Abstract
Magnetic resonance fingerprinting (MRF) is a novel imaging framework for fast and simultaneous quantification of multiple tissue properties. Recently, 3D MRF methods have been developed, but the acquisition speed needs to be improved before they can be adopted for clinical use. The purpose of this study is to develop a novel deep learning approach to accelerate 3D MRF acquisition along the slice-encoding direction in k-space. We introduce a graph-based convolutional neural network that caters to non-Cartesian spiral trajectories commonly used for MRF acquisition. We improve tissue quantification accuracy compared with the state of the art. Our method enables fast 3D MRF with high spatial resolution, allowing whole-brain coverage within 5 min, making MRF more feasible in clinical settings.
Keywords
This work was supported in part by NIH grant EB006733.
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29 September 2020
The original version of this chapter was revised. The NIH grant number has been corrected to EB006733 and typographical errors were corrected.
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Cheng, F., Chen, Y., Zong, X., Lin, W., Shen, D., Yap, PT. (2020). Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_16
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DOI: https://doi.org/10.1007/978-3-030-59713-9_16
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