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Accelerated white matter lesion analysis based on simultaneous T1 and T2∗ quantification using magnetic resonance fingerprinting and deep learning

Journal Article (2021)
Author(s)

Ingo Hermann (TU Delft - ImPhys/Computational Imaging, University Heidelberg)

Eloy Martinez-Heras (Universitat Autònoma de Barcelona)

Benedikt Rieger (University Heidelberg)

Ralf Roman Schmidt (University Heidelberg)

Alena K. Golla (University Heidelberg)

Jia Sheng Hong (National Taipei University of Technology)

WK Lee (National Taipei University of Technology)

M.A. Nagtegaal (TU Delft - ImPhys/Computational Imaging, TU Delft - ImPhys/Medical Imaging)

S.D. Weingärtner (TU Delft - ImPhys/Computational Imaging, TU Delft - ImPhys/Medical Imaging)

More Authors (External organisation)

Research Group
ImPhys/Computational Imaging
Copyright
© 2021 I. Hermann, Eloy Martínez-Heras, Benedikt Rieger, Ralf Schmidt, Alena Kathrin Golla, Jia Sheng Hong, Wei Kai Lee, M.A. Nagtegaal, S.D. Weingärtner, More Authors
DOI related publication
https://doi.org/10.1002/mrm.28688
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 I. Hermann, Eloy Martínez-Heras, Benedikt Rieger, Ralf Schmidt, Alena Kathrin Golla, Jia Sheng Hong, Wei Kai Lee, M.A. Nagtegaal, S.D. Weingärtner, More Authors
Research Group
ImPhys/Computational Imaging
Issue number
1
Volume number
86
Pages (from-to)
471-486
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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 (Formula presented.) and (Formula presented.) in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF (Formula presented.) and (Formula presented.) parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the (Formula presented.) and (Formula presented.) 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 (Formula presented.) (deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for (Formula presented.) (deviations 6.0%). Conclusions: MRF is a fast and robust tool for quantitative (Formula presented.) and (Formula presented.) mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.