Dataset Information

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

ABSTRACT:

Purpose

To use deep learning for suppression of the artifact-generating T₁ -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 T₁ -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 T₁ -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 T₁ -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 T₁ -relaxation echo in DENSE MRI, enabling a 42% reduction in scan time compared to DENSE with phase-cycling.

SUBMITTER: Abdi M

PROVIDER: S-EPMC8295221 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Abdi Mohamad M Feng Xue X Sun Changyu C Bilchick Kenneth C KC Meyer Craig H CH Epstein Frederick H FH

Magnetic resonance in medicine 20210522 4

<h4>Purpose</h4>To use deep learning for suppression of the artifact-generating T<sub>1</sub> -relaxation echo in cine displacement encoding with stimulated echoes (DENSE) for the purpose of reducing the scan time.<h4>Methods</h4>A U-Net was trained to suppress the artifact-generating T<sub>1</sub> -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 frequenc ...[more]

PMID: 34021628

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Dataset Information

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

Purpose

Methods

Results

Conclusion

Publications

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

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