用机器学习自动评估胎儿脑MRI超分辨率重建质量，提升多中心数据可靠性。
Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction
- 基于随机森林提取100+图像质量指标，实现非深度学习的质量评分。
- 跨机构测试下表现优异（ROC AUC=0.89），对未知设备或方法有强鲁棒性。
- 适用于多中心胎儿脑影像研究，尤其适合缺乏标注数据的场景。
质量控制（QC）对于保障神经影像研究的可靠性至关重要，尤其在胎儿脑MRI中，因采集与处理流程尚未标准化，更显重要。本文聚焦于胎儿脑MRI超分辨率重建（SRR）体积的自动化质量控制，该步骤将多组厚层2D切片配准融合，生成单一各向同性、无伪影的T2加权图像。我们提出FetMRQC$_{SR}$，一种基于机器学习的方法，从图像中提取超过100个质量指标，利用随机森林模型预测质量评分。该方法适用于高维、异质性强且数据量小的问题。我们在域外（OOD）设置下验证了该方法，报告了高性能（ROC AUC = 0.89），即使面对未知机构或重建方法的数据仍保持稳健。我们还分析了失败案例，发现45%的误判源于专家评分存疑的模糊配置。结果表明，非深度学习方法在这一复杂任务中同样具备优势。相关工具及全部代码已开源：https://github.com/Medical-Image-Analysis-Laboratory/fetmrqc_sr/
原文摘要 · Abstract (English)
Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where acquisitions and image processing techniques are less standardized than in adult imaging. In this work, we focus on automated quality control of super-resolution reconstruction (SRR) volumes of fetal brain MRI, an important processing step where multiple stacks of thick 2D slices are registered together and combined to build a single, isotropic and artifact-free T2 weighted volume. We propose FetMRQC$_{SR}$, a machine-learning method that extracts more than 100 image quality metrics to predict image quality scores using a random forest model. This approach is well suited to a problem that is high dimensional, with highly heterogeneous data and small datasets. We validate FetMRQC$_{SR}$ in an out-of-domain (OOD) setting and report high performance (ROC AUC = 0.89), even when faced with data from an unknown site or SRR method. We also investigate failure cases and show that they occur in $45\%$ of the images due to ambiguous configurations for which the rating from the expert is arguable. These results are encouraging and illustrate how a non deep learning-based method like FetMRQC$_{SR}$ is well suited to this multifaceted problem. Our tool, along with all the code used to generate, train and evaluate the model are available at https://github.com/Medical-Image-Analysis-Laboratory/fetmrqc_sr/ .
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。