Feature-tracking-based strain analysis – a comparison of tracking algorithms

PURPOSE: Optical flow feature-tracking (FT) strain assessment is increasingly being employed scientifically and clinically. Several software packages, employing different algorithms, enable computation of FT-derived strains. The aim of this study is to investigate the impact of the underlying algorithm on the validity and robustness of FT-derived strain results. MATERIAL AND METHODS: CSPAMM and SSFP cine sequences were acquired in 30 subjects (15 patients with aortic stenosis and associated secondary hypertrophic cardiomyopathy, and 15 controls) in identical midventricular short-axis locations. Global peak systolic circumferential strain (PSCS) was calculated using tagging and feature-tracking software with different algorithms (non-rigid, elastic image registration, and blood myocardial border tracing). Intermodality agreement and intra- as well inter-observer variability were assessed. RESULTS: Intermodality/inter-algorithm comparison for global PSCS using Friedman’s test revealed statistically significant differences (tagging vs. blood myocardial border tracing algorithm). Intermodality assessment revealed the highest correlation between tagging and non-rigid, elastic image registration (r = 0.84), while correlation between tagging and blood myocardial border tracing (r = 0.36) and between the two feature-tracking software packages (r = 0.5) were considerably lower. CONCLUSIONS: The type of algorithm employed during feature-tracking strain assessment has a significant impact on the results. The non-rigid, elastic image registration algorithm produces more precise and reproducible results than the blood myocardium tracing algorithm.