Davaux Florian, Valladon Lucas, Dole Lucie, Fillion Jean Christophe, Paniagua Beatriz, Styner Martin, Prieto Juan Carlos
University of North Carolina, Chapel Hill, United States.
Kitware Inc, Carrboro, United States.
Proc SPIE Int Soc Opt Eng. 2025 Feb;13410. doi: 10.1117/12.3047233. Epub 2025 Apr 2.
The Adolescent Brain Cognitive Development (ABCD) Study collects extensive neuroimaging data, including over 20,000 MRI sessions, to understand brain development in children. Ensuring high-quality MRI data is essential for accurate analysis, but manual Quality Control (QC) is impractical for large datasets due to time and resource constraints. We propose an automated QC method using an ensemble model that leverages metrics from FSQC and a novel deep learning model for brain shape analysis that uses cortical thickness, curvature, sulcal depth, and surface area as input features. The ensemble model achieved an accuracy of 76%, while our method achieved an accuracy of 72.62%, with balanced precision, recall, and F1 scores for both classes. This automated method promises to improve QC processes and accelerate the analysis of ABCD data.
青少年大脑认知发展（ABCD）研究收集了大量神经影像数据，包括超过20000次磁共振成像（MRI）扫描，以了解儿童的大脑发育情况。确保高质量的MRI数据对于准确分析至关重要，但由于时间和资源限制，手动质量控制（QC）对于大型数据集来说是不切实际的。我们提出了一种使用集成模型的自动化QC方法，该模型利用来自FSQC的指标和一种用于脑形状分析的新型深度学习模型，该模型使用皮质厚度、曲率、脑沟深度和表面积作为输入特征。集成模型的准确率达到了76%，而我们的方法准确率为72.62%，两类的精确率、召回率和F1分数均保持平衡。这种自动化方法有望改善QC流程并加速对ABCD数据的分析。
