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Accelerated white matter lesion analysis based on simultaneous T1 and T*2 quantification using magnetic resonance fingerprinting and deep learning

Purpose To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning. Methods MRF using echo-planar imaging (EPI) scans with varying repetition and echo times were acqu...

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Main Authors: Hermann, Ingo (Author) , Martínez-Heras, Eloy (Author) , Rieger, Benedikt (Author) , Schmidt, Ralf (Author) , Golla, Alena-Kathrin (Author) , Hong, Jia-Sheng (Author) , Lee, Wei-Kai (Author) , Yu-Te, Wu (Author) , Nagtegaal, Martijn (Author) , Solana, Elisabeth (Author) , Llufriu, Sara (Author) , Gass, Achim (Author) , Schad, Lothar R. (Author) , Weingärtner, Sebastian (Author) , Zöllner, Frank G. (Author)
Format: Article (Journal)
Language:English
Published: 2021
In: Magnetic resonance in medicine
Year: 2021, Volume: 86, Issue: 1, Pages: 471-486
ISSN:1522-2594
DOI:10.1002/mrm.28688
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1002/mrm.28688
Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28688
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Author Notes:Ingo Hermann, Eloy Martínez-Heras, Benedikt Rieger, Ralf Schmidt, Alena-Kathrin Golla, Jia-Sheng Hong, Wei-Kai Lee, Wu Yu-Te, Martijn Nagtegaal, Elisabeth Solana, Sara Llufriu, Achim Gass, Lothar R. Schad, Sebastian Weingärtner, Frank G. Zöllner
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Summary:Purpose To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning. Methods MRF using echo-planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of and in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF and parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the and parametric maps, and the WM and GM probability maps. Results Deep learning-based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision. Mean absolute error performed the best for (deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for (deviations 6.0%). Conclusions MRF is a fast and robust tool for quantitative and mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.
Item Description:First published: 05 February 2021
Im Titel sind die "1" und "2" tiefgestellt
Im Titel ist das Sternchen hochgestellt
Gesehen am 28.10.2021
Physical Description:Online Resource
ISSN:1522-2594
DOI:10.1002/mrm.28688

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