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Abstract #3632

Deep learning for undersampled spiral DENSE reconstruction

Samuel W Fielden1,2, Eric D Carruth1, Christopher D Nevius1, Christopher M Haggerty1, and Brandon K Fornwalt1,3
1Imaging Sciences & Innovation, Geisinger, Danville, PA, United States, 2Medical & Health Physics, Geisinger, Danville, PA, United States, 3Radiology, Geisinger, Danville, PA, United States

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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