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Abstract #5137

Unsupervised deep learning with variational autoencoder for image quality improvement in ultrafast cardiac cine MRI: Initial results

Yajing Zhang1, Guohui Ruan1, Tianyu Han2, Christiane Kuhl3, Masami Yoneyama4, Daniel Truhn3, and Shuo Zhang3,5
1MR Clinical Science, Philips Health Technology, Suzhou, China, 2RWTH University Aachen, Aachen, Germany, 3Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany, 4Philips Japan, Tokyo, Japan, 5Philips Market DACH, Hamburg, Germany

Synopsis

Keywords: Myocardium, CardiovascularUltrafast imaging with high acceleration in cardiac MRI is of great clinical interest, but so far often results in inferior image quality that prevents its use in routine diagnosis. In this work, we aim to establish an unsupervised deep learning neural network based on vector quantized variational autoencoder for noise reduction and image quality improvement. Initial results on both public and clinical data promise a new approach to the existing methods. Further investigations with focus on its effectiveness of performance in real world applications are warranted.

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