Assessing exercise cardiac reserve using real-time cardiovascular magnetic resonance

Abstract

Background Exercise cardiovascular magnetic resonance (ExCMR) has great potential for clinical use but its development has been limited by a lack of compatible equipment and robust real-time imaging techniques. We developed an exCMR protocol using an in-scanner cycle ergometer and assessed its performance in differentiating athletes from non-athletes. Methods Free-breathing real-time CMR (1.5T Aera, Siemens) was performed in 11 athletes (5 males; median age 29 [IQR: 28–39] years) and 16 age- and sex-matched healthy volunteers (7 males; median age 26 [interquartile range (IQR): 25–33] years). All participants underwent an in-scanner exercise protocol on a CMR compatible cycle ergometer (Lode BV, the Netherlands), with an initial workload of 25W followed by 25W-increment every minute. In 20 individuals, exercise capacity was also evaluated by cardiopulmonary exercise test (CPET). Scan-rescan reproducibility was assessed in 10 individuals, at least 7 days apart. Results The exCMR protocol demonstrated excellent scan-rescan (cardiac index (CI): 0.2 ± 0.5L/min/m2) and inter-observer (ventricular volumes: 1.2 ± 5.3mL) reproducibility. CI derived from exCMR and CPET had excellent correlation (r = 0.83, p < 0.001) and agreement (1.7 ± 1.8L/min/m2). Despite similar values at rest (P = 0.87), athletes had increased exercise CI compared to healthy individuals (at peak exercise: 12.2 [IQR: 10.2–13.5] L/min/m2 versus 8.9 [IQR: 7.5–10.1] L/min/m2, respectively; P < 0.001). Peak exercise CI, where image acquisition lasted 13–17 s, outperformed that at rest (c-statistics = 0.95 [95% confidence interval: 0.87–1.00] versus 0.48 [95% confidence interval: 0.23–0.72], respectively; P < 0.0001 for comparison) in differentiating athletes from healthy volunteers; and had similar performance as VO2max (c-statistics = 0.84 [95% confidence interval = 0.62–1.00]; P = 0.29 for comparison). Conclusions We have developed a novel in-scanner exCMR protocol using real-time CMR that is highly reproducible. It may now be developed for clinical use for physiological studies of the heart and circulation.