstrated in this study through four specific clinical applications, is to integrate multiple types of functional data into a consistent framework using multi-scale computational modelling.

Published
2011
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