Abstract
Pre-clinical animal models are important to study the fundamental biological and functional mechanisms involved in the longitudinal evolution of heart failure (HF). Particularly, large animal models, like nonhuman primates (NHPs), that possess greater physiological, biochemical, and phylogenetic similarity to humans are gaining interest. To assess the translatability of these models into human diseases, imaging biomarkers play a significant role in non-invasive phenotyping, prediction of downstream remodeling, and evaluation of novel experimental therapeutics. This paper sheds insight into NHP cardiac function through the quantification of magnetic resonance (MR) imaging biomarkers that comprehensively characterize the spatiotemporal dynamics of left ventricular (LV) systolic pumping and LV diastolic relaxation. MR tagging and phase contrast (PC) imaging were used to quantify NHP cardiac strain and flow. Temporal inter-relationships between rotational mechanics, myocardial strain and LV chamber flow are presented, and functional biomarkers are evaluated through test-retest repeatability and inter subject variability analyses. The temporal trends observed in strain and flow was similar to published data in humans. Our results indicate a dominant dimension based pumping during early systole, followed by a torsion dominant pumping action during late systole. Early diastole is characterized by close to 65% of untwist, the remainder of which likely contributes to efficient filling during atrial kick. Our data reveal that moderate to good intra-subject repeatability was observed for peak strain, strain-rates, E/circumferential strain-rate (CSR) ratio, E/longitudinal strain-rate (LSR) ratio, and deceleration time. The inter-subject variability was high for strain dyssynchrony, diastolic strain-rates, peak torsion and peak untwist rate. We have successfully characterized cardiac function in NHPs using MR imaging. Peak strain, average systolic strain-rate, diastolic E/CSR and E/LSR ratios, and deceleration time were identified as robust biomarkers that could potentially be applied to future pre-clinical drug studies.