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- W3203370825 abstract "Magnetic resonance fingerprinting (MRF) is a relatively new multi-parametric quantitative imaging method that involves a two-step process: (i) reconstructing a series of time frames from highly-undersampled non-Cartesian spiral k-space data and (ii) pattern matching using the time frames to infer tissue properties (e.g., (T_1) and (T_2) relaxation times). In this paper, we introduce a novel end-to-end deep learning framework to seamlessly map the tissue properties directly from spiral k-space MRF data, thereby avoiding time-consuming processing such as the non-uniform fast Fourier transform (NUFFT) and the dictionary-based fingerprint matching. Our method directly consumes the non-Cartesian k-space data, performs adaptive density compensation, and predicts multiple tissue property maps in one forward pass. Experiments on both 2D and 3D MRF data demonstrate that quantification accuracy comparable to state-of-the-art methods can be accomplished within 0.5 s, which is 1,100 to 7,700 times faster than the original MRF framework. The proposed method is thus promising for facilitating the adoption of MRF in clinical settings." @default.
- W3203370825 created "2021-10-11" @default.
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- W3203370825 date "2021-01-01" @default.
- W3203370825 modified "2023-10-13" @default.
- W3203370825 title "Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting" @default.
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- W3203370825 doi "https://doi.org/10.1007/978-3-030-87231-1_16" @default.
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