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