Department of Electronics and Communication Engineering, SRM University-AP, Andhra Pradesh, India; Clinic for Neurology, University Medical Center, Göttingen, Germany.
Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India.
Comput Biol Med. 2021 Dec;139:104997. doi: 10.1016/j.compbiomed.2021.104997. Epub 2021 Nov 1.
Magnetic resonance imaging (MRI)-based morphometry and relaxometry are proven methods for the structural assessment of the human brain in several neurological disorders. These procedures are generally based on T1-weighted (T1w) and/or T2-weighted (T2w) MRI scans, and rigid and affine registrations to a standard template(s) are essential steps in such studies. Therefore, a fully automatic quality control (QC) of these registrations is necessary in big data scenarios to ensure that they are suitable for subsequent processing.
A supervised machine learning (ML) framework is proposed by computing similarity metrics such as normalized cross-correlation, normalized mutual information, and correlation ratio locally. We have used these as candidate features for cross-validation and testing of different ML classifiers. For 5-fold repeated stratified grid search cross-validation, 400 correctly aligned, 2000 randomly generated misaligned images were used from the human connectome project young adult (HCP-YA) dataset. To test the cross-validated models, the datasets from autism brain imaging data exchange (ABIDE I) and information eXtraction from images (IXI) were used.
The ensemble classifiers, random forest, and AdaBoost yielded best performance with F1-scores, balanced accuracies, and Matthews correlation coefficients in the range of 0.95-1.00 during cross-validation. The predictive accuracies reached 0.99 on the Test set #1 (ABIDE I), 0.99 without and 0.96 with noise on Test set #2 (IXI, stratified w.r.t scanner vendor and field strength).
The cross-validated and tested ML models could be used for QC of both T1w and T2w rigid and affine registrations in large-scale MRI studies.
基于磁共振成像（MRI）的形态计量学和弛豫率是评估多种神经疾病患者大脑结构的成熟方法。这些方法通常基于 T1 加权（T1w）和/或 T2 加权（T2w）MRI 扫描，并且刚性和仿射配准到标准模板是此类研究的必要步骤。因此，在大数据环境下，需要对这些配准进行全面的自动质量控制（QC），以确保其适合后续处理。
通过在局部计算归一化互相关、归一化互信息和相关比等相似性度量，提出了一种有监督的机器学习（ML）框架。我们将这些作为候选特征进行交叉验证和不同 ML 分类器的测试。对于 5 折重复分层网格搜索交叉验证，从人类连接组计划青年组（HCP-YA）数据集中使用了 400 个正确对齐的、2000 个随机生成的配准错误的图像。为了测试交叉验证模型，我们使用了自闭症脑成像数据交换（ABIDE I）和图像信息提取（IXI）数据集。
在交叉验证过程中，集成分类器随机森林和 AdaBoost 产生的 F1 分数、平衡准确率和马修斯相关系数最佳，范围为 0.95-1.00。在测试集 #1（ABIDE I）上的预测准确率达到 0.99，在测试集 #2（IXI，按扫描仪供应商和场强分层）上无噪声时为 0.99，有噪声时为 0.96。
经过交叉验证和测试的 ML 模型可用于大型 MRI 研究中 T1w 和 T2w 刚性和仿射配准的 QC。
