What is the clinical evidence from this study?

Study design: Observational. Population: Known or suspected cardiac disease and healthy volunteers (n=95). Intervention: Highly accelerated bSSFP real-time myocardial tagging vs. ECG-segmented tagging. Primary outcome: Repeatability of real-time tagging mid-wall global circumferential strain (GCS) measures (ICC 0.86, 95% CI 0.71-0.94).

February 1, 2023Open Access

Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance

Key Result

Highly accelerated free-breathing real-time myocardial tagging demonstrated good correlation (r = 0.71) with ECG-segmented measures and excellent repeatability (ICC 0.86) for myocardial deformation.

Study Design

Type

Observational (n=95)

Blinding

Single-blind

Multicenter

Structured PICO

Does a highly accelerated free-breathing real-time myocardial tagging technique allow for quantification of myocardial deformation during exercise CMR in healthy subjects and patients with cardiac disease?

Population

95 subjects total across two prospective studies. Rest study: 27 patients referred for clinical CMR (mean age 54±16, 70% male) and 19 healthy subjects (mean age 26±4, 37% male). Ex-CMR study: 26 patients with known or suspected cardiac disease (mean age 55±15, 69% male) and 23 healthy subjects (mean age 40±14, 43% male).

Intervention

Highly accelerated balanced steady-state free-precession (bSSFP) real-time tagging technique for 3 T using a 12-fold acceleration with deep learning resolution enhancement (REGAIN).

Comparator

Breath-hold ECG-segmented tagging sequences (used for comparison in the rest study).

Outcome

Feasibility of quantifying myocardial deformation (short-axis global circumferential strain [GCS]), tagline quality, and artifact level.surrogate

A highly accelerated real-time tagging technique enables feasible quantification of myocardial deformation during exercise CMR, overcoming limitations of ECG-segmented sequences.

Main Result

Effect estimate: ICC 0.86 (95% CI 0.71-0.94)

Limitations

Small sample size
Imaging not performed during peak exercise because subjects were exercised outside of the scanner bore
Stress perfusion was not included as part of the imaging protocol
Evaluated using lines only, model does not support grid tagging
Quantification of beat-to-beat variation was performed in the short axis only
Only three slices were acquired, so mid-wall GCS does not represent the whole volume
Did not evaluate inter- or intra-observer reproducibility of measures of mid-wall GCS
Data from a single vendor and field strength were used for REGAIN training

Abstract

Exercise cardiovascular magnetic resonance (Ex-CMR) myocardial tagging would enable quantification of myocardial deformation after exercise. However, current electrocardiogram (ECG)-segmented sequences are limited for Ex-CMR. We developed a highly accelerated balanced steady-state free-precession real-time tagging technique for 3 T. A 12-fold acceleration was achieved using incoherent sixfold random Cartesian sampling, twofold truncated outer phase encoding, and a deep learning resolution enhancement model. The technique was tested in two prospective studies. In a rest study of 27 patients referred for clinical CMR and 19 healthy subjects, a set of ECG-segmented for comparison and two sets of real-time tagging images for repeatability assessment were collected in 2-chamber and short-axis views with spatiotemporal resolution 2.0 × 2.0 mm2 and 29 ms. In an Ex-CMR study of 26 patients with known or suspected cardiac disease and 23 healthy subjects, real-time images were collected before and after exercise. Deformation was quantified using measures of short-axis global circumferential strain (GCS). Two experienced CMR readers evaluated the image quality of all real-time data pooled from both studies using a 4-point Likert scale for tagline quality (1-excellent; 2-good; 3-moderate; 4-poor) and artifact level (1-none; 2-minimal; 3-moderate; 4-significant). Statistical evaluation included Pearson correlation coefficient (r), intraclass correlation coefficient (ICC), and coefficient of variation (CoV). In the rest study, deformation was successfully quantified in 90% of cases. There was a good correlation (r = 0.71) between ECG-segmented and real-time measures of GCS, and repeatability was good to excellent (ICC = 0.86 0.71, 0.94) with a CoV of 4.7%. In the Ex-CMR study, deformation was successfully quantified in 96% of subjects pre-exercise and 84% of subjects post-exercise. Short-axis and 2-chamber tagline quality were 1.6 ± 0.7 and 1.9 ± 0.8 at rest and 1.9 ± 0.7 and 2.5 ± 0.8 after exercise, respectively. Short-axis and 2-chamber artifact level was 1.2 ± 0.5 and 1.4 ± 0.7 at rest and 1.3 ± 0.6 and 1.5 ± 0.8 post-exercise, respectively. We developed a highly accelerated real-time tagging technique and demonstrated its potential for Ex-CMR quantification of myocardial deformation. Further studies are needed to assess the clinical utility of our technique.

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