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, so methods to accelerate image acquisition are needed. Deep learning has shown promise to assist with a myriad of reconstruction problems, including DENSE. Here, we explore the reconstruction performance of a non-Cartesian Deep Cascade of Convolutional Neural Networks (DCCNN) when presented with undersampled data generated from multiple spiral trajectory designs and acceleration rates.
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