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arXiv 2109.01622eess.IV
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用于量化$R_2^\ast$映射的基于学习的运动干扰消除网络（LEARN）

Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative $R_2^\ast$ Mapping

Xiaojian Xu, Satya V. V. N. Kothapalli, Jiaming Liu, Sayan Kahali, Weijie Gan, Dmitriy A. Yablonskiy, Ulugbek S. Kamilov

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摘要：

目的：介绍两种新型的基于学习的运动伪影去除网络（LEARN），用于从运动受损的多梯度回波（mGRE）MRI数据中估计定量运动和$B0$不稳定性校正的$R_2^\ast$图。研究方法：我们训练两个卷积神经网络（CNN）来校正运动伪影，以高质量地估计来自mGRE序列的定量$B0$ -不稳定性校正的$R_2^\ast$图。第一个CNN，LEARN-IMG，对复杂的mGRE图像执行运动校正，以便使用标准的逐体素分析或基于机器学习的分析来实现高质量无运动定量$R_2^\ast$（和任何其他启用mGRE的）图的后续计算。第二个CNN，LEARN-BIO，被训练为通过利用描述mGRE信号衰减的生物物理模型，从运动受损的仅幅度mGRE图像直接生成运动和$B0$不稳定性校正的定量$R_2^\ast$图。我们证明了在合成MR图像上训练的两种CNN都能够抑制运动伪影，同时保留预测的定量$R_2^\ast$图中的细节。通过使用我们的训练模型，也实现了在实验体内运动损坏的数据的运动伪影的显着减少。结论：LEARN-IMG和LEARN-BIO都可以计算高质量的运动和$B0$不稳定性校正的$R_2^\ast$图。LEARN-IMG对mGRE图像执行运动校正，并依赖于随后的分析来估计$R_2^\ast$图，而LEARN-BIO直接执行运动和$B0$不稳定性校正的$R_2^\ast$估计。LEARN-IMG和LEARN-BIO联合处理所有可用的梯度回波，这使它们能够利用数据中可用的空间模式。LEARN-BIO的高计算速度是一个优势，可以导致更广泛的临床应用。

英文摘要：

Purpose: To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ maps from motion-corrupted multi-Gradient-Recalled Echo (mGRE) MRI data. Methods: We train two convolutional neural networks (CNNs) to correct motion artifacts for high-quality estimation of quantitative $B0$-inhomogeneity-corrected $R_2^\ast$ maps from mGRE sequences. The first CNN, LEARN-IMG, performs motion correction on complex mGRE images, to enable the subsequent computation of high-quality motion-free quantitative $R_2^\ast$ (and any other mGRE-enabled) maps using the standard voxel-wise analysis or machine-learning-based analysis. The second CNN, LEARN-BIO, is trained to directly generate motion- and $B0$-inhomogeneity-corrected quantitative $R_2^\ast$ maps from motion-corrupted magnitude-only mGRE images by taking advantage of the biophysical model describing the mGRE signal decay. We show that both CNNs trained on synthetic MR images are capable of suppressing motion artifacts while preserving details in the predicted quantitative $R_2^\ast$ maps. Significant reduction of motion artifacts on experimental in vivo motion-corrupted data has also been achieved by using our trained models. Conclusion: Both LEARN-IMG and LEARN-BIO can enable the computation of high-quality motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ maps. LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of $R_2^\ast$ maps, while LEARN-BIO directly performs motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ estimation. Both LEARN-IMG and LEARN-BIO jointly process all the available gradient echoes, which enables them to exploit spatial patterns available in the data. The high computational speed of LEARN-BIO is an advantage that can lead to a broader clinical application.

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