Fetal brain MRI is useful for diagnosing brain abnormalities but is
challenged by fetal motion. The current protocol for T2-weighted fetal brain
MRI is not robust to motion so image volumes are degraded by inter- and intra-
slice motion artifacts. Besides, manual annotation for fetal MR image quality
assessment are usually time-consuming. Therefore, in this work, a
semi-supervised deep learning method that detects slices with artifacts during
the brain volume scan is proposed. Our method is based on the mean teacher
model, where we not only enforce consistency between student and teacher models
on the whole image, but also adopt an ROI consistency loss to guide the network
to focus on the brain region. The proposed method is evaluated on a fetal brain
MR dataset with 11,223 labeled images and more than 200,000 unlabeled images.
Results show that compared with supervised learning, the proposed method can
improve model accuracy by about 6\% and outperform other state-of-the-art
semi-supervised learning methods. The proposed method is also implemented and
evaluated on an MR scanner, which demonstrates the feasibility of online image
quality assessment and image reacquisition during fetal MR scans.

本研究提出了一种基于半监督的深度学习方法，通过均值教师模型及 ROI 一致性损失检测胎儿脑体积扫描中的伪迹，结果表明相比于有监督学习，该方法可以提高模型准确度约 6％，并且优于其他半监督学习方法。此外，该方法已在 MR 扫描仪上进行实现和评估，并证明了胎儿 MR 扫描过程中在线图像质量评估和图像重新采集的可行性。