The effect of stretching on transmural shear wave anisotropy in cardiac shear wave elastography

Caenen, Annette; Thabit, Abdullah; Shcherbakova, Darya; Swillens, Abigaïl; Segers, Patrick; Pernot, Mathieu; Mertens, Luc

doi:10.1109/ultsym.2017.8092507

Cited by 3 publications

(7 citation statements)

References 13 publications

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“…The geometry of the FEM-model is the same geometry used previously in our experimental set-up in Caenen et al (2017c), i.e. a rectangular section of left ventricular tissue, obtained from the heart by first removing the right ventricle and cutting the left ventricle from base to apex, along the interventricular septum (see left panel of figure 1).…”

Section: Geometry and Set-upmentioning

confidence: 99%

“…The resulting dimensions of the tissue slab in between the clamps are 60.0 mm × 20.0 mm × 64.6 mm. Similar to the SWE experiments in Caenen et al (2017c), three stretch ratios, i.e. 5%, 10% and 15% with respect to the original length of the slab, were considered.…”

Section: Geometry and Set-upmentioning

confidence: 99%

“…with maximal amplitude A, coordinates of the focal point (x 0 , y 0 , z 0 ), and Gaussian width in lateral, elevational and axial direction, σ x , σ y and σ z respectively. The focus of the ARF was taken the same as in the experiment in Caenen et al (2017c), i.e. (0 mm, 0 mm, 30 mm), corresponding to the center of the myocardial slab (axial depth thus varied from 20 mm to 40 mm for the non-stretched myocardial slab; with stretching, the thickness of the slab decreases).…”

Section: Numerical Implementation: Uniaxial Stretching In Combination...mentioning

confidence: 99%

“…The velocity data resulting from modeling step 3 (the virtual SWE acquisition on the local model) were extracted at a sampling rate of 25 kHz during the complete SWE-simulation of 4 ms. For visualization and post-processing purposes, subsequent interpolation of the extracted data was necessary from the mesh geometry defined in Abaqus to a newly defined equidistant rectangular grid with sampling 0.2 mm in all three directions and a polar grid with similar sampling in the radial and axial direction and an angular sampling of ∆θ = 5° (two times smaller than the rotation angle of the linear probe in the experiments (Caenen et al 2017c)). Interpolation was performed using Matlab 2016b (The MathWorks Inc., Natick, MA, USA).…”

Section: Post-processing Of Shear Wave Elastography Data 241 Data Ext...mentioning

confidence: 99%

“…This work focuses on SWE using impulsive ARF methods. The validity of the cardiac SWE simulations is assessed by comparing the resulting shear wave group speed features to those of our earlier ex vivo cardiac experiments reported in Caenen et al (2017c).…”

Section: Introductionmentioning

confidence: 99%

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Anin silicoframework to analyze the anisotropic shear wave mechanics in cardiac shear wave elastography

et al. 2018

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Shear wave elastography (SWE) is a potential tool to non-invasively assess cardiac muscle stiffness. This study focused on the effect of the orthotropic material properties and mechanical loading on the performance of cardiac SWE, as it is known that these factors contribute to complex 3D anisotropic shear wave propagation. To investigate the specific impact of these complexities, we constructed a finite element model with an orthotropic material law subjected to different uniaxial stretches to simulate SWE in the stressed cardiac wall. Group and phase speed were analyzed in function of tissue thickness and virtual probe rotation angle. Tissue stretching increased the group and phase speed of the simulated shear wave, especially in the direction of the muscle fiber. As the model provided access to the true fiber orientation and material properties, we assessed the accuracy of two fiber orientation extraction methods based on SWE. We found a higher accuracy (but lower robustness) when extracting fiber orientations based on the location of maximal shear wave speed instead of the angle of the major axis of the ellipsoidal group speed surface. Both methods had a comparable performance for the center region of the cardiac wall, and performed less well towards the edges. Lastly, we also assessed the (theoretical) impact of pathology on shear wave physics and characterization in the model. It was found that SWE was able to detect changes in fiber orientation and material characteristics, potentially associated with cardiac pathologies such as myocardial fibrosis. Furthermore, the model showed clearly altered shear wave patterns for the fibrotic myocardium compared to the healthy myocardium, which forms an initial but promising outcome of this modeling study.

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Section: Geometry and Set-upmentioning

confidence: 99%

Section: Geometry and Set-upmentioning

confidence: 99%

Section: Numerical Implementation: Uniaxial Stretching In Combination...mentioning

confidence: 99%

Section: Post-processing Of Shear Wave Elastography Data 241 Data Ext...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Anin silicoframework to analyze the anisotropic shear wave mechanics in cardiac shear wave elastography

et al. 2018

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Recent Advances and Trends in Pediatric Cardiac Imaging

Mawad

Mertens

2018

Curr Treat Options Cardio Med

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Cardiac imaging is central to today's pediatric cardiology practice not only to diagnose structural congenital defects and delineate cardiac and extracardiac anatomy but also for determining the hemodynamic impact of the structural defects and acquired pediatric diseases. Not so long ago, clinicians had to heavily rely on angiography as the main cardiac imaging modality to visualize the heart. Particularly, the development of echocardiography in the 1970s and 1980s together with the development of magnetic resonance imaging (MRI) and computed tomography (CT) resulted in a non-invasive diagnostic revolution with diagnostic catheterization becoming obsolete apart for very specific indications. The continuous improvements in non-invasive imaging modalities allow an unprecedented level of understanding of cardiac morphology and function. Over the last few years, the specific roles of the three imaging modalities and their complementary roles in diagnosis and treatment have become well established resulting in a multimodality approach to specific congenital lesions. Recently, multimodality guidelines were published for postoperative tetralogy of Fallot and patients with transposition of the great arteries (Cohen et al. J Am Soc Echocardiogr. 2016;29(7):571-621, Valente et al. J Am Soc Echocardiogr. 2014;27(2):111-41). In this paper, we aim to highlight some of the most significant advances and highlight some emerging trends in pediatric cardiac imaging.

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Transmural Wave Speed Gradient May Distinguish Intrinsic Myocardial Stiffening From Preload-Induced Changes in Operational Stiffness in Shear Wave Elastography

Caenen

Bezy²,

Petrescu³

et al. 2023

IEEE Trans. Biomed. Eng.

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Background: Shear wave elastography (SWE) 7 is a promising technique to non-invasively assess myocar-8 dial stiffness based on the propagation speed of mechan-9 ical waves. However, a high wave propagation speed can 10 either be attributed to an elevated intrinsic myocardial stiff-11 ness or to a preload-induced increase in operational stiff-12 ness. Objective: Our objective was to find a way to discrim-13 inate intrinsic myocardial stiffening from stiffening caused 14 by an increased pressure in SWE. Methods: We used the 15 finite element method to study the shear wave propagation 16 patterns when stiffness and/or pressure is elevated, com-17 pared to normal stiffness and pressure. Numerical findings 18 were verified in a few human subjects. Results: The trans-19 mural wave speed gradient was able to distinguish changes 20 in intrinsic stiffness from those induced by differing hemo-21 dynamic load (a speed of ±3.2 m/s in parasternal short-axis 22 (PSAX) view was associated with a wave speed gradient of 23 −0.17 ± 0.15 m/s/mm when pressure was elevated com-24 pared to 0.04 ± 0.05 m/s/mm when stiffness was elevated). 25 The gradient however decreased when stiffness increased 26 (decrease with a factor 3 in PSAX when stiffness doubled 27 at 20 mmHg). The human data analysis confirmed the pres-28 ence of a wave speed gradient in a patient with elevated 29 ventricular pressure. Conclusion: Cardiac SWE modeling 30 is a useful tool to gain additional insights into the com-31 plex wave physics and to guide post-processing. The trans-32 mural differences in wave speed may help to distinguish 33 Manuscript

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The effect of stretching on transmural shear wave anisotropy in cardiac shear wave elastography

Cited by 3 publications

References 13 publications

Anin silicoframework to analyze the anisotropic shear wave mechanics in cardiac shear wave elastography

Anin silicoframework to analyze the anisotropic shear wave mechanics in cardiac shear wave elastography

Recent Advances and Trends in Pediatric Cardiac Imaging

Transmural Wave Speed Gradient May Distinguish Intrinsic Myocardial Stiffening From Preload-Induced Changes in Operational Stiffness in Shear Wave Elastography

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