Fully automated quality control of rigid and affine registrations of T1w and T2w MRI in big data using machine learning.
Comput Biol Med
; 139: 104997, 2021 12.
Article
in En
| MEDLINE
| ID: mdl-34753079
ABSTRACT
BACKGROUND:
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.METHOD:
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.RESULTS:
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).CONCLUSIONS:
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Magnetic Resonance Imaging
/
Big Data
Type of study:
Prognostic_studies
Limits:
Adult
/
Humans
Language:
En
Journal:
Comput Biol Med
Year:
2021
Type:
Article