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  • Open Access

In-vivo waveguide cardiac magnetic resonance elastography

  • 1, 4,
  • 1,
  • 3, 4,
  • 2 and
  • 3, 4
Journal of Cardiovascular Magnetic Resonance201517 (Suppl 1) :P35

  • Published:


  • Diffusion Tensor Imaging
  • Heart Failure With Preserve Ejection Fraction
  • Preserve Ejection Fraction
  • Diffusion Tensor Imaging Data
  • Diffusion Tensor Imaging Parameter


Myocardial stiffness (MS) is elevated in heart failure with preserved ejection fraction(HFPEF)[1]. In addition, stiffness elevation in HFPEF exhibits directional dependency[2]. Conventional determinants of MS such as pressure-volume relationship and mechanical testing are invasive and hence clinically inefficient. Therefore, there is a need to non-invasively estimate anisotropic MS to assist in diagnosis and prognosis of HFPEF. In this study we implement waveguide cardiac magnetic resonance elastography (CMRE)[3] to demonstrate the feasibility of estimating anisotropic MS non-invasively in an in-vivo porcine model.


Waveguide CMRE involves performing diffusion tensor imaging (DTI) in conjunction with conventional CMRE. In-vivo CMRE was performed on a pig in a 1.5T MRI scanner. CMRE imaging parameters: TE/TR=9.7/21.4; flip angle 25; mechanical frequency 80 Hz; encoding frequency 160 Hz. Post CMRE acquisition the heart was arrested in diastole using potassium chloride and in-situ cardiac DTI was performed. Cardiac DTI parameters: TE/TR=80/3200; flip angle 90; b-value=0/1000 s/mm2; number of directions=12; number of averages=10. CMRE and DTI was performed at the same resolution and the parameters were FOV=320mm3; imaging matrix 128x128; slice thickness=2.5mm; DTI was registered with CMRE to exactly match the voxel information from both sets of acquisition. Then both CMRE and DTI were masked to extract the left ventricle. Masked images were processed to estimate i) principle eigenvectors from DTI data sets; ii) and first harmonic displacements from CMRE wave data. Next, a spatial spectral filter was applied on the first harmonic displacement data to isolate waves traveling in particular directions defined by the principle eigenvector. Simultaneously, Helmholtz decomposition was performed to separate the filtered displacements into its longitudinal and transverse components. An orthotropic inversion [3] was performed to calculate compressional (C11,C22,C33) and shear (C44,C55,C66) stiffness coefficients.


Figure 1 shows stiffness maps for end-systole and end-diastole. The mean and SD of the compressional and shear stiffness coefficients is listed in Table 1. We have observed that compressional stiffness is higher than shear stiffness. In addition both compressional and shear stiffness coefficients are higher in end-systole as compared to end-diastole.
Figure 1
Figure 1

Anisotropic stiffness maps for end-systole (top row) and end-diastole (bottom row).

Table 1

Anisotropic Stiffness Measurements


Compressional Stiffness Coefficients

Shear Stiffness Coefficients









13.97 ± 0.38

8.68 ± 0.19

27.34 ± 1.02

6.57 ± 1.23

6.44 ± 0.62

6.45 ± 0.68


5.49 ± 0.07

7.02 ± 0.22

20.00 ± 0.54

5.52 ± 0.31

5.56 ± 0.39

5.53 ± 0.31


We have demonstrated the feasibility of estimating in-vivo anisotropic stiffness using CMRE. However further validation and application in a diseased model is required.


This study has been supported by AHA 13SDG14690027.

Authors’ Affiliations

Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA
Naval Research Laboratory, Washington, DC, USA
Department of Internal Medicine-Division of Cardiology, The Ohio State University, Columbus, OH, USA
Department of Radiology, The Ohio State University, Columbus, OH, USA


  1. Mandinov L, et al: Cardiovascular research. 2000, 813-25.Google Scholar
  2. Baldwin SL, et al: IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2007, 1360-9.Google Scholar
  3. Romano A, et al: Proceedings of the 21st Annual Meeting of ISMRM. 2013, Utah, USA, 2431-Google Scholar


© Mazumder et al; licensee BioMed Central Ltd. 2015

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