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Abstract #3740

Accelerated High-Resolution 3D MR Fingerprinting Using a Graph Convolutional Network

Feng Cheng1, Zhenghan Fang2, Xiaopeng Zong3, Weili Lin3, Yong Chen3, and Pew-Thian Yap3
1Department of Computer Science, University of North Carolina at Chapel Hill, CHAPEL Hill, NC, United States, 2CuraCloud Corporation, Seattle, WA, United States, 3Department of Radiology, University of North Carolina at Chapel Hill, CHAPEL Hill, NC, United States

In this study, a k-space interpolation technique for high-resolution 3D MR Fingerprinting is proposed. We formulate the problem as a graph and apply a graph convolutional network on the graph to interpolate the missing partitions. Our preliminary results show that the proposed method can provide improved results both in reconstructed k-space data and in extracted quantitative maps and can potentially allow higher acceleration factors along the partition-encoding direction.

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