The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper was to develop a standalone novel technique to suppress motion artefacts in MR images using a data-driven deep learning approach. A simulation framework was developed to generate motion-corrupted images from motion-free images using randomly generated motion profiles. An Inception-ResNet deep learning network architecture was used as the encoder and was augmented with a stack of convolution and upsampling layers to form an encoder-decoder network. The network was trained on simulated motion-corrupted images to identify and suppress those artefacts attributable to motion. The network was validated on unseen simulated datasets and real-world experimental motion-corrupted in vivo brain datasets. The trained network was able to suppress the motion artefacts in the reconstructed images, and the mean structural similarity (SSIM) increased from 0.9058 to 0.9338. The network was also able to suppress the motion artefacts from the real-world experimental dataset, and the mean SSIM increased from 0.8671 to 0.9145. The motion correction of the experimental datasets demonstrated the effectiveness of the motion simulation generation process. The proposed method successfully removed motion artefacts and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error, which were 5–10% better for the proposed method. In conclusion, a novel, data-driven motion correction technique has been developed that can suppress motion artefacts from motion-corrupted MR images. The proposed technique is a standalone, post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for routine clinical practice.

中文翻译：

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使用具有运动模拟增强功能的 Inception-ResNet 网络抑制 MRI 中的运动伪影

抑制 MR 图像中的运动伪影是一项具有挑战性的任务。本文的目的是开发一种独立的新技术，使用数据驱动的深度学习方法来抑制 MR 图像中的运动伪影。开发了一个模拟框架，使用随机生成的运动配置文件从无运动图像中生成运动损坏的图像。Inception-ResNet 深度学习网络架构被用作编码器，并增加了一堆卷积和上采样层以形成编码器-解码器网络。该网络在模拟的运动损坏图像上进行了训练，以识别和抑制那些归因于运动的伪影。该网络在看不见的模拟数据集和真实世界实验性运动损坏的体内大脑数据集上进行了验证。训练后的网络能够抑制重建图像中的运动伪影，平均结构相似度（SSIM）从 0.9058 增加到 0.9338。该网络还能够抑制真实世界实验数据集中的运动伪影，平均 SSIM 从 0.8671 增加到 0.9145。实验数据集的运动校正证明了运动模拟生成过程的有效性。所提出的方法成功地去除了运动伪影，并在 SSIM 指数和归一化均方根误差方面优于迭代熵最小化方法，这比所提出的方法好 5-10%。总之，已经开发出一种新颖的、数据驱动的运动校正技术，可以抑制运动损坏的 MR 图像中的运动伪影。