Accelerated white matter lesion analysis based on simultaneous T-1 and T-2* quantification using magnetic resonance fingerprinting and deep learning

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.