In Vivo Quantification of Regional Circumferential Green Strain in the Thoracic and Abdominal Aorta by Two-Dimensional Spiral Cine DENSE MRI

Abstract

Regional tissue mechanics play a fundamental role in the patient-specific function and remodeling of the cardiovascular system. Nevertheless, regional in vivo assessments of aortic kinematics remain lacking due to the challenge of imaging the thin aortic wall. Herein, we present a novel application of displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI) to quantify the regional displacement and circumferential Green strain of the thoracic and abdominal aorta. Two-dimensional (2D) spiral cine DENSE and steady-state free procession (SSFP) cine images were acquired at 3T at either the infrarenal abdominal aorta (IAA), descending thoracic aorta (DTA), or distal aortic arch (DAA) in a pilot study of six healthy volunteers (22–59 y.o., 4 females). DENSE data were processed with multiple custom noise reduction techniques including time-smoothing, displacement vector smoothing, sectorized spatial smoothing, and reference point averaging to calculate circumferential Green strain across 16 equispaced sectors around the aorta. Each volunteer was scanned twice to evaluate interstudy repeatability. Circumferential Green strain was heterogeneously distributed in all volunteers and locations. The mean spatial heterogeneity index (standard deviation of all sector values divided by the mean strain) was 0.37 in the IAA, 0.28 in the DTA, and 0.59 in the DAA. Mean (homogenized) peak strain by DENSE for each cross section was consistent with the homogenized linearized strain estimated from SSFP cine. The mean difference in peak strain across all sectors following repeat imaging was −0.1±2.3%, with a mean absolute difference of 1.7%. Aortic cine DENSE MRI is a viable noninvasive technique for quantifying heterogeneous regional aortic wall strain and has significant potential to improve patient-specific clinical assessments of numerous aortopathies, as well as to provide the lacking spatiotemporal data required to refine patient-specific computational models of aortic growth and remodeling.