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| Verfasst von: | Hermann, Ingo [VerfasserIn]  |
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| | Martínez-Heras, Eloy [VerfasserIn] |
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| | Rieger, Benedikt [VerfasserIn] |
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| | Schmidt, Ralf [VerfasserIn] |
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| | Golla, Alena-Kathrin [VerfasserIn] |
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| | Hong, Jia-Sheng [VerfasserIn] |
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| | Lee, Wei-Kai [VerfasserIn] |
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| | Yu-Te, Wu [VerfasserIn] |
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| | Nagtegaal, Martijn [VerfasserIn] |
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| | Solana, Elisabeth [VerfasserIn] |
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| | Llufriu, Sara [VerfasserIn] |
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| | Gass, Achim [VerfasserIn] |
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| | Schad, Lothar R. [VerfasserIn] |
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| | Weingärtner, Sebastian [VerfasserIn] |
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| | Zöllner, Frank G. [VerfasserIn] |
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| Titel: | Accelerated white matter lesion analysis based on simultaneous T1 and T*2 quantification using magnetic resonance fingerprinting and deep learning |
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| Verf.angabe: | 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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| Jahr: | 2021 |
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| Umfang: | 16 S. |
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| Fussnoten: | First published: 05 February 2021 ; Im Titel sind die "1" und "2" tiefgestellt ; Im Titel ist das Sternchen hochgestellt ; Gesehen am 28.10.2021 |
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| Titel Quelle: | Enthalten in: Magnetic resonance in medicine |
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| Ort Quelle: | New York, NY [u.a.] : Wiley-Liss, 1984 |
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| Jahr Quelle: | 2021 |
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| Band/Heft Quelle: | 86(2021), 1, Seite 471-486 |
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| ISSN Quelle: | 1522-2594 |
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| Abstract: | 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. |
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| DOI: | doi:10.1002/mrm.28688 |
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| URL: | kostenfrei: Volltext: https://doi.org/10.1002/mrm.28688 |
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| | kostenfrei: Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28688 |
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| | DOI: https://doi.org/10.1002/mrm.28688 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | deep learning reconstruction |
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| | magnetic resonance fingerprinting |
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| | mapping |
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| K10plus-PPN: | 177565866X |
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| Verknüpfungen: | → Zeitschrift |
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| Lokale URL UB: |  Zum Volltext |
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Accelerated white matter lesion analysis based on simultaneous T1 and T*2 quantification using magnetic resonance fingerprinting and deep learning / Hermann, Ingo [VerfasserIn]; 2021 (Online-Ressource)
