Inline deep learning-based radial acceleration for real-time cine achieved a mean artifact score of 3.3 at rest compared to 3.9 for standard cine, demonstrating feasibility for exercise cardiovascular magnetic resonance.
Observational (n=22)
Single-blind
No
Does inline real-time cine with DL-based radial acceleration provide diagnostic image quality and accurate LV function assessment during exercise CMR?
Inline real-time cine with deep learning-based radial acceleration is feasible for exercise CMR, providing rapid reconstruction and diagnostic image quality.
Absolute Event Rate: 3.3% vs 3.9%
p-value: p=<0.001
BACKGROUND: Exercise cardiovascular magnetic resonance (Ex-CMR) is a promising stress imaging test for coronary artery disease (CAD). However, Ex-CMR requires accelerated imaging techniques that result in significant aliasing artifacts. Our goal was to develop and evaluate a free-breathing and electrocardiogram (ECG)-free real-time cine with deep learning (DL)-based radial acceleration for Ex-CMR. METHODS: A 3D (2D + time) convolutional neural network was implemented to suppress artifacts from aliased radial cine images. The network was trained using synthetic real-time radial cine images simulated using breath-hold, ECG-gated segmented Cartesian k-space data acquired at 3 T from 503 patients at rest. A prototype real-time radial sequence with acceleration rate = 12 was used to collect images with inline DL reconstruction. Performance was evaluated in 8 healthy subjects in whom only rest images were collected. Subsequently, 14 subjects (6 healthy and 8 patients with suspected CAD) were prospectively recruited for an Ex-CMR to evaluate image quality. At rest (n = 22), standard breath-hold ECG-gated Cartesian segmented cine and free-breathing ECG-free real-time radial cine images were acquired. During post-exercise stress (n = 14), only real-time radial cine images were acquired. Three readers evaluated residual artifact level in all collected images on a 4-point Likert scale (1-non-diagnostic, 2-severe, 3-moderate, 4-minimal). RESULTS: The DL model substantially suppressed artifacts in real-time radial cine images acquired at rest and during post-exercise stress. In real-time images at rest, 89.4% of scores were moderate to minimal. The mean score was 3.3 ± 0.7, representing increased (P < 0.001) artifacts compared to standard cine (3.9 ± 0.3). In real-time images during post-exercise stress, 84.6% of scores were moderate to minimal, and the mean artifact level score was 3.1 ± 0.6. Comparison of left-ventricular (LV) measures derived from standard and real-time cine at rest showed differences in LV end-diastolic volume (3.0 mL - 11.7, 17.8, P = 0.320) that were not significantly different from zero. Differences in measures of LV end-systolic volume (7.0 mL - 1.3, 15.3, P < 0.001) and LV ejection fraction (- 5.0% - 11.1, 1.0, P < 0.001) were significant. Total inline reconstruction time of real-time radial images was 16.6 ms per frame. CONCLUSIONS: Our proof-of-concept study demonstrated the feasibility of inline real-time cine with DL-based radial acceleration for Ex-CMR.
Morales et al. (Sat,) conducted a observational in Suspected coronary artery disease (n=22). Deep learning-based radial acceleration (DRAPR) real-time cine vs. Standard breath-hold ECG-gated Cartesian segmented cine was evaluated on Mean artifact level score at rest (4-point Likert scale) (p=<0.001). Inline deep learning-based radial acceleration for real-time cine achieved a mean artifact score of 3.3 at rest compared to 3.9 for standard cine, demonstrating feasibility for exercise cardiovascular magnetic resonance.