Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver
Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver
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DOI：
10.2463/mrms.mp.2018-0156
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发表时间：
2020-01-01
影响因子：
3
通讯作者：
Motosugi, Utaroh
中科院分区：
文献类型：
--
作者：
Tamada, Daiki;Kromrey, Marie-Luise;Motosugi, Utaroh
目的：利用深度学习方法提高动态对比增强 MRI (DCE-MRI) 获得的图像质量，这些图像包含运动伪影和模糊。材料和方法：提出了一种基于多通道卷积神经网络的方法，用于减少通过肝脏 DCE-MRI 获得的图像中由呼吸运动引起的运动伪影和模糊。神经网络的训练数据集包括带有和不带有呼吸引起的运动伪影或模糊的图像，并且通过模拟 k 空间中的相位误差来生成失真。使用肝脏的多相 T-1 加权破坏梯度回波序列进行患者研究，其中包含数据采集期间发生的屏气失败。将训练好的网络应用于获取的图像来分析滤波性能，并通过 Bland-Altman 图比较去噪前后的强度和对比度。结果：发现所提出的网络显着降低了呼吸运动引起的伪影和模糊的程度，并且通过网络处理后的图像的对比度与未处理的图像的对比度一致。结论：一种基于深度学习的肝脏 DCE-MRI 图像中消除运动伪影的方法得到了论证和验证。
Purpose: To improve the quality of images obtained via dynamic contrast enhanced MRI (DCE-MRI), which contain motion artifacts and blurring using a deep learning approach.Materials and Methods: A multi-channel convolutional neural network-based method is proposed for reducing the motion artifacts and blurring caused by respiratory motion in images obtained via DCE-MRI of the liver. The training datasets for the neural network included images with and without respiration-induced motion artifacts or blurring, and the distortions were generated by simulating the phase error in k-space. Patient studies were conducted using a multi-phase T-1-weighted spoiled gradient echo sequence for the liver, which contained breath-hold failures occurring during data acquisition. The trained network was applied to the acquired images to analyze the filtering performance, and the intensities and contrast ratios before and after denoising were compared via Bland-Altman plots.Results: The proposed network was found to be significantly reducing the magnitude of the artifacts and blurring induced by respiratory motion, and the contrast ratios of the images after processing via the network were consistent with those of the unprocessed images.Conclusion: A deep learning-based method for removing motion artifacts in images obtained via DCE-MRI of the liver was demonstrated and validated.