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.