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Magn Reson Med ; 86(1): 471-486, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33547656

RESUMO

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.


Assuntos
Aprendizado Profundo , Substância Branca , Encéfalo/diagnóstico por imagem , Humanos , Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Espectroscopia de Ressonância Magnética , Imagens de Fantasmas , Reprodutibilidade dos Testes , Substância Branca/diagnóstico por imagem
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