Determining Anisotropic Myocardial Stiffness from Magnetic Resonance Elastography: A Simulation Study

Miller, Renee; Jiang, Haodan; Mazumder, Ria; Cowan, Brett R.; Nash, Martyn P.; Kolipaka, Arunark; Young, Alistair A.

doi:10.1007/978-3-319-20309-6_40

Cited by 7 publications

(10 citation statements)

References 10 publications

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“…Realistic methods of extracting and modelling noise vary throughout MRE literature (Papazoglou et al, 2008;McGee et al, 2011). The limitation of the method applied in this research (Miller et al, 2015), is that the standard deviation, and hence noise level, changes with composition and frequency.…”

Section: Discussionmentioning

confidence: 99%

“…The displacement was averaged over a 2 mm slice thickness, centred on the plaque, synonymous to an MRI voxel. Noise was added to the simulated data using the method outlined by Miller et al (2015). Loree et al (1992).…”

Section: Inversion Algorithmmentioning

confidence: 99%

“…It is primarily used as a method of inversion and a selection of studies have used FEA to invert strain images through atherosclerosis (Franquet et al, 2013;Bertoglio et al, 2014). FEA has also been used to create synthetic data sets to assess inversion algorithms (Van Houten et al, 2001;Miller et al, 2015) and explore the sensitivity of technique parameters (Chen et al, 2005). Baldewsing et al (2004) and Doherty et al (2013) assessed the application of ultrasound elastography to atherosclerosis by varying the geometric and mechanical properties within finite element models.…”

Section: Introductionmentioning

confidence: 99%

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The simulation of magnetic resonance elastography through atherosclerosis

Thomas-Seale

Hollis

Klatt

et al. 2016

Journal of Biomechanics

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The clinical diagnosis of atherosclerosis via the measurement of stenosis size is widely acknowledged as an imperfect criterion. The vulnerability of an atherosclerotic plaque to rupture is associated with its mechanical properties. The potential to image these mechanical properties using magnetic resonance elastography (MRE) was investigated through synthetic datasets. An image of the steady state wave propagation, equivalent to the first harmonic, can be extracted directly from finite element analysis. Inversion of this displacement data yields a map of the shear modulus, known as an elastogram. The variation of plaque composition, stenosis size, Gaussian noise, filter thresholds and excitation frequency were explored. A decreasing mean shear modulus with an increasing lipid composition was identified through all stenosis sizes. However the inversion algorithm showed sensitivity to parameter variation leading to artefacts which disrupted both the elastograms and quantitative trends. As noise was increased up to a realistic level, the contrast was maintained between the fully fibrous and lipid plaques but lost between the interim compositions. Although incorporating a Butterworth filter improved the performance of the algorithm, restrictive filter thresholds resulted in a reduction of the sensitivity of the algorithm to composition and noise variation. Increasing the excitation frequency improved the techniques ability to image the magnitude of the shear modulus and identify a contrast between compositions. In conclusion, whilst the technique has the potential to image the shear modulus of atherosclerotic plaques, future research will require the integration of a heterogeneous inversion algorithm.

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Section: Discussionmentioning

confidence: 99%

Section: Inversion Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The simulation of magnetic resonance elastography through atherosclerosis

Thomas-Seale

Hollis

Klatt

et al. 2016

Journal of Biomechanics

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“…Gaussian noise [26] was added to the wave images based on the method outlined by Miller et al (2015) [27]. Noise levels were varied from 0 to 10%.…”

Section: Data Processingmentioning

confidence: 99%

Finite element analysis to investigate variability of MR elastography in the human thigh

Hollis

Barnhill

Perrins

et al. 2017

Magnetic Resonance Imaging

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Purpose: To develop finite element analysis (FEA) of magnetic resonance elastography (MRE) in the human thigh and investigate inter-individual variability of measurement of muscle mechanical properties. Methods: Segmentation was performed on MRI datasets of the human thigh from 5 individuals and FEA models consisting of 12 muscles and surrounding tissue created. The same material properties were applied to each tissue type and a previously developed transient FEA method of simulating MRE using Abaqus was performed at 4 frequencies. Synthetic noise was applied to the simulated data at various levels before inversion was performed using the Elastography Software Pipeline. Maps of material properties were created and visually assessed to determine key features. The coefficient of variation (CoV) was used to assess the variability of measurements in each individual muscle and in the groups of muscles across the subjects. Mean * Corresponding author * * Principal corresponding author Email address: lyamhollis@outlook.com (L. Hollis) Preprint submitted to Magnetic Resonance Imaging June 27, 2017 A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTmeasurements for the set of muscles were ranked in size order and compared with the expected ranking.Results: At noise levels of 2% the CoV in measurements of |G * | ranged from 5.3 to 21.9% and from 7.1 to 36.1% for measurements of φ in the individual muscles. A positive correlation (R 2 value 0.80) was attained when the expected and measured |G * | ranking were compared, whilst a negative correlation (R 2 value 0.43) was found for φ. Conclusions:Created elastograms demonstrated good definition of muscle structure and were robust to noise. Variability of measurements across the 5 subjects was dramatically lower for |G * | than it was for φ. This large variability in φ measurements was attributed to artefacts.

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“…less than 100 microns) perturbations in displacement at a frequency of around 80 Hz. It is possible to apply finite element analysis to MRE data to recover anisotropic material properties of heart tissue (Miller et al, 2015). This method may provide complimentary information to the large deformation finite elasticity analysis methods described above.…”

Section: Physiology and Biomechanicsmentioning

confidence: 99%

Cardiac image modelling: Breadth and depth in heart disease

Suinesiaputra

McCulloch

Nash

et al. 2016

Medical Image Analysis

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With the advent of large-scale imaging studies and big health data, and the corresponding growth in analytics, machine learning and computational image analysis methods, there are now exciting opportunities for deepening our understanding of the mechanisms and characteristics of heart disease. Two emerging fields are computational analysis of cardiac remodelling (shape and motion changes due to disease) and computational analysis of physiology and mechanics to estimate biophysical properties from non-invasive imaging. Many large cohort studies now underway around the world have been specifically designed based on non-invasive imaging technologies in order to gain new information about the development of heart disease from asymptomatic to clinical manifestations. These give an unprecedented breadth to the quantification of population variation and disease development. Also, for the individual patient, it is now possible to determine biophysical properties of myocardial tissue in health and disease by interpreting detailed imaging data using computational modelling. For these population and patient-specific computational modelling methods to develop further, we need open benchmarks for algorithm comparison and validation, open sharing of data and algorithms, and demonstration of clinical efficacy in patient management and care. The combination of population and patient-specific modelling will give new insights into the mechanisms of cardiac disease, in particular the development of heart failure, congenital heart disease, myocardial infarction, contractile dysfunction and diastolic dysfunction.

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Determining Anisotropic Myocardial Stiffness from Magnetic Resonance Elastography: A Simulation Study

Cited by 7 publications

References 10 publications

The simulation of magnetic resonance elastography through atherosclerosis

The simulation of magnetic resonance elastography through atherosclerosis

Finite element analysis to investigate variability of MR elastography in the human thigh

Cardiac image modelling: Breadth and depth in heart disease

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