Keywords: Analysis/Processing, Brain, Quality Control, Infant
Motivation: Manual quality control (QC) for infant brain MRI is time-consuming and labor-intensive. The implementation of automatic QC is necessary for clinical scenarios.
Goal(s): To develop a generalizable, highly accurate, automatic tool for infant brain T1w-MRI quality control.
Approach: We design a generalizable automatic model with Residual Network (ResNet) and Vision transformer (ViT) modules for infant brain T1w-MRI QC. Our model is trained and validated on two large-scale multi-site infant MRI datasets (including Baby Connectome Project and China Baby Connectome Project).
Results: Based on our method, we can automatically classify the data quality with the accuracy of over 95% for BCP and CBCP datasets.
Impact: Our automatic MRI quality control tool can consider both local and global image features and shows excellent performance and efficiency, specifically on the infants' 3D brain T1w-MRI. It considerably reduces the requirement of labor in the traditional QC process.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords