alps-qc-vision-reviewer

Automated visual quality control for ALPS DTI figures is achieved by applying visual analysis to detect normalization errors, artifacts, and ROI misplacements. This process flags problematic session images for human review.

Visual artifacts in DTI QC images that can be detected include y_drift, noisy DWI, and boundary artifacts. The visual analysis identifies these specific failure modes through visual cues in the session images.

To review DTI QC sessions with high Mahalanobis distance, select those specific sessions for detailed inspection. The visual analysis targets these flagged images to assess normalization and ROI placement accuracy beyond numeric metrics.

Visual analysis can detect ROI misplacements in neuroimaging figures by assessing anatomical alignment errors. It evaluates ROI placement accuracy to ensure flagged images meet quality standards for reproducible results.

Visual inspection for neuroimaging is needed when large datasets require detailed visual checks beyond numeric metrics. This approach catches anatomical alignment errors and artifacts that are difficult to detect numerically.

The limitation of automated visual analysis for DTI figures is that it assists quality assurance workflows but does not replace human judgment. Flagged images with potential issues still require further human review to confirm quality standards.