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PURPOSE: REACT (Relaxation-Enhanced Angiography without ContrasT) is a reliable non-contrast magnetic resonance angiography for imaging of the thoracic aorta but remains time-consuming. This study evaluates acceleration of image acquisition using compressed sensing and parallel imaging (Compressed SENSE, CS) combined with deep learning-based image reconstruction (CS-AI). METHODS: In this prospective single-center study, 40 volunteers underwent ECG- and navigator-triggered 3D REACT at 3 T using CS acceleration factor 4 (CS4; reference standard) and 8 (CS8). CS8 data were reconstructed with standard and CS-AI methods (CS8-AI). Two radiologists measured aortic diameters, rated subjective image quality and performed pairwise comparisons. Additionally, objective image quality metrics were calculated. RESULTS: Median scan time was reduced by 44 % (CS4: 8:41 min; CS8/CS8-AI: 4:52 min). All techniques showed excellent agreement in aortic diameter measurements (mean differences 0.999). CS8-AI demonstrated reduced mean absolute deviation from CS4 compared to CS8 (0.67 vs. 0.77 mm; P = 0.003), and measurement variance was 40-50 % lower with CS8-AI than with CS8 (inter-/intrarater: P 0.999). Objective metrics predominantly confirmed the subjective results. CONCLUSION: Deep learning-based reconstruction enables the acquisition of REACT of the thoracic aorta in less than five minutes while preserving high image quality and maintaining excellent measurement reproducibility.
Janßen et al. (Tue,) studied this question.