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Abstract #4048

Deep Learning for under-sampled non-cartesian ASL MRI reconstruction

Yanchen Guo1, Shichun Chen1, Li Zhao2, Manuel Taso3, David C. Alsop3, and Weiying Dai1
1Computer Science, State University of New York at Binghamton, Binghamton, NY, United States, 2Zhejiang University, Hangzhou, China, 3Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States


Compressed sensing allowed speeding up MRI image acquisition by several folds but with increased reconstruction time. Deep-learning was introduced for fast reconstruction with comparable or improved reconstruction quality. However, its applications on non-cartesian grids are scarce, mostly based on simulated resampling from original Cartesian grids. Here, we evaluated the performance of two deep-learning networks for reconstructing images acquired with k-space spiral trajectories in real MRI scanning. We demonstrated that deep learning networks can successfully reconstruct high-resolution images from under-sampled spiral trajectories with half of data in k-space and their performance is robust to spiral trajectories different from training.

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