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Abstract #5132

Deep Learning-Based Reconstruction of Accelerated Cardiac Cine MRI at 0.55T

Marc Vornehm1,2, Jens Wetzl2, Daniel Giese2,3, Jianing Pang4, Rizwan Ahmad5, and Florian Knoll1
1Computational Imaging Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 2Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany, 3Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 4Siemens Medical Solutions USA Inc., Chicago, IL, United States, 5Biomedical Engineering, The Ohio State University, Columbus, OH, United States

Synopsis

Keywords: Heart, Low-Field MRI

Acceleration of cardiac cine MRI is highly desirable in order to decrease the required breath-hold duration. On low-field MRI systems in particular, this could help make cardiac MRI more widely available. We present a method based on the Variational Network for reconstruction of cardiac cine MRI, trained on data from 1.5 and 3T systems. Reconstructions of retrospectively and prospectively undersampled acquisitions at 0.55T with an acceleration rate of eight are shown and compared to Compressed Sensing reconstructions. Despite the domain shift to low-field data, the neural network achieved an SSIM of 94.6%, which is comparable to the Compressed Sensing results.

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Keywords