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Abstract #2641

A comparison of spiral trajectories in a deep learning reconstruction for DENSE

Samuel Fielden1,2, Eric Carruth1, Brandon Fornwalt1,3,4, and Christopher Haggerty1,3
1Translational Data Science and Informatics, Geisinger, Danville, PA, United States, 2Medical and Health Physics, Geisinger, Danville, PA, United States, 3Heart Institute, Geisinger, Danville, PA, United States, 4Radiology, 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, 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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