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.

MRI 数据获取中的质量评估是确保数据质量和下游分析或解释成功的关键步骤。本研究演示了一种深度学习模型，用于检测 T1 加权脑图像中的刚体运动。该模型是 ArtifactID 工具的一部分，旨在在线自动检测 Gibbs 环绕、穿越和运动伪影。该工具自动化了耗时的质量评估过程，并增强了现场的专业知识，尤其适用于本地磁共振知识匮乏的资源匮乏环境。