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

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Abstract

To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning.
MRF using echo-planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of

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in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF

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parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the

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parametric maps, and the WM and GM probability maps.
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

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(deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for

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(deviations 6.0%).
MRF is a fast and robust tool for quantitative

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mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.
© 2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.