Abstract：
Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the personnel, posing limitations especially with large datasets. Machine learning based on convolutional neural networks (CNNs) is a promising approach to address these challenges by performing automated inspection of MR images. In this study, a CNN for the detection of random head motion artifacts (RHM) in T1-weighted MRI as one aspect of image quality is proposed. A two-step approach aimed to first identify images exhibiting pronounced motion artifacts, and second to evaluate the feasibility of a more detailed three-class classification. The utilized dataset consisted of 420 T1-weighted whole-brain image volumes with isotropic resolution. Human experts assigned each volume to one of three classes of artifact prominence. Results demonstrate an accuracy of 95% for the identification of images with pronounced artifact load. The addition of an intermediate class retained an accuracy of 76%. The findings highlight the potential of CNN-based approaches to increase the efficiency of post-hoc QAs in large datasets by flagging images with potentially relevant artifact loads for closer inspection.
摘要：
磁共振成像（MRI）的质量评估（QA）包括几个因素，如噪声，对比,同质性，和成像伪影。质量评价往往不规范，依赖于专业知识，和人员的警惕，构成限制，特别是对于大型数据集。基于卷积神经网络（CNN）的机器学习是通过执行MR图像的自动化检查来解决这些挑战的有前途的方法。在这项研究中,提出了用于检测T1加权MRI中的随机头部运动伪影(RHM)作为图像质量的一个方面的CNN。一种两步方法，旨在首先识别表现出明显运动伪影的图像，第二，评估更详细的三类分类的可行性。所利用的数据集由420个T1加权的全脑图像体积组成，具有各向同性的分辨率。人类专家将每卷分配给三类人工制品突出之一。结果表明，识别具有明显伪影负载的图像的准确率为95％。中间类的添加保持了76%的准确度。这些发现强调了基于CNN的方法通过标记具有潜在相关伪影负载的图像以进行更紧密的检查来提高大型数据集中的事后QA的效率的潜力。
