Abstract #1089

DAS-Net: A Generative Adversarial Net to Suppress Artifact-Generating Echoes in DENSE MRI

Mohammad Abdishektaei¹, Xue of Feng¹, Craig H Meyer^1,2, and Frederick H Epstein^1,2

¹Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, ²Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States

In DENSE, displacement-encoded stimulated echoes are acquired with an artifact-generating signal due to T1 relaxation. Phase-cycling acquisitions are generally used to suppress the artifact-generating echoes which can result in imperfect artifact suppression when there is motion between the two acquisitions. To avoid this problem, a generative adversarial convolutional neural network (DAS-Net) is proposed to suppress the artifacts from a single acquisition. DAS-Net was trained on a DENSE dataset acquired from healthy volunteers. Results show that DAS-Net can effectively suppress the artifact-generating echoes and has the potential to obviate the need for phase-cycling acquisitions

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