基于磁共振指纹成像和深度学习的同时定量加速白质病变分析。
Accelerated white matter lesion analysis based on simultaneous and quantification using magnetic resonance fingerprinting and deep learning.
机构信息
Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands.
出版信息
Magn Reson Med. 2021 Jul;86(1):471-486. doi: 10.1002/mrm.28688. Epub 2021 Feb 5.
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.
目的
开发一种加速的后处理管道，以使用定量磁共振指纹图谱（MRF）和深度学习对脑白质病变进行可重复且高效的评估。
方法
对 50 例多发性硬化症（MS）患者和 10 名健康志愿者的整个大脑进行了基于 EPI 扫描的 MRF 采集，扫描的重复时间和回波时间不同。对 MRF 和参数图进行了失真校正和去噪。训练一个卷积神经网络（CNN）来重建 和 参数图以及 WM 和 GM 概率图。
结果
基于深度学习的后处理可将重建和图像处理时间从数小时缩短到几秒钟，同时保持高精度、高可靠性和高精确性。对于 （偏差为 5.6%），平均绝对误差的表现最佳，对于 （偏差为 6.0%），对数双曲余弦损失的表现最佳。
结论
MRF 是一种快速且强大的定量 和 映射工具。它的重建时间长，并且有几个后处理步骤，可以使用深度学习来简化和加速。
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