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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