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Morris PD, Anderton RA, Marshall-Goebel K, Britton JK, Lee SMC, Smith NP, van de Vosse FN, Ong KM, Newman TA, Taylor DJ, Chico T, Gunn JP, Narracott AJ, Hose DR, Halliday I. Computational modelling of cardiovascular pathophysiology to risk stratify commercial spaceflight. Nat Rev Cardiol 2024:10.1038/s41569-024-01047-5. [PMID: 39030270 DOI: 10.1038/s41569-024-01047-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/30/2024] [Indexed: 07/21/2024]
Abstract
For more than 60 years, humans have travelled into space. Until now, the majority of astronauts have been professional, government agency astronauts selected, in part, for their superlative physical fitness and the absence of disease. Commercial spaceflight is now becoming accessible to members of the public, many of whom would previously have been excluded owing to unsatisfactory fitness or the presence of cardiorespiratory diseases. While data exist on the effects of gravitational and acceleration (G) forces on human physiology, data on the effects of the aerospace environment in unselected members of the public, and particularly in those with clinically significant pathology, are limited. Although short in duration, these high acceleration forces can potentially either impair the experience or, more seriously, pose a risk to health in some individuals. Rather than expose individuals with existing pathology to G forces to collect data, computational modelling might be useful to predict the nature and severity of cardiovascular diseases that are of sufficient risk to restrict access, require modification, or suggest further investigation or training before flight. In this Review, we explore state-of-the-art, zero-dimensional, compartmentalized models of human cardiovascular pathophysiology that can be used to simulate the effects of acceleration forces, homeostatic regulation and ventilation-perfusion matching, using data generated by long-arm centrifuge facilities of the US National Aeronautics and Space Administration and the European Space Agency to risk stratify individuals and help to improve safety in commercial suborbital spaceflight.
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Affiliation(s)
- Paul D Morris
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK.
- Department of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
| | - Ryan A Anderton
- Medical Department, Spaceflight, UK Civil Aviation Authority, Gatwick, UK
| | - Karina Marshall-Goebel
- The National Aeronautics and Space Administration (NASA) Johnson Space Center, Houston, TX, USA
| | - Joseph K Britton
- Aerospace Medicine Specialist Wing, Royal Air Force (RAF) Centre of Aerospace Medicine, Henlow, UK
| | - Stuart M C Lee
- KBR, Human Health Countermeasures Element, NASA Johnson Space Center, Houston, TX, USA
| | - Nicolas P Smith
- Victoria University of Wellington, Wellington, New Zealand
- Auckland Bioengineering Institute, Auckland, New Zealand
| | - Frans N van de Vosse
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Karen M Ong
- Virgin Galactic Medical, Truth or Consequences, NM, USA
| | - Tom A Newman
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
- Department of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Daniel J Taylor
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
| | - Tim Chico
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
- Department of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Julian P Gunn
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
- Department of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Andrew J Narracott
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
- Insigneo Institute, University of Sheffield, Sheffield, UK
| | - D Rod Hose
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
- Insigneo Institute, University of Sheffield, Sheffield, UK
| | - Ian Halliday
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
- Insigneo Institute, University of Sheffield, Sheffield, UK
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2
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Heidari A, Elkhodary KI, Pop C, Badran M, Vali H, Abdel-Raouf YMA, Torbati S, Asgharian M, Steele RJ, Mahmoudzadeh Kani I, Sheibani S, Pouraliakbar H, Sadeghian H, Cecere R, Friedrich MGW, Tafti HA. Patient-specific finite element analysis of heart failure and the impact of surgical intervention in pulmonary hypertension secondary to mitral valve disease. Med Biol Eng Comput 2022; 60:1723-1744. [PMID: 35442004 DOI: 10.1007/s11517-022-02556-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 03/12/2022] [Indexed: 12/31/2022]
Abstract
Pulmonary hypertension (PH), a chronic and complex medical condition affecting 1% of the global population, requires clinical evaluation of right ventricular maladaptation patterns under various conditions. A particular challenge for clinicians is a proper quantitative assessment of the right ventricle (RV) owing to its intimate coupling to the left ventricle (LV). We, thus, proposed a patient-specific computational approach to simulate PH caused by left heart disease and its main adverse functional and structural effects on the whole heart. Information obtained from both prospective and retrospective studies of two patients with severe PH, a 72-year-old female and a 61-year-old male, is used to present patient-specific versions of the Living Heart Human Model (LHHM) for the pre-operative and post-operative cardiac surgery. Our findings suggest that before mitral and tricuspid valve repair, the patients were at risk of right ventricular dilatation which may progress to right ventricular failure secondary to their mitral valve disease and left ventricular dysfunction. Our analysis provides detailed evidence that mitral valve replacement and subsequent chamber pressure unloading are associated with a significant decrease in failure risk post-operatively in the context of pulmonary hypertension. In particular, right-sided strain markers, such as tricuspid annular plane systolic excursion (TAPSE) and circumferential and longitudinal strains, indicate a transition from a range representative of disease to within typical values after surgery. Furthermore, the wall stresses across the RV and the interventricular septum showed a notable decrease during the systolic phase after surgery, lessening the drive for further RV maladaptation and significantly reducing the risk of RV failure.
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Affiliation(s)
- Alireza Heidari
- Department of Mechanical Engineering, McGill University, 817 Sherbrooke Street West, Montreal, QC, H3A 0C3, Canada. .,Department of Anatomy & Cell Biology, McGill University, Montreal, QC, Canada.
| | - Khalil I Elkhodary
- Department of Mechanical Engineering, American University in Cairo, New Cairo, 11835, Egypt
| | - Cristina Pop
- Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Mohamed Badran
- Department of Mechanical Engineering, Future University in Egypt, New Cairo, Egypt
| | - Hojatollah Vali
- Department of Anatomy & Cell Biology, McGill University, Montreal, QC, Canada
| | - Yousof M A Abdel-Raouf
- Department of Mechanical Engineering, American University in Cairo, New Cairo, 11835, Egypt
| | - Saeed Torbati
- School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Masoud Asgharian
- Department of Mathematics and Statistics, McGill University, Montreal, QC, Canada
| | - Russell J Steele
- Department of Mathematics and Statistics, McGill University, Montreal, QC, Canada
| | | | - Sara Sheibani
- Department of Anatomy & Cell Biology, McGill University, Montreal, QC, Canada
| | - Hamidreza Pouraliakbar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Hakimeh Sadeghian
- Faculty of Medicine, Tehran University of Medical Science, Tehran, Iran.,Department of Surgery, Tehran Heart Center, Tehran, Iran
| | - Renzo Cecere
- Department of Mechanical Engineering, McGill University, 817 Sherbrooke Street West, Montreal, QC, H3A 0C3, Canada.,Department of Surgery, Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada
| | - Matthias G W Friedrich
- Departments of Medicine and Diagnostic Radiology, McGill University, Montreal, QC, Canada
| | - Hossein Ahmadi Tafti
- Faculty of Medicine, Tehran University of Medical Science, Tehran, Iran.,Department of Surgery, Tehran Heart Center, Tehran, Iran
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3
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Patte C, Brillet PY, Fetita C, Bernaudin JF, Gille T, Nunes H, Chapelle D, Genet M. Estimation of Regional Pulmonary Compliance in Idiopathic Pulmonary Fibrosis Based On Personalized Lung Poromechanical Modeling. J Biomech Eng 2022; 144:1139545. [PMID: 35292805 DOI: 10.1115/1.4054106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Indexed: 11/08/2022]
Abstract
Pulmonary function is tightly linked to the lung mechanical behavior, especially large deformation during breathing. Interstitial lung diseases, such as Idiopathic Pulmonary Fibrosis (IPF), have an impact on the pulmonary mechanics and consequently alter lung function. However, IPF remains poorly understood, poorly diagnosed and poorly treated. Currently, the mechanical impact of such diseases is assessed by pressure-volume curves, giving only global information. We developed a poromechanical model of the lung that can be personalized to a patient based on routine clinical data. The personalization pipeline uses clinical data, mainly CT-images at two time steps and involves the formulation of an inverse problem to estimate regional compliances. The estimation problem can be formulated both in terms of "effective", i.e., without considering the mixture porosity, or "rescaled", i.e., where the first-order effect of the porosity has been taken into account, compliances. Regional compliances are estimated for one control subject and three IPF patients, allowing to quantify the IPF-induced tissue stiffening. This personalized model could be used in the clinic as an objective and quantitative tool for IPF diagnosis.
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Affiliation(s)
- Cécile Patte
- Inria, Palaiseau, France, Laboratoire de Mécanique des Solides, École Polytechnique/CNRS/IPP, Palaiseau, France
| | - Pierre-Yves Brillet
- Hypoxie et Poumon, Universit é Sorbonne Paris Nord/INSERM, Bobigny, France; Hôpital Avicenne, APHP, Bobigny, France
| | - Catalin Fetita
- SAMOVAR, Telecom SudParis/Institut Mines-Télécom/IPP, Évry, France
| | | | - Thomas Gille
- Hypoxie et Poumon, Universit é Sorbonne Paris Nord/INSERM, Bobigny, France; Hôpital Avicenne, APHP, Bobigny, France
| | - Hilario Nunes
- Hypoxie et Poumon, Universit é Sorbonne Paris Nord/INSERM, Bobigny, France; Hôpital Avicenne, APHP, Bobigny, France
| | - Dominique Chapelle
- Inria, Palaiseau, France, Laboratoire de Mécanique des Solides, École Polytechnique/CNRS/IPP, Palaiseau, France
| | - Martin Genet
- Laboratoire de Mecanique des Solides, École Polytechnique/CNRS/IPP, Palaiseau, France; Inria, Palaiseau, France
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4
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Brosch T, Peters J, Groth A, Weber FM, Weese J. Model-based segmentation using neural network-based boundary detectors: Application to prostate and heart segmentation in MR images. MACHINE LEARNING WITH APPLICATIONS 2021. [DOI: 10.1016/j.mlwa.2021.100078] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Augustin CM, Gsell MA, Karabelas E, Willemen E, Prinzen FW, Lumens J, Vigmond EJ, Plank G. A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021; 386:114092. [PMID: 34630765 PMCID: PMC7611781 DOI: 10.1016/j.cma.2021.114092] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Computer models of cardiac electro-mechanics (EM) show promise as an effective means for the quantitative analysis of clinical data and, potentially, for predicting therapeutic responses. To realize such advanced applications methodological key challenges must be addressed. Enhanced computational efficiency and robustness is crucial to facilitate, within tractable time frames, model personalization, the simulation of prolonged observation periods under a broad range of conditions, and physiological completeness encompassing therapy-relevant mechanisms is needed to endow models with predictive capabilities beyond the mere replication of observations. Here, we introduce a universal feature-complete cardiac EM modeling framework that builds on a flexible method for coupling a 3D model of bi-ventricular EM to the physiologically comprehensive 0D CircAdapt model representing atrial mechanics and closed-loop circulation. A detailed mathematical description is given and efficiency, robustness, and accuracy of numerical scheme and solver implementation are evaluated. After parameterization and stabilization of the coupled 3D-0D model to a limit cycle under baseline conditions, the model's ability to replicate physiological behaviors is demonstrated, by simulating the transient response to alterations in loading conditions and contractility, as induced by experimental protocols used for assessing systolic and diastolic ventricular properties. Mechanistic completeness and computational efficiency of this novel model render advanced applications geared towards predicting acute outcomes of EM therapies feasible.
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Affiliation(s)
- Christoph M. Augustin
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Matthias A.F. Gsell
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Elias Karabelas
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Erik Willemen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Frits W. Prinzen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Edward J. Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux, France
| | - Gernot Plank
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
- Correspondence to: Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Neue Stiftingtalstrasse 6/IV, Graz 8010, Austria. (G. Plank)
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6
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Augustin CM, Gsell MAF, Karabelas E, Willemen E, Prinzen FW, Lumens J, Vigmond EJ, Plank G. A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021; 386:114092. [PMID: 34630765 DOI: 10.1016/jxma.2021.114092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Computer models of cardiac electro-mechanics (EM) show promise as an effective means for the quantitative analysis of clinical data and, potentially, for predicting therapeutic responses. To realize such advanced applications methodological key challenges must be addressed. Enhanced computational efficiency and robustness is crucial to facilitate, within tractable time frames, model personalization, the simulation of prolonged observation periods under a broad range of conditions, and physiological completeness encompassing therapy-relevant mechanisms is needed to endow models with predictive capabilities beyond the mere replication of observations. Here, we introduce a universal feature-complete cardiac EM modeling framework that builds on a flexible method for coupling a 3D model of bi-ventricular EM to the physiologically comprehensive 0D CircAdapt model representing atrial mechanics and closed-loop circulation. A detailed mathematical description is given and efficiency, robustness, and accuracy of numerical scheme and solver implementation are evaluated. After parameterization and stabilization of the coupled 3D-0D model to a limit cycle under baseline conditions, the model's ability to replicate physiological behaviors is demonstrated, by simulating the transient response to alterations in loading conditions and contractility, as induced by experimental protocols used for assessing systolic and diastolic ventricular properties. Mechanistic completeness and computational efficiency of this novel model render advanced applications geared towards predicting acute outcomes of EM therapies feasible.
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Affiliation(s)
- Christoph M Augustin
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Matthias A F Gsell
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Elias Karabelas
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Erik Willemen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Frits W Prinzen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux, France
| | - Gernot Plank
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
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7
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Upendra RR, Wentz BJ, Simon R, Shontz SM, Linte CA. CNN-Based Cardiac Motion Extraction to Generate Deformable Geometric Left Ventricle Myocardial Models from Cine MRI. FUNCTIONAL IMAGING AND MODELING OF THE HEART : ... INTERNATIONAL WORKSHOP, FIMH ..., PROCEEDINGS. FIMH 2021; 12738:253-263. [PMID: 37216301 PMCID: PMC10198131 DOI: 10.1007/978-3-030-78710-3_25] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans. Cine cardiac magnetic resonance (MR) imaging provides high resolution images to reconstruct patient-specific geometric models of the LV myocardium. With the advent of deep learning, accurate segmentation of cardiac chambers from cine cardiac MR images and unsupervised learning for image registration for cardiac motion estimation on a large number of image datasets is attainable. Here, we propose a deep leaning-based framework for the development of patient-specific geometric models of LV myocardium from cine cardiac MR images, using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. We use the deformation field estimated from the VoxelMorph-based convolutional neural network (CNN) to propagate the isosurface mesh and volume mesh of the end-diastole (ED) frame to the subsequent frames of the cardiac cycle. We assess the CNN-based propagated models against segmented models at each cardiac phase, as well as models propagated using another traditional nonrigid image registration technique. Additionally, we generate dynamic LV myocardial volume meshes at all phases of the cardiac cycle using the log barrier-based mesh warping (LBWARP) method and compare them with the CNN-propagated volume meshes.
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Affiliation(s)
- Roshan Reddy Upendra
- Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA
| | - Brian Jamison Wentz
- Bioengineering Program, University of Kansas, Lawrence, KS, USA
- Information and Telecommunication Center, University of Kansas, Lawrence, KS, USA
| | - Richard Simon
- Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, USA
| | - Suzanne M Shontz
- Bioengineering Program, University of Kansas, Lawrence, KS, USA
- Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA
- Information and Telecommunication Center, University of Kansas, Lawrence, KS, USA
| | - Cristian A Linte
- Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA
- Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, USA
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Fedele M, Quarteroni A. Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3435. [PMID: 33415829 PMCID: PMC8244076 DOI: 10.1002/cnm.3435] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 01/01/2021] [Accepted: 01/02/2021] [Indexed: 06/05/2023]
Abstract
In order to simulate the cardiac function for a patient-specific geometry, the generation of the computational mesh is crucially important. In practice, the input is typically a set of unprocessed polygonal surfaces coming either from a template geometry or from medical images. These surfaces need ad-hoc processing to be suitable for a volumetric mesh generation. In this work we propose a set of new algorithms and tools aiming to facilitate the mesh generation process. In particular, we focus on different aspects of a cardiac mesh generation pipeline: (1) specific polygonal surface processing for cardiac geometries, like connection of different heart chambers or segmentation outputs; (2) generation of accurate boundary tags; (3) definition of mesh-size functions dependent on relevant geometric quantities; (4) processing and connecting together several volumetric meshes. The new algorithms-implemented in the open-source software vmtk-can be combined with each other allowing the creation of personalized pipelines, that can be optimized for each cardiac geometry or for each aspect of the cardiac function to be modeled. Thanks to these features, the proposed tools can significantly speed-up the mesh generation process for a large range of cardiac applications, from single-chamber single-physics simulations to multi-chambers multi-physics simulations. We detail all the proposed algorithms motivating them in the cardiac context and we highlight their flexibility by showing different examples of cardiac mesh generation pipelines.
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Affiliation(s)
- Marco Fedele
- MOX, Department of MathematicsPolitecnico di MilanoMilanItaly
| | - Alfio Quarteroni
- MOX, Department of MathematicsPolitecnico di MilanoMilanItaly
- Institute of MathematicsÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
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9
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Precision medicine in human heart modeling : Perspectives, challenges, and opportunities. Biomech Model Mechanobiol 2021; 20:803-831. [PMID: 33580313 PMCID: PMC8154814 DOI: 10.1007/s10237-021-01421-z] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/07/2021] [Indexed: 01/05/2023]
Abstract
Precision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.
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10
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Gallo C, Olbers J, Ridolfi L, Scarsoglio S, Witt N. Testing a Patient-Specific In-Silico Model to Noninvasively Estimate Central Blood Pressure. Cardiovasc Eng Technol 2021; 12:144-157. [PMID: 33438147 DOI: 10.1007/s13239-020-00512-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 12/22/2020] [Indexed: 11/30/2022]
Abstract
PURPOSE To show some preliminary results about the possibility to exploit a cardiovascular mathematical model-made patient-specific by noninvasive data routinely measured during ordinary clinical examinations-in order to obtain sufficiently accurate central blood pressure (BP) estimates. METHODS A closed-loop multiscale (0D and 1D) model of the cardiovascular system is made patient-specific by using as model inputs the individual mean heart rate and left-ventricular contraction time, weight, height, age, sex and mean/pulse brachial BPs. The resulting framework is used to determine central systolic, diastolic, mean and pulse pressures, which are compared with the beat-averaged invasive pressures of 12 patients aged 72 ± 6.61 years. RESULTS Errors in central systolic, diastolic, mean and pulse pressures by the model are 4.26 ± 2.81, 5.86 ± 4.38, 4.98 ± 3.95 and 3.51±2.38 mmHg, respectively. CONCLUSION The proposed modeling approach shows a good patient-specific response and appears to be potentially useful in clinical practice. However, this approach needs to be evaluated in a larger cohort of patients and could possibly be improved through more accurate oscillometric BP measurement methods.
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Affiliation(s)
- Caterina Gallo
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy.
| | - Joakim Olbers
- Division of Cardiology, Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
| | - Luca Ridolfi
- Department of Environmental, Land and Infrastructure Engineering, Politecnico di Torino, Torino, Italy
| | - Stefania Scarsoglio
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy
| | - Nils Witt
- Division of Cardiology, Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
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11
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Nguyen TD, Kadri OE, Voronov RS. An Introductory Overview of Image-Based Computational Modeling in Personalized Cardiovascular Medicine. Front Bioeng Biotechnol 2020; 8:529365. [PMID: 33102452 PMCID: PMC7546862 DOI: 10.3389/fbioe.2020.529365] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 08/31/2020] [Indexed: 02/05/2023] Open
Abstract
Cardiovascular diseases account for the number one cause of deaths in the world. Part of the reason for such grim statistics is our limited understanding of the underlying mechanisms causing these devastating pathologies, which is made difficult by the invasiveness of the procedures associated with their diagnosis (e.g., inserting catheters into the coronal artery to measure blood flow to the heart). Likewise, it is also difficult to design and test assistive devices without implanting them in vivo. However, with the recent advancements made in biomedical scanning technologies and computer simulations, image-based modeling (IBM) has arisen as the next logical step in the evolution of non-invasive patient-specific cardiovascular medicine. Yet, due to its novelty, it is still relatively unknown outside of the niche field. Therefore, the goal of this manuscript is to review the current state-of-the-art and the limitations of the methods used in this area of research, as well as their applications to personalized cardiovascular investigations and treatments. Specifically, the modeling of three different physics – electrophysiology, biomechanics and hemodynamics – used in the cardiovascular IBM is discussed in the context of the physiology that each one of them describes and the mechanisms of the underlying cardiac diseases that they can provide insight into. Only the “bare-bones” of the modeling approaches are discussed in order to make this introductory material more accessible to an outside observer. Additionally, the imaging methods, the aspects of the unique cardiac anatomy derived from them, and their relation to the modeling algorithms are reviewed. Finally, conclusions are drawn about the future evolution of these methods and their potential toward revolutionizing the non-invasive diagnosis, virtual design of treatments/assistive devices, and increasing our understanding of these lethal cardiovascular diseases.
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Affiliation(s)
- Thanh Danh Nguyen
- Otto H. York Department of Chemical and Materials Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Olufemi E Kadri
- Otto H. York Department of Chemical and Materials Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ, United States.,UC-P&G Simulation Center, University of Cincinnati, Cincinnati, OH, United States
| | - Roman S Voronov
- Otto H. York Department of Chemical and Materials Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ, United States.,Department of Biomedical Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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13
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Cardiovascular models for personalised medicine: Where now and where next? Med Eng Phys 2020; 72:38-48. [PMID: 31554575 DOI: 10.1016/j.medengphy.2019.08.007] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 08/23/2019] [Indexed: 12/14/2022]
Abstract
The aim of this position paper is to provide a brief overview of the current status of cardiovascular modelling and of the processes required and some of the challenges to be addressed to see wider exploitation in both personal health management and clinical practice. In most branches of engineering the concept of the digital twin, informed by extensive and continuous monitoring and coupled with robust data assimilation and simulation techniques, is gaining traction: the Gartner Group listed it as one of the top ten digital trends in 2018. The cardiovascular modelling community is starting to develop a much more systematic approach to the combination of physics, mathematics, control theory, artificial intelligence, machine learning, computer science and advanced engineering methodology, as well as working more closely with the clinical community to better understand and exploit physiological measurements, and indeed to develop jointly better measurement protocols informed by model-based understanding. Developments in physiological modelling, model personalisation, model outcome uncertainty, and the role of models in clinical decision support are addressed and 'where-next' steps and challenges discussed.
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14
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Genet M. A relaxed growth modeling framework for controlling growth-induced residual stresses. Clin Biomech (Bristol, Avon) 2019; 70:270-277. [PMID: 31831206 DOI: 10.1016/j.clinbiomech.2019.08.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 08/26/2019] [Accepted: 08/27/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND Constitutive models of the mechanical response of soft tissues have been established and are widely accepted, but models of soft tissues remodeling are more controversial. Specifically for growth, one important question arises pertaining to residual stresses: existing growth models inevitably introduce residual stresses, but it is not entirely clear if this is physiological or merely an artifact of the modeling framework. As a consequence, in simulating growth, some authors have chosen to keep growth-induced residual stresses, and others have chosen to remove them. METHODS In this paper, we introduce a novel "relaxed growth" framework allowing for a fine control of the amount of residual stresses generated during tissue growth. It is a direct extension of the classical framework of the multiplicative decomposition of the transformation gradient, to which an additional sub-transformation is introduced in order to let the original unloaded configuration evolve, hence relieving some residual stresses. We provide multiple illustrations of the framework mechanical response, on time-driven constrained growth as well as the strain-driven growth problem of the artery under internal pressure, including the opening angle experiment. FINDINGS The novel relaxed growth modeling framework introduced in this paper allows for a better control of growth-induced residual stresses compared to standard growth models based on the multiplicative decomposition of the transformation gradient. INTERPRETATION Growth-induced residual stresses should be better handled in soft tissues biomechanical models, especially in patient-specific models of diseased organs that are aimed at augmented diagnosis and treatment optimization.
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Affiliation(s)
- M Genet
- Laboratoire de Mécanique des Solides, École Polytechnique/Institut Polytechnique de Paris/CNRS, Palaiseau, France; M3DISIM Team, INRIA/Université Paris-Saclay, Palaiseau, France.
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15
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Naci H, Salcher-Konrad M, Mcguire A, Berger F, Kuehne T, Goubergrits L, Muthurangu V, Wilson B, Kelm M. Impact of predictive medicine on therapeutic decision making: a randomized controlled trial in congenital heart disease. NPJ Digit Med 2019; 2:17. [PMID: 31304365 PMCID: PMC6550204 DOI: 10.1038/s41746-019-0085-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 02/01/2019] [Indexed: 11/09/2022] Open
Abstract
Computational modelling has made significant progress towards clinical application in recent years. In addition to providing detailed diagnostic data, these methods have the potential to simulate patient-specific interventions and to predict their outcome. Our objective was to evaluate to which extent patient-specific modelling influences treatment decisions in coarctation of the aorta (CoA), a common congenital heart disease. We selected three cases with CoA, two of which had borderline indications for intervention according to current clinical guidelines. The third case was not indicated for intervention according to guidelines. For each case, we generated two separate datasets. First dataset included conventional diagnostic parameters (echocardiography and magnetic resonance imaging). In the second, we added modelled parameters (pressure fields). For the two cases with borderline indications for intervention, the second dataset also included pressure fields after virtual stenting simulations. All parameters were computed by modelling methods that were previously validated. In an online-administered, invitation-only survey, we randomized 178 paediatric cardiologists to view either conventional (control) or add-on modelling (experimental) datasets. Primary endpoint was the proportion of participants recommending different therapeutic options: (1) surgery or catheter lab (collectively, "intervention") or (2) no intervention (follow-up with or without medication). Availability of data from computational predictive modelling influenced therapeutic decision making in two of three cases. There was a statistically significant association between group assignment and the recommendation of an intervention for one borderline case and one non-borderline case: 94.3% vs. 72.2% (RR: 1.31, 95% CI: 1.14-1.50, p = 0.00) and 18.8% vs. 5.1% (RR: 3.09, 95% CI: 1.17-8.18, p = 0.01) of participants in the experimental and control groups respectively recommended an intervention. For the remaining case, there was no difference between the experimental and control group and the majority of participants recommended intervention. In sub-group analyses, findings were not affected by the experience level of participating cardiologists. Despite existing clinical guidelines, the therapy recommendations of the participating physicians were heterogeneous. Validated patient-specific computational modelling has the potential to influence treatment decisions. Future studies in broader areas are needed to evaluate whether differences in decisions result in improved outcomes (Trial Registration: NCT02700737).
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Affiliation(s)
- Huseyin Naci
- 1LSE Health, Department of Health Policy, London School of Economics and Political Science, London, UK
| | - Maximilian Salcher-Konrad
- 1LSE Health, Department of Health Policy, London School of Economics and Political Science, London, UK
| | - Alistair Mcguire
- 1LSE Health, Department of Health Policy, London School of Economics and Political Science, London, UK
| | - Felix Berger
- 2German Heart Institute Berlin (DHZB), Berlin, Germany.,3Charité - Universitätsmedizin Berlin, Pediatric Cardiology, Berlin, Germany.,4DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
| | - Titus Kuehne
- 2German Heart Institute Berlin (DHZB), Berlin, Germany.,4DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany.,Institute for Computational and Imaging Science in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Leonid Goubergrits
- Institute for Computational and Imaging Science in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Vivek Muthurangu
- 6Great Ormond Street Hospital, University College London, London, UK
| | - Ben Wilson
- 7Department of Sociology, Stockholm University, Stockholm, Sweden.,8Department of Methodology, London School of Economics and Political Science, London, UK
| | - Marcus Kelm
- 2German Heart Institute Berlin (DHZB), Berlin, Germany.,3Charité - Universitätsmedizin Berlin, Pediatric Cardiology, Berlin, Germany.,Institute for Computational and Imaging Science in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
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16
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Lopez-Perez A, Sebastian R, Izquierdo M, Ruiz R, Bishop M, Ferrero JM. Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia. Front Physiol 2019; 10:580. [PMID: 31156460 PMCID: PMC6531915 DOI: 10.3389/fphys.2019.00580] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 04/25/2019] [Indexed: 12/20/2022] Open
Abstract
In the chronic stage of myocardial infarction, a significant number of patients develop life-threatening ventricular tachycardias (VT) due to the arrhythmogenic nature of the remodeled myocardium. Radiofrequency ablation (RFA) is a common procedure to isolate reentry pathways across the infarct scar that are responsible for VT. Unfortunately, this strategy show relatively low success rates; up to 50% of patients experience recurrent VT after the procedure. In the last decade, intensive research in the field of computational cardiac electrophysiology (EP) has demonstrated the ability of three-dimensional (3D) cardiac computational models to perform in-silico EP studies. However, the personalization and modeling of certain key components remain challenging, particularly in the case of the infarct border zone (BZ). In this study, we used a clinical dataset from a patient with a history of infarct-related VT to build an image-based 3D ventricular model aimed at computational simulation of cardiac EP, including detailed patient-specific cardiac anatomy and infarct scar geometry. We modeled the BZ in eight different ways by combining the presence or absence of electrical remodeling with four different levels of image-based patchy fibrosis (0, 10, 20, and 30%). A 3D torso model was also constructed to compute the ECG. Patient-specific sinus activation patterns were simulated and validated against the patient's ECG. Subsequently, the pacing protocol used to induce reentrant VTs in the EP laboratory was reproduced in-silico. The clinical VT was induced with different versions of the model and from different pacing points, thus identifying the slow conducting channel responsible for such VT. Finally, the real patient's ECG recorded during VT episodes was used to validate our simulation results and to assess different strategies to model the BZ. Our study showed that reduced conduction velocities and heterogeneity in action potential duration in the BZ are the main factors in promoting reentrant activity. Either electrical remodeling or fibrosis in a degree of at least 30% in the BZ were required to initiate VT. Moreover, this proof-of-concept study confirms the feasibility of developing 3D computational models for cardiac EP able to reproduce cardiac activation in sinus rhythm and during VT, using exclusively non-invasive clinical data.
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Affiliation(s)
- Alejandro Lopez-Perez
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Rafael Sebastian
- Computational Multiscale Simulation Lab (CoMMLab), Universitat de València, Valencia, Spain
| | - M Izquierdo
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Ricardo Ruiz
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Martin Bishop
- Division of Imaging Sciences & Biomedical Engineering, Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Jose M Ferrero
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
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17
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Molléro R, Pennec X, Delingette H, Ayache N, Sermesant M. Population-based priors in cardiac model personalisation for consistent parameter estimation in heterogeneous databases. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2019; 35:e3158. [PMID: 30239175 DOI: 10.1002/cnm.3158] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 09/10/2018] [Accepted: 09/16/2018] [Indexed: 06/08/2023]
Abstract
Personalised cardiac models are a virtual representation of the patient heart, with parameter values for which the simulation fits the available clinical measurements. Models usually have a large number of parameters while the available data for a given patient are typically limited to a small set of measurements; thus, the parameters cannot be estimated uniquely. This is a practical obstacle for clinical applications, where accurate parameter values can be important. Here, we explore an original approach based on an algorithm called Iteratively Updated Priors (IUP), in which we perform successive personalisations of a full database through maximum a posteriori (MAP) estimation, where the prior probability at an iteration is set from the distribution of personalised parameters in the database at the previous iteration. At the convergence of the algorithm, estimated parameters of the population lie on a linear subspace of reduced (and possibly sufficient) dimension in which for each case of the database, there is a (possibly unique) parameter value for which the simulation fits the measurements. We first show how this property can help the modeller select a relevant parameter subspace for personalisation. In addition, since the resulting priors in this subspace represent the population statistics in this subspace, they can be used to perform consistent parameter estimation for cases where measurements are possibly different or missing in the database, which we illustrate with the personalisation of a heterogeneous database of 811 cases.
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Affiliation(s)
- Roch Molléro
- Inria, Epione Research Project, Sophia Antipolis, France
| | - Xavier Pennec
- Inria, Epione Research Project, Sophia Antipolis, France
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18
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Casas B, Viola F, Cedersund G, Bolger AF, Karlsson M, Carlhäll CJ, Ebbers T. Non-invasive Assessment of Systolic and Diastolic Cardiac Function During Rest and Stress Conditions Using an Integrated Image-Modeling Approach. Front Physiol 2018; 9:1515. [PMID: 30425650 PMCID: PMC6218619 DOI: 10.3389/fphys.2018.01515] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 10/09/2018] [Indexed: 01/08/2023] Open
Abstract
Background: The possibility of non-invasively assessing load-independent parameters characterizing cardiac function is of high clinical value. Typically, these parameters are assessed during resting conditions. However, for diagnostic purposes, the parameter behavior across a physiologically relevant range of heart rate and loads is more relevant than the isolated measurements performed at rest. This study sought to evaluate changes in non-invasive estimations of load-independent parameters of left-ventricular contraction and relaxation patterns at rest and during dobutamine stress. Methods: We applied a previously developed approach that combines non-invasive measurements with a physiologically-based, reduced-order model of the cardiovascular system to provide subject-specific estimates of parameters characterizing left ventricular function. In this model, the contractile state of the heart at each time point along the cardiac cycle is modeled using a time-varying elastance curve. Non-invasive data, including four-dimensional magnetic resonance imaging (4D Flow MRI) measurements, were acquired in nine subjects without a known heart disease at rest and during dobutamine stress. For each of the study subjects, we constructed two personalized models corresponding to the resting and the stress state. Results: Applying the modeling framework, we identified significant increases in the left ventricular contraction rate constant [from 1.5 ± 0.3 to 2 ± 0.5 (p = 0.038)] and relaxation constant [from 37.2 ± 6.9 to 46.1 ± 12 (p = 0.028)]. In addition, we found a significant decrease in the elastance diastolic time constant from 0.4 ± 0.04 s to 0.3 ± 0.03 s (p = 0.008). Conclusions: The integrated image-modeling approach allows the assessment of cardiovascular function given as model-based parameters. The agreement between the estimated parameter values and previously reported effects of dobutamine demonstrates the potential of the approach to assess advanced metrics of pathophysiology that are otherwise difficult to obtain non-invasively in clinical practice.
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Affiliation(s)
- Belén Casas
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Federica Viola
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Gunnar Cedersund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Ann F Bolger
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Department of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Matts Karlsson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Division of Applied Thermodynamics and Fluid Mechanics, Department of Management and Engineering, Linköping University, Linköping, Sweden
| | - Carl-Johan Carlhäll
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Clinical Physiology, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Tino Ebbers
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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19
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Gray RA, Pathmanathan P. Patient-Specific Cardiovascular Computational Modeling: Diversity of Personalization and Challenges. J Cardiovasc Transl Res 2018; 11:80-88. [PMID: 29512059 PMCID: PMC5908828 DOI: 10.1007/s12265-018-9792-2] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 02/02/2018] [Indexed: 02/07/2023]
Abstract
Patient-specific computer models have been developed representing a variety of aspects of the cardiovascular system spanning the disciplines of electrophysiology, electromechanics, solid mechanics, and fluid dynamics. These physiological mechanistic models predict macroscopic phenomena such as electrical impulse propagation and contraction throughout the entire heart as well as flow and pressure dynamics occurring in the ventricular chambers, aorta, and coronary arteries during each heartbeat. Such models have been used to study a variety of clinical scenarios including aortic aneurysms, coronary stenosis, cardiac valvular disease, left ventricular assist devices, cardiac resynchronization therapy, ablation therapy, and risk stratification. After decades of research, these models are beginning to be incorporated into clinical practice directly via marketed devices and indirectly by improving our understanding of the underlying mechanisms of health and disease within a clinical context.
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Affiliation(s)
- Richard A Gray
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA.
- , Silver Spring, USA.
| | - Pras Pathmanathan
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA
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20
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Chalon A, Favre J, Piotrowski B, Landmann V, Grandmougin D, Maureira JP, Laheurte P, Tran N. Contribution of computational model for assessment of heart tissue local stress caused by suture in LVAD implantation. J Mech Behav Biomed Mater 2018; 82:291-298. [PMID: 29649657 DOI: 10.1016/j.jmbbm.2018.03.032] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Revised: 03/23/2018] [Accepted: 03/26/2018] [Indexed: 10/17/2022]
Abstract
STUDY Implantation of a Left Ventricular Assist Device (LVAD) may produce both excessive local tissue stress and resulting strain-induced tissue rupture that are potential iatrogenic factors influencing the success of the surgical attachment of the LVAD into the myocardium. By using a computational simulation compared to mechanical tests, we sought to investigate the characteristics of stress-induced suture material on porcine myocardium. METHODS Tensile strength experiments (n = 8) were performed on bulk left myocardium to establish a hyperelastic reduced polynomial constitutive law. Simultaneously, suture strength tests on left myocardium (n = 6) were performed with a standard tensile test setup. Experiments were made on bulk ventricular wall with a single U-suture (polypropylene 3-0) and a PTFE pledget. Then, a Finite Element simulation of a LVAD suture case was performed. Strength versus displacement behavior was compared between mechanical and numerical experiments. Local stress fields in the model were thus analyzed. RESULTS A strong correlation between the experimental and the numerical responses was observed, validating the relevance of the numerical model. A secure damage limit of 100 kPa on heart tissue was defined from mechanical suture testing and used to describe numerical results. The impact of suture on heart tissue could be accurately determined through new parameters of numerical data (stress diffusion, triaxiality stress). Finally, an ideal spacing between sutures of 2 mm was proposed. CONCLUSION Our computational model showed a reliable ability to provide and predict various local tissue stresses created by suture penetration into the myocardium. In addition, this model contributed to providing valuable information useful to design less traumatic sutures for LVAD implantation. Therefore, our computational model is a promising tool to predict and optimize LVAD myocardial suture.
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Affiliation(s)
- A Chalon
- Ecole de Chirurgie Nancy-Lorraine, HVL, Université de Lorraine, Nancy, France; UMR INSERM 1116 DCAC, Nancy, France; Université de Lorraine, CNRS, Arts et Métiers ParisTech, LEM3, F-57000 Metz, France.
| | - J Favre
- Laboratoire Georges Friedel, ENSM-SE, UMR CNRS 5307, Saint-Etienne, France
| | - B Piotrowski
- Université de Lorraine, CNRS, Arts et Métiers ParisTech, LEM3, F-57000 Metz, France
| | - V Landmann
- Université de Lorraine, CNRS, Arts et Métiers ParisTech, LEM3, F-57000 Metz, France
| | - D Grandmougin
- Ecole de Chirurgie Nancy-Lorraine, HVL, Université de Lorraine, Nancy, France
| | - J-P Maureira
- Ecole de Chirurgie Nancy-Lorraine, HVL, Université de Lorraine, Nancy, France
| | - P Laheurte
- Université de Lorraine, CNRS, Arts et Métiers ParisTech, LEM3, F-57000 Metz, France
| | - N Tran
- Ecole de Chirurgie Nancy-Lorraine, HVL, Université de Lorraine, Nancy, France; UMR INSERM 1116 DCAC, Nancy, France
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21
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Gao H, Aderhold A, Mangion K, Luo X, Husmeier D, Berry C. Changes and classification in myocardial contractile function in the left ventricle following acute myocardial infarction. J R Soc Interface 2018; 14:rsif.2017.0203. [PMID: 28747397 PMCID: PMC5550971 DOI: 10.1098/rsif.2017.0203] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 07/04/2017] [Indexed: 01/05/2023] Open
Abstract
In this research, we hypothesized that novel biomechanical parameters are discriminative in patients following acute ST-segment elevation myocardial infarction (STEMI). To identify these biomechanical biomarkers and bring computational biomechanics ‘closer to the clinic’, we applied state-of-the-art multiphysics cardiac modelling combined with advanced machine learning and multivariate statistical inference to a clinical database of myocardial infarction. We obtained data from 11 STEMI patients (ClinicalTrials.gov NCT01717573) and 27 healthy volunteers, and developed personalized mathematical models for the left ventricle (LV) using an immersed boundary method. Subject-specific constitutive parameters were achieved by matching to clinical measurements. We have shown, for the first time, that compared with healthy controls, patients with STEMI exhibited increased LV wall active tension when normalized by systolic blood pressure, which suggests an increased demand on the contractile reserve of remote functional myocardium. The statistical analysis reveals that the required patient-specific contractility, normalized active tension and the systolic myofilament kinematics have the strongest explanatory power for identifying the myocardial function changes post-MI. We further observed a strong correlation between two biomarkers and the changes in LV ejection fraction at six months from baseline (the required contractility (r = − 0.79, p < 0.01) and the systolic myofilament kinematics (r = 0.70, p = 0.02)). The clinical and prognostic significance of these biomechanical parameters merits further scrutinization.
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Affiliation(s)
- Hao Gao
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Andrej Aderhold
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Kenneth Mangion
- British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
| | - Xiaoyu Luo
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Dirk Husmeier
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Colin Berry
- British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
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Pathmanathan P, Gray RA. Validation and Trustworthiness of Multiscale Models of Cardiac Electrophysiology. Front Physiol 2018; 9:106. [PMID: 29497385 PMCID: PMC5818422 DOI: 10.3389/fphys.2018.00106] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 01/31/2018] [Indexed: 02/06/2023] Open
Abstract
Computational models of cardiac electrophysiology have a long history in basic science applications and device design and evaluation, but have significant potential for clinical applications in all areas of cardiovascular medicine, including functional imaging and mapping, drug safety evaluation, disease diagnosis, patient selection, and therapy optimisation or personalisation. For all stakeholders to be confident in model-based clinical decisions, cardiac electrophysiological (CEP) models must be demonstrated to be trustworthy and reliable. Credibility, that is, the belief in the predictive capability, of a computational model is primarily established by performing validation, in which model predictions are compared to experimental or clinical data. However, there are numerous challenges to performing validation for highly complex multi-scale physiological models such as CEP models. As a result, credibility of CEP model predictions is usually founded upon a wide range of distinct factors, including various types of validation results, underlying theory, evidence supporting model assumptions, evidence from model calibration, all at a variety of scales from ion channel to cell to organ. Consequently, it is often unclear, or a matter for debate, the extent to which a CEP model can be trusted for a given application. The aim of this article is to clarify potential rationale for the trustworthiness of CEP models by reviewing evidence that has been (or could be) presented to support their credibility. We specifically address the complexity and multi-scale nature of CEP models which makes traditional model evaluation difficult. In addition, we make explicit some of the credibility justification that we believe is implicitly embedded in the CEP modeling literature. Overall, we provide a fresh perspective to CEP model credibility, and build a depiction and categorisation of the wide-ranging body of credibility evidence for CEP models. This paper also represents a step toward the extension of model evaluation methodologies that are currently being developed by the medical device community, to physiological models.
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Affiliation(s)
- Pras Pathmanathan
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, United States
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23
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Rocatello G, El Faquir N, De Santis G, Iannaccone F, Bosmans J, De Backer O, Sondergaard L, Segers P, De Beule M, de Jaegere P, Mortier P. Patient-Specific Computer Simulation to Elucidate the Role of Contact Pressure in the Development of New Conduction Abnormalities After Catheter-Based Implantation of a Self-Expanding Aortic Valve. Circ Cardiovasc Interv 2018; 11:e005344. [DOI: 10.1161/circinterventions.117.005344] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 12/18/2017] [Indexed: 11/16/2022]
Affiliation(s)
- Giorgia Rocatello
- From the IBiTech-bioMMeda, Ghent University, Belgium (G.R., P.S., M.D.B.); Department of Cardiology, Erasmus MC, Rotterdam, the Netherlands (N.E.F., P.d.J.); FEops NV, Ghent, Belgium (G.D.S., F.I., M.D.B., P.M.); University Hospital Antwerp, Belgium (J.B.); and Department of Cardiology, Rigshospitalet University Hospital, Copenhagen, Denmark (O.D.B., L.S.)
| | - Nahid El Faquir
- From the IBiTech-bioMMeda, Ghent University, Belgium (G.R., P.S., M.D.B.); Department of Cardiology, Erasmus MC, Rotterdam, the Netherlands (N.E.F., P.d.J.); FEops NV, Ghent, Belgium (G.D.S., F.I., M.D.B., P.M.); University Hospital Antwerp, Belgium (J.B.); and Department of Cardiology, Rigshospitalet University Hospital, Copenhagen, Denmark (O.D.B., L.S.)
| | - Gianluca De Santis
- From the IBiTech-bioMMeda, Ghent University, Belgium (G.R., P.S., M.D.B.); Department of Cardiology, Erasmus MC, Rotterdam, the Netherlands (N.E.F., P.d.J.); FEops NV, Ghent, Belgium (G.D.S., F.I., M.D.B., P.M.); University Hospital Antwerp, Belgium (J.B.); and Department of Cardiology, Rigshospitalet University Hospital, Copenhagen, Denmark (O.D.B., L.S.)
| | - Francesco Iannaccone
- From the IBiTech-bioMMeda, Ghent University, Belgium (G.R., P.S., M.D.B.); Department of Cardiology, Erasmus MC, Rotterdam, the Netherlands (N.E.F., P.d.J.); FEops NV, Ghent, Belgium (G.D.S., F.I., M.D.B., P.M.); University Hospital Antwerp, Belgium (J.B.); and Department of Cardiology, Rigshospitalet University Hospital, Copenhagen, Denmark (O.D.B., L.S.)
| | - Johan Bosmans
- From the IBiTech-bioMMeda, Ghent University, Belgium (G.R., P.S., M.D.B.); Department of Cardiology, Erasmus MC, Rotterdam, the Netherlands (N.E.F., P.d.J.); FEops NV, Ghent, Belgium (G.D.S., F.I., M.D.B., P.M.); University Hospital Antwerp, Belgium (J.B.); and Department of Cardiology, Rigshospitalet University Hospital, Copenhagen, Denmark (O.D.B., L.S.)
| | - Ole De Backer
- From the IBiTech-bioMMeda, Ghent University, Belgium (G.R., P.S., M.D.B.); Department of Cardiology, Erasmus MC, Rotterdam, the Netherlands (N.E.F., P.d.J.); FEops NV, Ghent, Belgium (G.D.S., F.I., M.D.B., P.M.); University Hospital Antwerp, Belgium (J.B.); and Department of Cardiology, Rigshospitalet University Hospital, Copenhagen, Denmark (O.D.B., L.S.)
| | - Lars Sondergaard
- From the IBiTech-bioMMeda, Ghent University, Belgium (G.R., P.S., M.D.B.); Department of Cardiology, Erasmus MC, Rotterdam, the Netherlands (N.E.F., P.d.J.); FEops NV, Ghent, Belgium (G.D.S., F.I., M.D.B., P.M.); University Hospital Antwerp, Belgium (J.B.); and Department of Cardiology, Rigshospitalet University Hospital, Copenhagen, Denmark (O.D.B., L.S.)
| | - Patrick Segers
- From the IBiTech-bioMMeda, Ghent University, Belgium (G.R., P.S., M.D.B.); Department of Cardiology, Erasmus MC, Rotterdam, the Netherlands (N.E.F., P.d.J.); FEops NV, Ghent, Belgium (G.D.S., F.I., M.D.B., P.M.); University Hospital Antwerp, Belgium (J.B.); and Department of Cardiology, Rigshospitalet University Hospital, Copenhagen, Denmark (O.D.B., L.S.)
| | - Matthieu De Beule
- From the IBiTech-bioMMeda, Ghent University, Belgium (G.R., P.S., M.D.B.); Department of Cardiology, Erasmus MC, Rotterdam, the Netherlands (N.E.F., P.d.J.); FEops NV, Ghent, Belgium (G.D.S., F.I., M.D.B., P.M.); University Hospital Antwerp, Belgium (J.B.); and Department of Cardiology, Rigshospitalet University Hospital, Copenhagen, Denmark (O.D.B., L.S.)
| | - Peter de Jaegere
- From the IBiTech-bioMMeda, Ghent University, Belgium (G.R., P.S., M.D.B.); Department of Cardiology, Erasmus MC, Rotterdam, the Netherlands (N.E.F., P.d.J.); FEops NV, Ghent, Belgium (G.D.S., F.I., M.D.B., P.M.); University Hospital Antwerp, Belgium (J.B.); and Department of Cardiology, Rigshospitalet University Hospital, Copenhagen, Denmark (O.D.B., L.S.)
| | - Peter Mortier
- From the IBiTech-bioMMeda, Ghent University, Belgium (G.R., P.S., M.D.B.); Department of Cardiology, Erasmus MC, Rotterdam, the Netherlands (N.E.F., P.d.J.); FEops NV, Ghent, Belgium (G.D.S., F.I., M.D.B., P.M.); University Hospital Antwerp, Belgium (J.B.); and Department of Cardiology, Rigshospitalet University Hospital, Copenhagen, Denmark (O.D.B., L.S.)
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Viceconti M, Cobelli C, Haddad T, Himes A, Kovatchev B, Palmer M. In silico assessment of biomedical products: The conundrum of rare but not so rare events in two case studies. Proc Inst Mech Eng H 2017; 231:455-466. [PMID: 28427321 DOI: 10.1177/0954411917702931] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
In silico clinical trials, defined as "The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention," have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients' phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern.
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Affiliation(s)
- Marco Viceconti
- 1 Department of Mechanical Engineering, INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
| | - Claudio Cobelli
- 2 Department of Information Engineering, University of Padova, Padova, Italy
| | | | | | - Boris Kovatchev
- 4 Center for Diabetes Technology, The University of Virginia, Charlottesville, VA, USA
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Gao H, Mangion K, Carrick D, Husmeier D, Luo X, Berry C. Estimating prognosis in patients with acute myocardial infarction using personalized computational heart models. Sci Rep 2017; 7:13527. [PMID: 29051544 PMCID: PMC5648923 DOI: 10.1038/s41598-017-13635-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 09/12/2017] [Indexed: 02/06/2023] Open
Abstract
Biomechanical computational models have potential prognostic utility in patients after an acute ST-segment-elevation myocardial infarction (STEMI). In a proof-of-concept study, we defined two groups (1) an acute STEMI group (n = 6, 83% male, age 54 ± 12 years) complicated by left ventricular (LV) systolic dysfunction; (2) an age- and sex- matched hyper-control group (n = 6, 83% male, age 46 ± 14 years), no prior history of cardiovascular disease and normal systolic blood pressure (SBP < 130 mmHg). Cardiac MRI was performed in the patients (2 days & 6 months post-STEMI) and the volunteers, and biomechanical heart models were synthesized for each subject. The candidate parameters included normalized active tension (AT norm) and active tension at the resting sarcomere length (T req, reflecting required contractility). Myocardial contractility was inversely determined from personalized heart models by matching CMR-imaged LV dynamics. Compared with controls, patients with recent STEMI exhibited increased LV wall active tension when normalized by SBP. We observed a linear relationship between T req 2 days post-MI and global longitudinal strain 6 months later (r = 0.86; p = 0.03). T req may be associated with changes in LV function in the longer term in STEMI patients complicated by LV dysfunction. Further studies seem warranted.
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Affiliation(s)
- Hao Gao
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Kenneth Mangion
- British Heart Foundation, Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Science, University of Glasgow, Glasgow, UK
- Golden Jubilee National Hospital, Clydebank, UK
| | - David Carrick
- British Heart Foundation, Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Science, University of Glasgow, Glasgow, UK
- Golden Jubilee National Hospital, Clydebank, UK
| | - Dirk Husmeier
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Xiaoyu Luo
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Colin Berry
- British Heart Foundation, Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Science, University of Glasgow, Glasgow, UK.
- Golden Jubilee National Hospital, Clydebank, UK.
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Abstract
Biomedical research and clinical practice are struggling to cope with the growing complexity that the progress of health care involves. The most challenging diseases, those with the largest socioeconomic impact (cardiovascular conditions; musculoskeletal conditions; cancer; metabolic, immunity, and neurodegenerative conditions), are all characterized by a complex genotype-phenotype interaction and by a "systemic" nature that poses a challenge to the traditional reductionist approach. In 2005 a small group of researchers discussed how the vision of computational physiology promoted by the Physiome Project could be translated into clinical practice and formally proposed the term Virtual Physiological Human. Our knowledge about these diseases is fragmentary, as it is associated with molecular and cellular processes on the one hand and with tissue and organ phenotype changes (related to clinical symptoms of disease conditions) on the other. The problem could be solved if we could capture all these fragments of knowledge into predictive models and then compose them into hypermodels that help us tame the complexity that such systemic behavior involves. In 2005 this was simply not possible-the necessary methods and technologies were not available. Now, 10 years later, it seems the right time to reflect on the original vision, the results achieved so far, and what remains to be done.
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Affiliation(s)
- Marco Viceconti
- Department of Mechanical Engineering and Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield S1 3JD, United Kingdom;
| | - Peter Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
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Gao H, Qi N, Feng L, Ma X, Danton M, Berry C, Luo X. Modelling mitral valvular dynamics-current trend and future directions. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:e2858. [PMID: 27935265 PMCID: PMC5697636 DOI: 10.1002/cnm.2858] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Revised: 09/30/2016] [Accepted: 11/26/2016] [Indexed: 05/19/2023]
Abstract
Dysfunction of mitral valve causes morbidity and premature mortality and remains a leading medical problem worldwide. Computational modelling aims to understand the biomechanics of human mitral valve and could lead to the development of new treatment, prevention and diagnosis of mitral valve diseases. Compared with the aortic valve, the mitral valve has been much less studied owing to its highly complex structure and strong interaction with the blood flow and the ventricles. However, the interest in mitral valve modelling is growing, and the sophistication level is increasing with the advanced development of computational technology and imaging tools. This review summarises the state-of-the-art modelling of the mitral valve, including static and dynamics models, models with fluid-structure interaction, and models with the left ventricle interaction. Challenges and future directions are also discussed.
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Affiliation(s)
- Hao Gao
- School of Mathematics and StatisticsUniversity of GlasgowUK
| | - Nan Qi
- School of Mathematics and StatisticsUniversity of GlasgowUK
| | - Liuyang Feng
- School of Mathematics and StatisticsUniversity of GlasgowUK
| | | | - Mark Danton
- Department of Cardiac SurgeryRoyal Hospital for ChildrenGlasgowUK
| | - Colin Berry
- Institute of Cardiovascular and Medical SciencesUniversity of GlasgowUK
| | - Xiaoyu Luo
- School of Mathematics and StatisticsUniversity of GlasgowUK
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Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models. Biomech Model Mechanobiol 2017; 17:285-300. [PMID: 28894984 DOI: 10.1007/s10237-017-0960-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Accepted: 08/31/2017] [Indexed: 10/18/2022]
Abstract
Personalised computational models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However, the simulation of a single heartbeat with a 3D cardiac electromechanical model can be long and computationally expensive, which makes some practical applications, such as the estimation of model parameters from clinical data (the personalisation), very slow. Here we introduce an original multifidelity approach between a 3D cardiac model and a simplified "0D" version of this model, which enables to get reliable (and extremely fast) approximations of the global behaviour of the 3D model using 0D simulations. We then use this multifidelity approximation to speed-up an efficient parameter estimation algorithm, leading to a fast and computationally efficient personalisation method of the 3D model. In particular, we show results on a cohort of 121 different heart geometries and measurements. Finally, an exploitable code of the 0D model with scripts to perform parameter estimation will be released to the community.
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Hessenthaler A, Gaddum NR, Holub O, Sinkus R, Röhrle O, Nordsletten D. Experiment for validation of fluid-structure interaction models and algorithms. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:e2848. [PMID: 27813272 PMCID: PMC5600002 DOI: 10.1002/cnm.2848] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Revised: 10/22/2016] [Accepted: 10/25/2016] [Indexed: 05/30/2023]
Abstract
In this paper a fluid-structure interaction (FSI) experiment is presented. The aim of this experiment is to provide a challenging yet easy-to-setup FSI test case that addresses the need for rigorous testing of FSI algorithms and modeling frameworks. Steady-state and periodic steady-state test cases with constant and periodic inflow were established. Focus of the experiment is on biomedical engineering applications with flow being in the laminar regime with Reynolds numbers 1283 and 651. Flow and solid domains were defined using computer-aided design (CAD) tools. The experimental design aimed at providing a straightforward boundary condition definition. Material parameters and mechanical response of a moderately viscous Newtonian fluid and a nonlinear incompressible solid were experimentally determined. A comprehensive data set was acquired by using magnetic resonance imaging to record the interaction between the fluid and the solid, quantifying flow and solid motion.
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Affiliation(s)
- A. Hessenthaler
- Institute of Applied Mechanics (CE)University of StuttgartPfaffenwaldring 770569 StuttgartGermany
| | - N. R. Gaddum
- Division of Imaging Sciences and Biomedical EngineeringKing's College London, 4th Floor, Lambeth Wing St. Thomas Hospital LondonSE1 7EHUK
| | - O. Holub
- Division of Imaging Sciences and Biomedical EngineeringKing's College London, 4th Floor, Lambeth Wing St. Thomas Hospital LondonSE1 7EHUK
| | - R. Sinkus
- Division of Imaging Sciences and Biomedical EngineeringKing's College London, 4th Floor, Lambeth Wing St. Thomas Hospital LondonSE1 7EHUK
| | - O. Röhrle
- Institute of Applied Mechanics (CE)University of StuttgartPfaffenwaldring 770569 StuttgartGermany
| | - D. Nordsletten
- Division of Imaging Sciences and Biomedical EngineeringKing's College London, 4th Floor, Lambeth Wing St. Thomas Hospital LondonSE1 7EHUK
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Hessenthaler A, Röhrle O, Nordsletten D. Validation of a non-conforming monolithic fluid-structure interaction method using phase-contrast MRI. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:e2845. [PMID: 27813346 PMCID: PMC5574003 DOI: 10.1002/cnm.2845] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Revised: 10/13/2016] [Accepted: 10/21/2016] [Indexed: 06/01/2023]
Abstract
This paper details the validation of a non-conforming arbitrary Lagrangian-Eulerian fluid-structure interaction technique using a recently developed experimental 3D fluid-structure interaction benchmark problem. Numerical experiments for steady and transient test cases of the benchmark were conducted employing an inf-sup stable and a general Galerkin scheme. The performance of both schemes is assessed. Spatial refinement with three mesh refinement levels and fluid domain truncation with two fluid domain lengths are studied as well as employing a sequence of increasing time step sizes for steady-state cases. How quickly an approximate steady-state or periodic steady-state is reached is investigated and quantified based on error norm computations. Comparison of numerical results with experimental phase-contrast magnetic resonance imaging data shows very good overall agreement including governing of flow patterns observed in the experiment.
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Affiliation(s)
- Andreas Hessenthaler
- Institute of Applied Mechanics (CE)University of StuttgartPfaffenwaldring 770569 StuttgartGermany
| | - Oliver Röhrle
- Institute of Applied Mechanics (CE)University of StuttgartPfaffenwaldring 770569 StuttgartGermany
| | - David Nordsletten
- Division of Imaging Sciences and Biomedical EngineeringKing's College London4th Floor, Lambeth Wing St. Thomas Hospital London, SE1 7EHUK
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Cai L, Wang Y, Gao H, Li Y, Luo X. A mathematical model for active contraction in healthy and failing myocytes and left ventricles. PLoS One 2017; 12:e0174834. [PMID: 28406991 PMCID: PMC5391010 DOI: 10.1371/journal.pone.0174834] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Accepted: 03/15/2017] [Indexed: 02/07/2023] Open
Abstract
Cardiovascular disease is one of the leading causes of death worldwide, in particular myocardial dysfunction, which may lead to heart failure eventually. Understanding the electro-mechanics of the heart will help in developing more effective clinical treatments. In this paper, we present a multi-scale electro-mechanics model of the left ventricle (LV). The Holzapfel-Ogden constitutive law was used to describe the passive myocardial response in tissue level, a modified Grandi-Pasqualini-Bers model was adopted to model calcium dynamics in individual myocytes, and the active tension was described using the Niederer-Hunter-Smith myofilament model. We first studied the electro-mechanics coupling in a single myocyte in the healthy and diseased left ventricle, and then the single cell model was embedded in a dynamic LV model to investigate the compensation mechanism of LV pump function due to myocardial dysfunction caused by abnormality in cellular calcium dynamics. The multi-scale LV model was solved using an in-house developed hybrid immersed boundary method with finite element extension. The predictions of the healthy LV model agreed well with the clinical measurements and other studies, and likewise, the results in the failing states were also consistent with clinical observations. In particular, we found that a low level of intracellular Ca2+ transient in myocytes can result in LV pump function failure even with increased myocardial contractility, decreased systolic blood pressure, and increased diastolic filling pressure, even though they will increase LV stroke volume. Our work suggested that treatments targeted at increased contractility and lowering the systolic blood pressure alone are not sufficient in preventing LV pump dysfunction, restoring a balanced physiological Ca2+ handling mechanism is necessary.
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Affiliation(s)
- Li Cai
- NPU-UoG International Cooperative Lab for Computation & Application in Cardiology, Northwestern Polytechnical University, Xi’an, Shanxi Province, China
| | - Yongheng Wang
- NPU-UoG International Cooperative Lab for Computation & Application in Cardiology, Northwestern Polytechnical University, Xi’an, Shanxi Province, China
| | - Hao Gao
- School of Mathematics and Statistics, University of Glasgow, Glasgow, Scotland, United Kingdom
| | - Yiqiang Li
- NPU-UoG International Cooperative Lab for Computation & Application in Cardiology, Northwestern Polytechnical University, Xi’an, Shanxi Province, China
| | - Xiaoyu Luo
- School of Mathematics and Statistics, University of Glasgow, Glasgow, Scotland, United Kingdom
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33
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Soto Iglesias D, Duchateau N, Kostantyn Butakov CB, Andreu D, Fernandez-Armenta J, Bijnens B, Berruezo A, Sitges M, Camara O. Quantitative Analysis of Electro-Anatomical Maps: Application to an Experimental Model of Left Bundle Branch Block/Cardiac Resynchronization Therapy. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2016; 5:1900215. [PMID: 29164019 PMCID: PMC5477765 DOI: 10.1109/jtehm.2016.2634006] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Revised: 08/08/2016] [Accepted: 11/14/2016] [Indexed: 01/02/2023]
Abstract
Electro-anatomical maps (EAMs) are commonly acquired in clinical routine for guiding
ablation therapies. They provide voltage and activation time information on a 3-D
anatomical mesh representation, making them useful for analyzing the electrical
activation patterns in specific pathologies. However, the variability between the
different acquisitions and anatomies hampers the comparison between different maps.
This paper presents two contributions for the analysis of electrical patterns in EAM
data from biventricular surfaces of cardiac chambers. The first contribution is an
integrated automatic 2-D disk representation (2-D bull’s eye plot) of the left
ventricle (LV) and right ventricle (RV) obtained with a quasi-conformal mapping from
the 3-D EAM meshes, that allows an analysis of cardiac resynchronization therapy
(CRT) lead positioning, interpretation of global (total activation time), and local
indices (local activation time (LAT), surrogates of conduction velocity,
inter-ventricular, and transmural delays) that characterize changes in the electrical
activation pattern. The second contribution is a set of indices derived from the
electrical activation: speed maps, computed from LAT values, to study the electrical
wave propagation, and histograms of isochrones to analyze regional electrical
heterogeneities in the ventricles. We have applied the proposed methods to look for
the underlying physiological mechanisms of left bundle branch block (LBBB) and CRT,
with the goal of optimizing the therapy by improving CRT response. To better
illustrate the benefits of the proposed tools, we created a set of synthetically
generated and fully controlled activation patterns, where the proposed representation
and indices were validated. Then, the proposed analysis tools are used to analyze EAM
data from an experimental swine model of induced LBBB with an implanted CRT device.
We have analyzed and compared the electrical activation patterns at baseline, LBBB,
and CRT stages in four animals: two without any structural disease and two with an
induced infarction. By relating the CRT lead location with electrical dyssynchrony,
we evaluated current hypotheses about lead placement in CRT and showed that optimal
pacing sites should target the RV lead close to the apex and the LV one distant from
it.
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Affiliation(s)
- David Soto Iglesias
- PhySense, Information and Communication Technologies DepartmentUniversitat Pompeu Fabra.,Cardiology DepartmentThorax Institute, Hospital Clinic
| | | | | | - David Andreu
- Cardiology DepartmentThorax Institute, Hospital Clinic
| | | | - Bart Bijnens
- PhySense, Information and Communication Technologies DepartmentUniversitat Pompeu Fabra.,Catalan Institution for Research and Advanced Studies
| | | | - Marta Sitges
- Cardiology DepartmentThorax Institute, Hospital Clinic
| | - Oscar Camara
- PhySense, Information and Communication Technologies DepartmentUniversitat Pompeu Fabra
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Niederer SA, Smith NP. Using physiologically based models for clinical translation: predictive modelling, data interpretation or something in-between? J Physiol 2016; 594:6849-6863. [PMID: 27121495 PMCID: PMC5134392 DOI: 10.1113/jp272003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2015] [Accepted: 03/13/2016] [Indexed: 02/02/2023] Open
Abstract
Heart disease continues to be a significant clinical problem in Western society. Predictive models and simulations that integrate physiological understanding with patient information derived from clinical data have huge potential to contribute to improving our understanding of both the progression and treatment of heart disease. In particular they provide the potential to improve patient selection and optimisation of cardiovascular interventions across a range of pathologies. Currently a significant proportion of this potential is still to be realised. In this paper we discuss the opportunities and challenges associated with this realisation. Reviewing the successful elements of model translation for biophysically based models and the emerging supporting technologies, we propose three distinct modes of clinical translation. Finally we outline the challenges ahead that will be fundamental to overcome if the ultimate goal of fully personalised clinical cardiac care is to be achieved.
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Affiliation(s)
- Steven A. Niederer
- Department of Biomedical Engineering and Imaging SciencesSt Thomas’ HospitalKing's College LondonThe Rayne Institute4th Floor Lambeth WingLondonSE1 7EHUK
| | - Nic P. Smith
- Department of Biomedical Engineering and Imaging SciencesSt Thomas’ HospitalKing's College LondonThe Rayne Institute4th Floor Lambeth WingLondonSE1 7EHUK
- Engineering School Block 1University of AucklandLevel 5, 20 Symonds StreetAuckland101New Zealand
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Patient-specific computer modelling - its role in the planning of transcatheter aortic valve implantation. Neth Heart J 2016; 25:100-105. [PMID: 27888494 PMCID: PMC5260618 DOI: 10.1007/s12471-016-0923-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Transcatheter aortic valve implantation is increasingly used to treat patients with severe aortic stenosis who are at increased risk for surgical aortic valve replacement and is projected to be the preferred treatment modality. As patient selection and operator experience have improved, it is hypothesised that device-host interactions will play a more dominant role in outcome. This, in combination with the increasing number of valve types and sizes, confronts the physician with the dilemma to choose the valve that best fits the individual patient. This necessitates the availability of pre-procedural computer simulation that is based upon the integration of the patient-specific anatomy, the physical and (bio)mechanical properties of the valve and recipient anatomy derived from in-vitro experiments. The objective of this paper is to present such a model and illustrate its potential clinical utility via a few case studies.
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36
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Soto-Iglesias D, Butakoff C, Andreu D, Fernández-Armenta J, Berruezo A, Camara O. Integration of electro-anatomical and imaging data of the left ventricle: An evaluation framework. Med Image Anal 2016; 32:131-44. [DOI: 10.1016/j.media.2016.03.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Revised: 01/22/2016] [Accepted: 03/25/2016] [Indexed: 10/22/2022]
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Frangi AF, Taylor ZA, Gooya A. Precision Imaging: more descriptive, predictive and integrative imaging. Med Image Anal 2016; 33:27-32. [PMID: 27373145 DOI: 10.1016/j.media.2016.06.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Revised: 06/15/2016] [Accepted: 06/15/2016] [Indexed: 12/22/2022]
Abstract
Medical image analysis has grown into a matured field challenged by progress made across all medical imaging technologies and more recent breakthroughs in biological imaging. The cross-fertilisation between medical image analysis, biomedical imaging physics and technology, and domain knowledge from medicine and biology has spurred a truly interdisciplinary effort that stretched outside the original boundaries of the disciplines that gave birth to this field and created stimulating and enriching synergies. Consideration on how the field has evolved and the experience of the work carried out over the last 15 years in our centre, has led us to envision a future emphasis of medical imaging in Precision Imaging. Precision Imaging is not a new discipline but rather a distinct emphasis in medical imaging borne at the cross-roads between, and unifying the efforts behind mechanistic and phenomenological model-based imaging. It captures three main directions in the effort to deal with the information deluge in imaging sciences, and thus achieve wisdom from data, information, and knowledge. Precision Imaging is finally characterised by being descriptive, predictive and integrative about the imaged object. This paper provides a brief and personal perspective on how the field has evolved, summarises and formalises our vision of Precision Imaging for Precision Medicine, and highlights some connections with past research and current trends in the field.
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Affiliation(s)
- Alejandro F Frangi
- CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine, Electronic and Electrical Engineering Department, University of Sheffield, Sheffield, UK.
| | - Zeike A Taylor
- CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine, Mechanical Engineering Department, University of Sheffield, Sheffield, UK.
| | - Ali Gooya
- CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine, Electronic and Electrical Engineering Department, University of Sheffield, Sheffield, UK.
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38
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Weese J, Lorenz C. Four challenges in medical image analysis from an industrial perspective. Med Image Anal 2016; 33:44-49. [PMID: 27344939 DOI: 10.1016/j.media.2016.06.023] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Revised: 06/14/2016] [Accepted: 06/15/2016] [Indexed: 12/12/2022]
Abstract
Today's medical imaging systems produce a huge amount of images containing a wealth of information. However, the information is hidden in the data and image analysis algorithms are needed to extract it, to make it readily available for medical decisions and to enable an efficient work flow. Advances in medical image analysis over the past 20 years mean there are now many algorithms and ideas available that allow to address medical image analysis tasks in commercial solutions with sufficient performance in terms of accuracy, reliability and speed. At the same time new challenges have arisen. Firstly, there is a need for more generic image analysis technologies that can be efficiently adapted for a specific clinical task. Secondly, efficient approaches for ground truth generation are needed to match the increasing demands regarding validation and machine learning. Thirdly, algorithms for analyzing heterogeneous image data are needed. Finally, anatomical and organ models play a crucial role in many applications, and algorithms to construct patient-specific models from medical images with a minimum of user interaction are needed. These challenges are complementary to the on-going need for more accurate, more reliable and faster algorithms, and dedicated algorithmic solutions for specific applications.
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Affiliation(s)
- Jürgen Weese
- Philips Research Hamburg, Röntgenstrasse 22 - 24, D-22335 Hamburg, Germany.
| | - Cristian Lorenz
- Philips Research Hamburg, Röntgenstrasse 22 - 24, D-22335 Hamburg, Germany.
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39
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Sudarshan VK, Acharya UR, Ng EYK, Tan RS, Chou SM, Ghista DN. An integrated index for automated detection of infarcted myocardium from cross-sectional echocardiograms using texton-based features (Part 1). Comput Biol Med 2016; 71:231-40. [PMID: 26898671 DOI: 10.1016/j.compbiomed.2016.01.028] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Revised: 01/14/2016] [Accepted: 01/30/2016] [Indexed: 11/15/2022]
Abstract
Cross-sectional view echocardiography is an efficient non-invasive diagnostic tool for characterizing Myocardial Infarction (MI) and stages of expansion leading to heart failure. An automated computer-aided technique of cross-sectional echocardiography feature assessment can aid clinicians in early and more reliable detection of MI patients before subsequent catastrophic post-MI medical conditions. Therefore, this paper proposes a novel Myocardial Infarction Index (MII) to discriminate infarcted and normal myocardium using features extracted from apical cross-sectional views of echocardiograms. The cross-sectional view of normal and MI echocardiography images are represented as textons using Maximum Responses (MR8) filter banks. Fractal Dimension (FD), Higher-Order Statistics (HOS), Hu's moments, Gabor Transform features, Fuzzy Entropy (FEnt), Energy, Local binary Pattern (LBP), Renyi's Entropy (REnt), Shannon's Entropy (ShEnt), and Kapur's Entropy (KEnt) features are extracted from textons. These features are ranked using t-test and fuzzy Max-Relevancy and Min-Redundancy (mRMR) ranking methods. Then, combinations of highly ranked features are used in the formulation and development of an integrated MII. This calculated novel MII is used to accurately and quickly detect infarcted myocardium by using one numerical value. Also, the highly ranked features are subjected to classification using different classifiers for the characterization of normal and MI LV ultrasound images using a minimum number of features. Our current technique is able to characterize MI with an average accuracy of 94.37%, sensitivity of 91.25% and specificity of 97.50% with 8 apical four chambers view features extracted from only single frame per patient making this a more reliable and accurate classification.
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Affiliation(s)
- Vidya K Sudarshan
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore; Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
| | - E Y K Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | - Ru San Tan
- Department of Cardiology, National Heart Centre, Singapore
| | - Siaw Meng Chou
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
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40
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Schultz C, Rodriguez-Olivares R, Bosmans J, Lefèvre T, De Santis G, Bruining N, Collas V, Dezutter T, Bosmans B, Rahhab Z, El Faquir N, Watanabe Y, Segers P, Verhegghe B, Chevalier B, van Mieghem N, De Beule M, Mortier P, de Jaegere P. Patient-specific image-based computer simulation for theprediction of valve morphology and calcium displacement after TAVI with the Medtronic CoreValve and the Edwards SAPIEN valve. EUROINTERVENTION 2016; 11:1044-52. [DOI: 10.4244/eijv11i9a212] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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41
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Morris PD, Narracott A, von Tengg-Kobligk H, Silva Soto DA, Hsiao S, Lungu A, Evans P, Bressloff NW, Lawford PV, Hose DR, Gunn JP. Computational fluid dynamics modelling in cardiovascular medicine. Heart 2015; 102:18-28. [PMID: 26512019 PMCID: PMC4717410 DOI: 10.1136/heartjnl-2015-308044] [Citation(s) in RCA: 222] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Accepted: 09/21/2015] [Indexed: 12/24/2022] Open
Abstract
This paper reviews the methods, benefits and challenges associated with the adoption and translation of computational fluid dynamics (CFD) modelling within cardiovascular medicine. CFD, a specialist area of mathematics and a branch of fluid mechanics, is used routinely in a diverse range of safety-critical engineering systems, which increasingly is being applied to the cardiovascular system. By facilitating rapid, economical, low-risk prototyping, CFD modelling has already revolutionised research and development of devices such as stents, valve prostheses, and ventricular assist devices. Combined with cardiovascular imaging, CFD simulation enables detailed characterisation of complex physiological pressure and flow fields and the computation of metrics which cannot be directly measured, for example, wall shear stress. CFD models are now being translated into clinical tools for physicians to use across the spectrum of coronary, valvular, congenital, myocardial and peripheral vascular diseases. CFD modelling is apposite for minimally-invasive patient assessment. Patient-specific (incorporating data unique to the individual) and multi-scale (combining models of different length- and time-scales) modelling enables individualised risk prediction and virtual treatment planning. This represents a significant departure from traditional dependence upon registry-based, population-averaged data. Model integration is progressively moving towards ‘digital patient’ or ‘virtual physiological human’ representations. When combined with population-scale numerical models, these models have the potential to reduce the cost, time and risk associated with clinical trials. The adoption of CFD modelling signals a new era in cardiovascular medicine. While potentially highly beneficial, a number of academic and commercial groups are addressing the associated methodological, regulatory, education- and service-related challenges.
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Affiliation(s)
- Paul D Morris
- Department of Cardiovascular Science, University of Sheffield, Sheffield, UK Insigneo Institute for In Silico Medicine, Sheffield, UK Department of Cardiology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK
| | - Andrew Narracott
- Department of Cardiovascular Science, University of Sheffield, Sheffield, UK Insigneo Institute for In Silico Medicine, Sheffield, UK
| | - Hendrik von Tengg-Kobligk
- University Institute for Diagnostic, Interventional and Pediatric Radiology, University Hospital of Bern, Inselspital, Bern, Switzerland
| | - Daniel Alejandro Silva Soto
- Department of Cardiovascular Science, University of Sheffield, Sheffield, UK Insigneo Institute for In Silico Medicine, Sheffield, UK
| | - Sarah Hsiao
- Department of Cardiovascular Science, University of Sheffield, Sheffield, UK
| | - Angela Lungu
- Department of Cardiovascular Science, University of Sheffield, Sheffield, UK Insigneo Institute for In Silico Medicine, Sheffield, UK
| | - Paul Evans
- Department of Cardiovascular Science, University of Sheffield, Sheffield, UK Insigneo Institute for In Silico Medicine, Sheffield, UK
| | - Neil W Bressloff
- Faculty of Engineering & the Environment, University of Southampton, Southampton, UK
| | - Patricia V Lawford
- Department of Cardiovascular Science, University of Sheffield, Sheffield, UK Insigneo Institute for In Silico Medicine, Sheffield, UK
| | - D Rodney Hose
- Department of Cardiovascular Science, University of Sheffield, Sheffield, UK Insigneo Institute for In Silico Medicine, Sheffield, UK
| | - Julian P Gunn
- Department of Cardiovascular Science, University of Sheffield, Sheffield, UK Insigneo Institute for In Silico Medicine, Sheffield, UK Department of Cardiology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK
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42
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Clinical Diagnostic Biomarkers from the Personalization of Computational Models of Cardiac Physiology. Ann Biomed Eng 2015; 44:46-57. [PMID: 26399986 DOI: 10.1007/s10439-015-1439-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Accepted: 08/25/2015] [Indexed: 10/23/2022]
Abstract
Computational modelling of the heart is rapidly advancing to the point of clinical utility. However, the difficulty of parameterizing and validating models from clinical data indicates that the routine application of truly predictive models remains a significant challenge. We argue there is significant value in an intermediate step towards prediction. This step is the use of biophysically based models to extract clinically useful information from existing patient data. Specifically in this paper we review methodologies for applying modelling frameworks for this goal in the areas of quantifying cardiac anatomy, estimating myocardial stiffness and optimizing measurements of coronary perfusion. Using these indicative examples of the general overarching approach, we finally discuss the value, ongoing challenges and future potential for applying biophysically based modelling in the clinical context.
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43
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Computer-aided diagnosis of Myocardial Infarction using ultrasound images with DWT, GLCM and HOS methods: A comparative study. Comput Biol Med 2015; 62:86-93. [DOI: 10.1016/j.compbiomed.2015.03.033] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Revised: 03/19/2015] [Accepted: 03/31/2015] [Indexed: 11/23/2022]
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44
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McKeever S, Johnson D. The role of markup for enabling interoperability in health informatics. Front Physiol 2015; 6:152. [PMID: 26042043 PMCID: PMC4434901 DOI: 10.3389/fphys.2015.00152] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Accepted: 04/27/2015] [Indexed: 11/13/2022] Open
Abstract
Interoperability is the faculty of making information systems work together. In this paper we will distinguish a number of different forms that interoperability can take and show how they are realized on a variety of physiological and health care use cases. The last 15 years has seen the rise of very cheap digital storage both on and off site. With the advent of the Internet of Things people's expectations are for greater interconnectivity and seamless interoperability. The potential impact these technologies have on healthcare are dramatic: from improved diagnoses through immediate access to a patient's electronic health record, to in silico modeling of organs and early stage drug trials, to predictive medicine based on top-down modeling of disease progression and treatment. We will begin by looking at the underlying technology, classify the various kinds of interoperability that exist in the field, and discuss how they are realized. We conclude with a discussion on future possibilities that big data and further standardizations will enable.
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Affiliation(s)
- Steve McKeever
- Department of Informatics and Media, Uppsala UniversityUppsala, Sweden
- Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO)Saint Petersburg, Russia
| | - David Johnson
- Data Science Institute, Imperial College LondonLondon, UK
- Department of Computing, Imperial College LondonLondon, UK
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45
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Tøndel K, Land S, Niederer SA, Smith NP. Quantifying inter-species differences in contractile function through biophysical modelling. J Physiol 2015; 593:1083-111. [PMID: 25480801 DOI: 10.1113/jphysiol.2014.279232] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Accepted: 11/28/2014] [Indexed: 11/08/2022] Open
Abstract
Animal models and measurements are frequently used to guide and evaluate clinical interventions. In this context, knowledge of inter-species differences in physiology is crucial for understanding the limitations and relevance of animal experimental assays for informing clinical applications. Extensive effort has been put into studying the structure and function of cardiac contractile proteins and how differences in these translate into the functional properties of muscles. However, integrating this knowledge into a quantitative description, formalising and highlighting inter-species differences both in the kinetics and in the regulation of physiological mechanisms, remains challenging. In this study we propose and apply a novel approach for the quantification of inter-species differences between mouse, rat and human. Assuming conservation of the fundamental physiological mechanisms underpinning contraction, biophysically based computational models are fitted to simulate experimentally recorded phenotypes from multiple species. The phenotypic differences between species are then succinctly quantified as differences in the biophysical model parameter values. This provides the potential of quantitatively establishing the human relevance of both animal-based experimental and computational models for application in a clinical context. Our results indicate that the parameters related to the sensitivity and cooperativity of calcium binding to troponin C and the activation and relaxation rates of tropomyosin/crossbridge binding kinetics differ most significantly between mouse, rat and human, while for example the reference tension, as expected, shows only minor differences between the species. Hence, while confirming expected inter-species differences in calcium sensitivity due to large differences in the observed calcium transients, our results also indicate more unexpected differences in the cooperativity mechanism. Specifically, the decrease in the unbinding rate of calcium to troponin C with increasing active tension was much lower for mouse than for rat and human. Our results also predicted crossbridge binding to be slowest in human and fastest in mouse.
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Affiliation(s)
- Kristin Tøndel
- Department of Biomedical Engineering, King's College London, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK; Simula Research Laboratory, Martin Linges v. 17/25, Rolfsbukta 4B, Fornebu, 1364, Norway
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46
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Collin A, Chapelle D, Moireau P. Sequential State Estimation for Electrophysiology Models with Front Level-Set Data Using Topological Gradient Derivations. FUNCTIONAL IMAGING AND MODELING OF THE HEART 2015. [DOI: 10.1007/978-3-319-20309-6_46] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
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47
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Suinesiaputra A, Medrano-Gracia P, Cowan BR, Young AA. Big heart data: advancing health informatics through data sharing in cardiovascular imaging. IEEE J Biomed Health Inform 2014; 19:1283-90. [PMID: 25415993 DOI: 10.1109/jbhi.2014.2370952] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The burden of heart disease is rapidly worsening due to the increasing prevalence of obesity and diabetes. Data sharing and open database resources for heart health informatics are important for advancing our understanding of cardiovascular function, disease progression and therapeutics. Data sharing enables valuable information, often obtained at considerable expense and effort, to be reused beyond the specific objectives of the original study. Many government funding agencies and journal publishers are requiring data reuse, and are providing mechanisms for data curation and archival. Tools and infrastructure are available to archive anonymous data from a wide range of studies, from descriptive epidemiological data to gigabytes of imaging data. Meta-analyses can be performed to combine raw data from disparate studies to obtain unique comparisons or to enhance statistical power. Open benchmark datasets are invaluable for validating data analysis algorithms and objectively comparing results. This review provides a rationale for increased data sharing and surveys recent progress in the cardiovascular domain. We also highlight the potential of recent large cardiovascular epidemiological studies enabling collaborative efforts to facilitate data sharing, algorithms benchmarking, disease modeling and statistical atlases.
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48
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Gao H, Wang H, Berry C, Luo X, Griffith BE. Quasi-static image-based immersed boundary-finite element model of left ventricle under diastolic loading. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2014; 30:1199-222. [PMID: 24799090 PMCID: PMC4233956 DOI: 10.1002/cnm.2652] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2013] [Revised: 04/24/2014] [Accepted: 04/25/2014] [Indexed: 05/07/2023]
Abstract
Finite stress and strain analyses of the heart provide insight into the biomechanics of myocardial function and dysfunction. Herein, we describe progress toward dynamic patient-specific models of the left ventricle using an immersed boundary (IB) method with a finite element (FE) structural mechanics model. We use a structure-based hyperelastic strain-energy function to describe the passive mechanics of the ventricular myocardium, a realistic anatomical geometry reconstructed from clinical magnetic resonance images of a healthy human heart, and a rule-based fiber architecture. Numerical predictions of this IB/FE model are compared with results obtained by a commercial FE solver. We demonstrate that the IB/FE model yields results that are in good agreement with those of the conventional FE model under diastolic loading conditions, and the predictions of the LV model using either numerical method are shown to be consistent with previous computational and experimental data. These results are among the first to analyze the stress and strain predictions of IB models of ventricular mechanics, and they serve both to verify the IB/FE simulation framework and to validate the IB/FE model. Moreover, this work represents an important step toward using such models for fully dynamic fluid-structure interaction simulations of the heart. © 2014 The Authors. International Journal for Numerical Methods in Engineering published by John Wiley & Sons, Ltd.
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Affiliation(s)
- Hao Gao
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
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49
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de Vecchi A, Gomez A, Pushparajah K, Schaeffter T, Nordsletten DA, Simpson JM, Penney GP, Smith NP. Towards a fast and efficient approach for modelling the patient-specific ventricular haemodynamics. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 116:3-10. [PMID: 25157924 DOI: 10.1016/j.pbiomolbio.2014.08.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Accepted: 08/12/2014] [Indexed: 11/17/2022]
Abstract
Computer modelling of the heart has emerged over the past decade as a powerful technique to explore the cardiovascular pathophysiology and inform clinical diagnosis. The current state-of-the-art in biophysical modelling requires a wealth of, potentially invasive, clinical data for the parametrisation and validation of the models, a process that is still too long and complex to be compatible with the clinical decision-making time. Therefore, there remains a need for models that can be quickly customised to reconstruct physical processes difficult to measure directly in patients. In this paper, we propose a less resource-intensive approach to modelling, whereby computational fluid-dynamics (CFD) models are constrained exclusively by boundary motion derived from imaging data through a validated wall tracking algorithm. These models are generated and parametrised based solely on ultrasound data, whose acquisition is fast, inexpensive and routine in all patients. To maximise the time and computational efficiency, a semi-automated pipeline is embedded in an image processing workflow to personalise the models. Applying this approach to two patient cases, we demonstrate this tool can be directly used in the clinic to interpret and complement the available clinical data by providing a quantitative indication of clinical markers that cannot be easily derived from imaging, such as pressure gradients and the flow energy.
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Affiliation(s)
- A de Vecchi
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London SE1 7EH, UK
| | - A Gomez
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London SE1 7EH, UK
| | - K Pushparajah
- Evelina London Children's Hospital, London SE1 7EH, UK
| | - T Schaeffter
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London SE1 7EH, UK
| | - D A Nordsletten
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London SE1 7EH, UK
| | - J M Simpson
- Evelina London Children's Hospital, London SE1 7EH, UK
| | - G P Penney
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London SE1 7EH, UK
| | - N P Smith
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London SE1 7EH, UK.
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50
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Romero L, Vela JM. Alternative Models in Drug Discovery and Development Part I:In SilicoandIn VitroModels. ACTA ACUST UNITED AC 2014. [DOI: 10.1002/9783527679348.ch02] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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