Suppression of artifact-generating echoes in cine DENSE using deep learning

Purpose: To use deep learning for suppression of the artifact-generating T-1-relaxation echo in cine displacement encoding with stimulated echoes (DENSE) for the purpose of reducing the scan time. Methods: A U-Net was trained to suppress the artifact-generating T-1-relaxation echo using complementary phase-cycled data as the ground truth. A data-augmentation method was developed that generates synthetic DENSE images with arbitrary displacement-encoding frequencies to suppress the T-1-relaxation echo modulated for a range of frequencies. The resulting U-Net (DAS-Net) was compared with k-space zero-filling as an alternative method. Non-phase-cycled DENSE images acquired in shorter breath-holds were processed by DAS-Net and compared with DENSE images acquired with phase cycling for the quantification of myocardial strain. Results: The DAS-Net method effectively suppressed the T-1-relaxation echo and its artifacts, and achieved root Mean Square(RMS) error = 5.5 +/- 0.8 and structural similarity index = 0.85 +/- 0.02 for DENSE images acquired with a displacement encoding frequency of 0.10 cycles/mm. The DAS-Net method outperformed zero-filling (root Mean Square error = 5.8 +/- 1.5 vs 13.5 +/- 1.5, DAS-Net vs zero-filling, P < .01; and structural similarity index = 0.83 +/- 0.04 vs 0.66 +/- 0.03, DAS-Net vs zero-filling, P < .01). Strain data for non-phase-cycled DENSE images with DAS-Net showed close agreement with strain from phase-cycled DENSE. Conclusion: The DAS-Net method provides an effective alternative approach for suppression of the artifact-generating T-1-relaxation echo in DENSE MRI, enabling a 42% reduction in scan time compared to DENSE with phase-cycling.