Displacement Encoding with Stimulated Echoes (DENSE) is a powerful technique that has found great utility in accurately measuring cardiac tissue displacement. However, DENSE remains time-consuming to acquire, particularly for 3-dimensionally encoded or higher resolution schemes, and so methods to accelerate image acquisition are needed. Here, we apply the Deep Cascade of Convolutional Neural Networks (DCCNN) to the complex-valued, non-Cartesian data of DENSE to show that accelerated imaging via k-space undersampling is feasible using a deep learning-based reconstruction.
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