An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance

• Published in: Journal of cardiovascular magnetic resonance Vol. 24; no. 1; pp. 47 - 14
• Main Authors: Morales, Manuel A., Assana, Salah, Cai, Xiaoying, Chow, Kelvin, Haji-valizadeh, Hassan, Sai, Eiryu, Tsao, Connie, Matos, Jason, Rodriguez, Jennifer, Berg, Sophie, Whitehead, Neal, Pierce, Patrick, Goddu, Beth, Manning, Warren J., Nezafat, Reza
• Format: Journal Article
• Language: English
• Published: London BioMed Central 11.08.2022; Elsevier
• Subjects: Angiology; Artificial neural networks; Bias; Cardiology; Cardiovascular disease; Cartesian coordinates; Coronary artery; Coronary artery disease; Coronary Artery Disease - diagnostic imaging; Data acquisition; Deep Learning; EKG; Electrocardiography; Exercise MRI; Exercise Test; Feasibility Studies; Heart; Heart diseases; Humans; Image acquisition; Image Interpretation, Computer-Assisted - methods; Image quality; Image reconstruction; Imaging; Imaging techniques; Inline; Magnetic resonance; Magnetic Resonance Imaging, Cine - methods; Medical imaging; Medicine; Medicine & Public Health; Neural networks; Patients; Performance evaluation; Radial golden angle; Radiology; Real time; Reproducibility of Results; Respiratory-Gated Imaging Techniques - methods; Rest; Scanners; Technical Notes; Ventricle; United States > US; Deep learning; Exercise MRI; Inline; Radial golden angle; Coronary artery disease
• ISSN: 1097-6647; 1532-429X; 1532-429X
• DOI: 10.1186/s12968-022-00879-9

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.