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Abstract #1964

Using Untrained Convolutional Neural Networks to Accelerate MRI in 2D and 3D

Dave Van Veen1,2, Arjun Desai1, Reinhard Heckel3,4, and Akshay S. Chaudhari1
1Stanford University, Stanford, CA, United States, 2University of Texas at Austin, Austin, TX, United States, 3Rice University, Houston, TX, United States, 4Technical University of Munich, Munich, Germany

We investigate untrained convolutional neural networks for accelerating both 2D and 3D MRI scans of the knee. Machine learning has demonstrated great potential to accelerate scans while maintaining high quality reconstructions. However, these methods are often trained over a large number of fully-sampled scans, which are difficult to acquire. Here we demonstrate MRI acceleration with untrained networks, achieving similar performance to a trained baseline. Further, we use undersampled k-space measurements as regularization priors to increase the robustness of untrained methods.

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