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Abstract #1970

DL2 - Deep Learning + Dictionary Learning-based Regularization for Accelerated 2D Dynamic Cardiac MR Image Reconstruction

Andreas Kofler1, Tobias Schaeffter1,2,3, and Christoph Kolbitsch1,2
1Physikalisch-Technische Bundesanstalt, Berlin and Braunschweig, Berlin, Germany, 2School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom, 3Department of Biomedical Engineering, Technical University of Berlin, Berlin, Germany

In this work, we combine Convolutional Neural Networks (CNN)- with Dictionary Learning (DL)- and Sparse Coding (SC)-based regularization for dynamic cardiac MR image reconstruction. The regularization on the image is imposed by patch-wise sparsity with respect to a learned overcomplete dictionary and closeness to a CNN-based image-prior which is obtained from a pre-trained CNN. We compare the proposed method to two iterative methods which incorporate the different components separately. We demonstrate the combination of CNNs with DL and SC leads to improved image quality and faster convergence compared to DL+SC only.

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