Background and Objective: Brain MRI is one of the most commonly used diagnostic imaging tools to detect neurodegenerative disease. Diagnostic image quality is a key factor to enable robust image analysis algorithms developed for downstream tasks such as segmentation. In clinical practice, one of the main challenges is the presence of image artefacts, which can lead to low diagnostic image quality.

Methods: In this paper, we propose using dense convolutional neural networks to detect and a residual U-net architecture to correct motion related brain MRI artefacts. We first generate synthetic artefacts using an MR physics based corruption strategy. Then, we use a detection strategy based on dense convolutional neural network to detect artefacts. The detected artefacts are corrected using a residual U-net network trained on corrupted data.

Results: Our pipeline for detection and correction of artefacts is capable of reaching not only better quality image quality, but also better segmentation accuracy of stroke segmentation. The algorithm is validated using 28 cases brain MRI stroke segmentation dataset and showed an accuracy of 97.8% for detecting artefacts in our experiments. We also illustrated the improved image quality and segmentation accuracy with the proposed correction algorithm.

Conclusions: Ensuring high image quality and high segmentation quality jointly can improve the automatic image analysis pipelines and reduce the influence of low image quality on final prognosis. With this work, we illustrate a performance analysis on brain MRI stroke segmentation.

背景与目的：脑MRI是检测神经退行性疾病最常用的诊断成像工具之一。诊断图像质量是启用针对下游任务（例如分段）开发的强大图像分析算法的关键因素。在临床实践中，主要挑战之一是图像伪像的存在，这可能导致诊断图像质量低下。

方法：在本文中，我们建议使用密集卷积神经网络进行检测，并使用残余的U-net体系结构来校正与运动有关的大脑MRI伪像。我们首先使用基于MR物理学的腐败策略生成人工合成物。然后，我们使用基于密集卷积神经网络的检测策略来检测伪像。使用对损坏的数据进行训练的残留U-net网络，可以纠正检测到的伪影。

结果：我们用于伪影检测和纠正的管道不仅能够获得更高质量的图像质量，而且还能达到笔划分割的更高分割精度。该算法已使用28例脑部MRI脑卒中分割数据集进行了验证，并在我们的实验中显示出97.8％的伪影检测精度。我们还通过提出的校正算法说明了改进的图像质量和分割精度。

结论：共同确保高图像质量和高分割质量可以改善自动图像分析流程，并减少低图像质量对最终预后的影响。通过这项工作，我们说明了对大脑MRI中风分割的性能分析。