ArticleFrontiers in radiology2024
Synthesis of MR fingerprinting information from magnitude-only MR imaging data using a parallelized, multi network U-Net convolutional neural network.
Article in Frontiers in radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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13 authors.
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Abstract
Background: MR fingerprinting (MRF) is a novel method for quantitative assessment of Objective: To develop a deep learning (DL) network for synthesizing MRF signals from conventional magnitude-only MR imaging data and to compare the results to the actual MRF signal acquired. Methods: A U-Net DL network was developed to synthesize MRF signals from magnitude-only 3D Results: The concordance correlation coefficient (and 95% confidence limits) for Conclusion: It is possible to synthesize MRF signals from MRI data using a DL network, thereby creating the potential for performing quantitative relaxometry assessment without the need for a dedicated MRF pulse sequence.
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