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Accelerated white matter lesion analysis based on simultaneous T 1 and T 2 ∗ quantification using magnetic resonance fingerprinting and deep learning
Author(s) -
Hermann Ingo,
MartínezHeras Eloy,
Rieger Benedikt,
Schmidt Ralf,
Golla AlenaKathrin,
Hong JiaSheng,
Lee WeiKai,
YuTe Wu,
Nagtegaal Martijn,
Solana Elisabeth,
Llufriu Sara,
Gass Achim,
Schad Lothar R.,
Weingärtner Sebastian,
Zöllner Frank G.
Publication year - 2021
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28688
Subject(s) - artificial intelligence , deep learning , magnetic resonance imaging , pattern recognition (psychology) , white matter , computer science , parametric statistics , nuclear magnetic resonance , nuclear medicine , physics , mathematics , statistics , medicine , radiology
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 T 1 and T 2 ∗ in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF T 1 and T 2 ∗ parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the T 1 and T 2 ∗ 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 T 1 (deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for T 2 ∗ (deviations 6.0%). Conclusions MRF is a fast and robust tool for quantitative T 1 and T 2 ∗ mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.