Multi-parametric MRI as an indirect evaluation tool of the mechanical properties of in-vitro cardiac tissues
1 École Polytechnique, Mechanical Engineering, Montréal, QC, Canada
2 Research center, CHU Sainte-Justine, Université de Montréal, Montréal, QC, Canada
3 Université de Montréal, Kinesiology, Montréal, QC, Canada
BMC Cardiovascular Disorders 2013, 13:24 doi:10.1186/1471-2261-13-24Published: 27 March 2013
Early detection of heart failure is essential to effectively reduce related mortality. The quantification of the mechanical properties of the myocardium, a primordial indicator of the viability of the cardiac tissue, is a key element in patient’s care. Despite an incremental utilization of multi-parametric magnetic resonance imaging (MRI) for cardiac tissue characteristics and function, the link between multi-parametric MRI and the mechanical properties of the heart has not been established. We sought to determine the parametric relationship between the myocardial mechanical properties and the MR parameters. The specific aim was to develop a reproducible evaluative quantitative tool of the mechanical properties of cardiac tissue using multi-parametric MRI associated to principal component analysis.
Samples from porcine hearts were submitted to a multi-parametric MRI acquisition followed by a uniaxial tensile test. Multi linear regressions were performed between dependent (Young’s modulus E) and independent (relaxation times T1, T2 and T2*, magnetization transfer ratio MTR, apparent diffusion coefficient ADC and fractional anisotropy FA) variables. A principal component analysis was used to convert the set of possibly correlated variables into a set of linearly uncorrelated variables.
Values of 46.1±12.7 MPa for E, 729±21 ms for T1, 61±6 ms for T2, 26±7 for T2*, 35±5% for MTRx100, 33.8±4.7 for FAx10-2, and 5.85±0.21 mm2/s for ADCx10-4 were measured. Multi linear regressions showed that only 45% of E can be explained by the MRI parameters. The principal component analysis reduced our seven variables to two principal components with a cumulative variability of 63%, which increased to 80% when considering the third principal component.
The proposed multi-parametric MRI protocol associated to principal component analysis is a promising tool for the evaluation of mechanical properties within the left ventricle in the in vitro porcine model. Our in vitro experiments will now allow us focused in vivo testing on healthy and infracted hearts in order to determine useful quantitative MR-based biomarkers.