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Caenen A, Pernot M, Nightingale KR, Voigt JU, Vos HJ, Segers P, D'hooge J. Assessing cardiac stiffness using ultrasound shear wave elastography. Phys Med Biol 2021; 67. [PMID: 34874312 DOI: 10.1088/1361-6560/ac404d] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 12/06/2021] [Indexed: 11/11/2022]
Abstract
Shear wave elastography offers a new dimension to echocardiography: it measures myocardial stiffness. Therefore, it could provide additional insights into the pathophysiology of cardiac diseases affecting myocardial stiffness and potentially improve diagnosis or guide patient treatment. The technique detects fast mechanical waves on the heart wall with high frame rate echography, and converts their propagation velocity into a stiffness value. A proper interpretation of shear wave data is required as the shear wave interacts with the intrinsic, yet dynamically changing geometrical and material characteristics of the heart under pressure. This dramatically alters the wave physics of the propagating wave, demanding adapted processing methods compared to other shear wave elastography applications as breast tumor and liver stiffness staging. Furthermore, several advanced analysis methods have been proposed to extract supplementary material features such as viscosity and anisotropy, potentially offering additional diagnostic value. This review explains the general mechanical concepts underlying cardiac shear wave elastography and provides an overview of the preclinical and clinical studies within the field. We also identify the mechanical and technical challenges ahead to make shear wave elastography a valuable tool for clinical practice.
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Affiliation(s)
- Annette Caenen
- Institute for Biomedical Engineering and Technology, Ghent University, Ghent, BELGIUM
| | - Mathieu Pernot
- INSERM U979 "Physics for medicine", ESPCI Paris, PSL Research University, CNRS UMR 7587, Institut Langevin, Paris, FRANCE
| | - Kathryn R Nightingale
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, UNITED STATES
| | - Jens-Uwe Voigt
- Department of Cardiovascular Sciences, KU Leuven, Leuven, BELGIUM
| | - Hendrik J Vos
- Department of Biomedical Engineering, Erasmus MC, Rotterdam, Zuid-Holland, NETHERLANDS
| | - Patrick Segers
- Institute of Biomedical Engineering and Technology, Universiteit Gent, Gent, BELGIUM
| | - Jan D'hooge
- Department of Cardiovascular Sciences, KU Leuven, Leuven, BELGIUM
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Capilnasiu A, Hadjicharalambous M, Fovargue D, Patel D, Holub O, Bilston L, Screen H, Sinkus R, Nordsletten D. Magnetic resonance elastography in nonlinear viscoelastic materials under load. Biomech Model Mechanobiol 2018; 18:111-135. [PMID: 30151814 PMCID: PMC6373278 DOI: 10.1007/s10237-018-1072-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 08/10/2018] [Indexed: 12/27/2022]
Abstract
Characterisation of soft tissue mechanical properties is a topic of increasing interest in translational and clinical research. Magnetic resonance elastography (MRE) has been used in this context to assess the mechanical properties of tissues in vivo noninvasively. Typically, these analyses rely on linear viscoelastic wave equations to assess material properties from measured wave dynamics. However, deformations that occur in some tissues (e.g. liver during respiration, heart during the cardiac cycle, or external compression during a breast exam) can yield loading bias, complicating the interpretation of tissue stiffness from MRE measurements. In this paper, it is shown how combined knowledge of a material's rheology and loading state can be used to eliminate loading bias and enable interpretation of intrinsic (unloaded) stiffness properties. Equations are derived utilising perturbation theory and Cauchy's equations of motion to demonstrate the impact of loading state on periodic steady-state wave behaviour in nonlinear viscoelastic materials. These equations demonstrate how loading bias yields apparent material stiffening, softening and anisotropy. MRE sensitivity to deformation is demonstrated in an experimental phantom, showing a loading bias of up to twofold. From an unbiased stiffness of [Formula: see text] Pa in unloaded state, the biased stiffness increases to 9767.5 [Formula: see text]1949.9 Pa under a load of [Formula: see text] 34% uniaxial compression. Integrating knowledge of phantom loading and rheology into a novel MRE reconstruction, it is shown that it is possible to characterise intrinsic material characteristics, eliminating the loading bias from MRE data. The framework introduced and demonstrated in phantoms illustrates a pathway that can be translated and applied to MRE in complex deforming tissues. This would contribute to a better assessment of material properties in soft tissues employing elastography.
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Affiliation(s)
- Adela Capilnasiu
- Division of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
| | - Myrianthi Hadjicharalambous
- Division of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,KIOS Research and Innovation Centre of Excellence, University of Cyprus, Nicosia, Cyprus
| | - Daniel Fovargue
- Division of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Dharmesh Patel
- Institute of Bioengineering, Queen Mary University of London, London, UK
| | - Ondrej Holub
- Division of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Lynne Bilston
- Prince of Wales Clinical School, University of New South Wales, Sydney, Australia.,Neuroscience Research Australia, Sydney, Australia
| | - Hazel Screen
- Institute of Bioengineering, Queen Mary University of London, London, UK
| | - Ralph Sinkus
- Division of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Inserm U1148, LVTS, University Paris Diderot, University Paris 13, 75018, Paris, France
| | - David Nordsletten
- Division of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Biomedical Engineering and Cardiac Surgery, University of Michigan, Ann Arbor, USA
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Manduca A, Rossman TL, Lake DS, Glaser KJ, Arani A, Arunachalam SP, Rossman PJ, Trzasko JD, Ehman RL, Dragomir-Daescu D, Araoz PA. Waveguide effects and implications for cardiac magnetic resonance elastography: A finite element study. NMR IN BIOMEDICINE 2018; 31:e3996. [PMID: 30101999 PMCID: PMC6783328 DOI: 10.1002/nbm.3996] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2017] [Revised: 06/07/2018] [Accepted: 06/12/2018] [Indexed: 06/08/2023]
Abstract
Magnetic resonance elastography (MRE) is increasingly being applied to thin or small structures in which wave propagation is dominated by waveguide effects, which can substantially bias stiffness results with common processing approaches. The purpose of this work was to investigate the importance of such biases and artifacts on MRE inversion results in: (i) various idealized 2D and 3D geometries with one or more dimensions that are small relative to the shear wavelength; and (ii) a realistic cardiac geometry. Finite element models were created using simple 2D geometries as well as a simplified and a realistic 3D cardiac geometry, and simulated displacements acquired by MRE from harmonic excitations from 60 to 220 Hz across a range of frequencies. The displacement wave fields were inverted with direct inversion of the Helmholtz equation with and without the application of bandpass filtering and/or the curl operator to the displacement field. In all geometries considered, and at all frequencies considered, strong biases and artifacts were present in inversion results when the curl operator was not applied. Bandpass filtering without the curl was not sufficient to yield accurate recovery. In the 3D geometries, strong biases and artifacts were present in 2D inversions even when the curl was applied, while only 3D inversions with application of the curl yielded accurate recovery of the complex shear modulus. These results establish that taking the curl of the wave field and performing a full 3D inversion are both necessary steps for accurate estimation of the shear modulus both in simple thin-walled or small structures and in a realistic cardiac geometry when using simple inversions that neglect the hydrostatic pressure term. In practice, sufficient wave amplitude, signal-to-noise ratio, and resolution will be required to achieve accurate results.
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Affiliation(s)
- A Manduca
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - T L Rossman
- Division of Engineering, Mayo Clinic, Rochester, MN, USA
| | - D S Lake
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - K J Glaser
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - A Arani
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - P J Rossman
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - J D Trzasko
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - R L Ehman
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - P A Araoz
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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Mangion K, Gao H, Husmeier D, Luo X, Berry C. Advances in computational modelling for personalised medicine after myocardial infarction. Heart 2017; 104:550-557. [PMID: 29127185 DOI: 10.1136/heartjnl-2017-311449] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 10/24/2017] [Accepted: 10/25/2017] [Indexed: 11/04/2022] Open
Abstract
Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners.
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Affiliation(s)
- Kenneth Mangion
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK.,West of Scotland Heart and Lung Centre, Golden Jubilee National Hospital, Clydebank, UK
| | - Hao Gao
- Department of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Dirk Husmeier
- Department of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Xiaoyu Luo
- Department of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Colin Berry
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK.,West of Scotland Heart and Lung Centre, Golden Jubilee National Hospital, Clydebank, UK
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Liu Y, Royston TJ, Klatt D, Lewandowski ED. Cardiac MR elastography of the mouse: Initial results. Magn Reson Med 2016; 76:1879-1886. [PMID: 26749052 DOI: 10.1002/mrm.26030] [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: 04/13/2015] [Revised: 10/09/2015] [Accepted: 10/10/2015] [Indexed: 12/22/2022]
Abstract
PURPOSE Many cardiovascular diseases are associated with abnormal function of myocardial contractility or dilatability, which is related to elasticity changes of the myocardium over the cardiac cycle. The mouse is a common animal model in studies of the progression of various cardiomyopathies. We introduce a novel noninvasive approach using microscopic scale MR elastography (MRE) to measure the myocardium stiffness change during the cardiac cycle on a mouse model. METHODS A harmonic mechanical wave of 400 Hz was introduced into the mouse body. An electrocardiograph-gated and respiratory-gated fractional encoding cine-MRE pulse sequence was applied to encode the resulting oscillatory motion on a short-axis slice of the heart. Five healthy mice (age range, 3-13.5 mo) were examined. The weighted summation effective stiffness of the left ventricle wall during the cardiac cycle was estimated. RESULTS The ratio of stiffness at end diastole and end systole was 0.5-0.67. Additionally, variation in shear wave amplitude in the left ventricle wall throughout the cardiac cycle was measured and found to correlate with estimates of stiffness variation. CONCLUSION This study demonstrates the feasibility of implementing cardiac MRE on a mouse model. Magn Reson Med 76:1879-1886, 2016. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Yifei Liu
- Department of Mechanical & Industrial Engineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Thomas J Royston
- Department of Mechanical & Industrial Engineering, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Dieter Klatt
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - E Douglas Lewandowski
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA.,Center for Cardiovascular Research, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Physiology and Biophysics, University of Illinois at Chicago, Chicago, Illinois, USA
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Images as drivers of progress in cardiac computational modelling. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 115:198-212. [PMID: 25117497 PMCID: PMC4210662 DOI: 10.1016/j.pbiomolbio.2014.08.005] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2014] [Accepted: 08/02/2014] [Indexed: 11/28/2022]
Abstract
Computational models have become a fundamental tool in cardiac research. Models are evolving to cover multiple scales and physical mechanisms. They are moving towards mechanistic descriptions of personalised structure and function, including effects of natural variability. These developments are underpinned to a large extent by advances in imaging technologies. This article reviews how novel imaging technologies, or the innovative use and extension of established ones, integrate with computational models and drive novel insights into cardiac biophysics. In terms of structural characterization, we discuss how imaging is allowing a wide range of scales to be considered, from cellular levels to whole organs. We analyse how the evolution from structural to functional imaging is opening new avenues for computational models, and in this respect we review methods for measurement of electrical activity, mechanics and flow. Finally, we consider ways in which combined imaging and modelling research is likely to continue advancing cardiac research, and identify some of the main challenges that remain to be solved.
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