LONI-QC

LONI-QC performs comprehensive quality control on multi-contrast, multi-modal neuroimaging data to compute QC metrics and support reproducible, standardized assessment across multi-site studies.

Key Features:

• Automated pre-processing workflow: Executes an automated pre-processing workflow that computes a comprehensive set of QC metrics and generates derived images.
• QC metrics (scalar and vector statistics): Computes both scalar and vector statistics for standardized quality assessment of imaging data.
• Parallel processing: Performs QC computations in parallel using a large compute cluster to enable scalability.
• Automated structural MRI QC: Provides an automated procedure for structural MRI that flags each QC metric as 'good' or 'bad.'
• Visual and automated QC: Supports both automated and visual QC procedures across multi-contrast and multi-modal brain imaging data.
• Validation and reproducibility: Demonstrated reproducibility on single- and multi-site datasets with sensitivity and specificity in identifying poor-quality images exceeding traditional visual inspection.

Scientific Applications:

• Multi-site neuroimaging studies (TRACK-TBI, ADNI): Applied to assess large neuroimaging datasets from multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI).
• Standardizing QC across centers: Provides a standardized approach to QC to maintain consistent data quality across multi-center neuroimaging research.

Methodology:

Imaging data are uploaded and ingested, undergo an automated pre-processing workflow that generates derived images, and compute scalar and vector statistics with parallel execution on a compute cluster as part of an exhaustive QC process.