Accelerating non-contrast MR angiography of the thoracic aorta using compressed SENSE with deep learning reconstruction

Abstract

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 < 0.2 mm; P > 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 < 0.001), and comparable to CS4. CS8 showed significantly lower subjective image quality scores than CS4 (3.70[3.33–4.00] vs. 4.25[3.90–4.50]; P < 0.001), while CS8-AI showed comparable or higher scores (4.40[4.00–4.70]; P = 0.076). Forced-choice comparisons favored CS4 over CS8 (90 % vs. 2.5 %; P < 0.001), but no preference was observed between CS4 and CS8-AI (42.5 % vs. 37.5 %; 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.