RESUMEN
The quantification of cardiac strains as structural indices of cardiac function has a growing prevalence in clinical diagnosis. However, the highly heterogeneous four-dimensional (4D) cardiac motion challenges accurate "regional" strain quantification and leads to sizable differences in the estimated strains depending on the imaging modality and post-processing algorithm, limiting the translational potential of strains as incremental biomarkers of cardiac dysfunction. There remains a crucial need for a feasible benchmark that successfully replicates complex 4D cardiac kinematics to determine the reliability of strain calculation algorithms. In this study, we propose an in-silico heart phantom derived from finite element (FE) simulations to validate the quantification of 4D regional strains. First, as a proof-of-concept exercise, we created synthetic magnetic resonance (MR) images for a hollow thick-walled cylinder under pure torsion with an exact solution and demonstrated that "ground-truth" values can be recovered for the twist angle, which is also a key kinematic index in the heart. Next, we used mouse-specific FE simulations of cardiac kinematics to synthesize dynamic MR images by sampling various sectional planes of the left ventricle (LV). Strains were calculated using our recently developed non-rigid image registration (NRIR) framework in both problems. Moreover, we studied the effects of image quality on distorting regional strain calculations by conducting in-silico experiments for various LV configurations. Our studies offer a rigorous and feasible tool to standardize regional strain calculations to improve their clinical impact as incremental biomarkers.
RESUMEN
The quantification of cardiac motion using cardiac magnetic resonance imaging (CMR) has shown promise as an early-stage marker for cardiovascular diseases. Despite the growing popularity of CMR-based myocardial strain calculations, measures of complete spatiotemporal strains (i.e., three-dimensional strains over the cardiac cycle) remain elusive. Complete spatiotemporal strain calculations are primarily hampered by poor spatial resolution, with the rapid motion of the cardiac wall also challenging the reproducibility of such strains. We hypothesize that a super-resolution reconstruction (SRR) framework that leverages combined image acquisitions at multiple orientations will enhance the reproducibility of complete spatiotemporal strain estimation. Two sets of CMR acquisitions were obtained for five wild-type mice, combining short-axis scans with radial and orthogonal long-axis scans. Super-resolution reconstruction, integrated with tissue classification, was performed to generate full four-dimensional (4D) images. The resulting enhanced and full 4D images enabled complete quantification of the motion in terms of 4D myocardial strains. Additionally, the effects of SRR in improving accurate strain measurements were evaluated using an in-silico heart phantom. The SRR framework revealed near isotropic spatial resolution, high structural similarity, and minimal loss of contrast, which led to overall improvements in strain accuracy. In essence, a comprehensive methodology was generated to quantify complete and reproducible myocardial deformation, aiding in the much-needed standardization of complete spatiotemporal strain calculations.
