Quality assessment, including inspecting the images for artifacts, is a critical step during MRI data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep learning model to detect rigid motion in T1-weighted brain images. We leveraged a 2D CNN for three-class classification and tested it on publicly available retrospective and prospective datasets. Grad-CAM heatmaps enabled the identification of failure modes and provided an interpretation of the model's results. The model achieved average precision and recall metrics of 85% and 80% on six motion-simulated retrospective datasets. Additionally, the model's classifications on the prospective dataset showed a strong inverse correlation (-0.84) compared to average edge strength, an image quality metric indicative of motion. This model is part of the ArtifactID tool, aimed at inline automatic detection of Gibbs ringing, wrap-around, and motion artifacts. This tool automates part of the time-consuming QA process and augments expertise on-site, particularly relevant in low-resource settings where local MR knowledge is scarce.

翻译：质量评估（包括检查图像伪影）是磁共振数据采集过程中确保数据质量及后续分析或解读成功的关键环节。本研究展示了一个用于检测T1加权脑图像中刚性运动的深度学习模型。我们利用二维卷积神经网络进行三分类任务，并在公开回顾性及前瞻性数据集上进行了测试。通过Grad-CAM热力图实现了故障模式识别，并提供了模型结果的解读。该模型在六个运动模拟回顾性数据集上的平均精确率和召回率分别达到85%和80%。此外，模型在前瞻性数据集上的分类结果与平均边缘强度（一种指示运动伪影的图像质量指标）呈强负相关（-0.84）。该模型作为ArtifactID工具的一部分，旨在实现吉布斯环状伪影、卷褶伪影和运动伪影的在线自动检测。该工具可自动化部分耗时的质量评估流程，并增强现场专业判断能力，尤其适用于本地磁共振知识匮乏的低资源环境。