Cheng F, Liu Y, Chen Y, Yap PT. High-Resolution 3D Magnetic Resonance Fingerprinting With a Graph Convolutional Network.
IEEE TRANSACTIONS ON MEDICAL IMAGING 2023;
42:674-683. [PMID:
36269931 PMCID:
PMC10081960 DOI:
10.1109/tmi.2022.3216527]
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Abstract
Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging framework for rapid and simultaneous quantification of multiple tissue properties. 3D MRF allows higher through-plane resolution, but the acquisition process is slow when whole-brain coverage is needed. Existing methods for acceleration mainly rely on GRAPPA for k-space interpolation in the partition-encoding direction, limiting the acceleration factor to 2 or 3. In this work, we replace GRAPPA with a deep learning approach for accurate tissue quantification with greater acceleration. Specifically, a graph convolution network (GCN) is developed to cater to the non-Cartesian spiral sampling trajectories typical in MRF acquisition. The GCN maintains high quantification accuracy with up to 6-fold acceleration and allows 1mm isotropic resolution whole-brain 3D MRF data to be acquired in 3min and submillimeter 3D MRF (0.8mm) in 5min, greatly improving the feasibility of MRF in clinical settings.
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