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

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

Hermann, I. (TU Delft ImPhys/Computational Imaging; University Heidelberg)
Martínez-Heras, Eloy (Universitat Autònoma de Barcelona)
Rieger, Benedikt (University Heidelberg)
Schmidt, Ralf (University Heidelberg)
Golla, Alena Kathrin (University Heidelberg)
Hong, Jia Sheng (National Taipei University of Technology)
Lee, Wei Kai (National Taipei University of Technology)
Nagtegaal, M.A. (TU Delft ImPhys/Computational Imaging; TU Delft ImPhys/Medical Imaging)
Weingärtner, S.D. (TU Delft ImPhys/Computational Imaging; TU Delft ImPhys/Medical Imaging)

2021

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.

mapping
deep learning reconstruction
magnetic resonance fingerprinting
T2∗ mapping

http://resolver.tudelft.nl/uuid:5323845c-ff1a-4126-a530-801c19b2dbe1

https://doi.org/10.1002/mrm.28688

0740-3194

Magnetic Resonance in Medicine, 86 (1), 471-486

Institutional Repository

journal article

© 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