Diffusion MRI Quality Control for Reliable Tractometry Analysis

The patent segments the complex dMRI analysis process into multiple distinct QC stages: brain-tissue segmentation QC, dMRI processing QC, and tractometry QC. Each stage validates specific parameters (e.g., segmentation accuracy, diffusion tensor metrics, bundle geometry) independently, allowing precise identification and correction of errors at each step, thereby improving overall measurement precision while managing analytical complexity.

The patent introduces an intermediary automatic QC system that acts as a mediator between raw dMRI data and final diagnostic analysis. This QC pipeline includes intermediate validation steps such as checking segmentation masks, verifying diffusion tensor imaging metrics, and validating tractometry results, which bridge the gap between complex imaging data and reliable quantitative measurements of white-matter microstructure.

The patent performs preliminary quality control validations at each processing stage before proceeding to the next. Brain-tissue segmentation is validated immediately after generation, dMRI processing parameters are checked before tractometry, and bundle results are verified before final analysis. This preliminary action approach prevents error propagation and reduces the need for reprocessing, ultimately saving time despite the additional validation steps.

The patent implements parameter-based validation thresholds for each QC stage (e.g., acceptable ranges for diffusion tensor metrics, orientation distribution function parameters, bundle volume and shape metrics). By automatically comparing processed parameters against these predefined thresholds, the system efficiently determines pass/fail outcomes without requiring time-consuming manual review, thus maintaining high accuracy while minimizing processing time loss.