Large-Scale Deployment and Analytical Implications of Structured Quality Control in Diffusion Magnetic Resonance Imaging

TL;DR

This study demonstrates the importance and feasibility of large-scale, structured quality control in diffusion MRI processing to ensure valid and interpretable quantitative results across extensive datasets.

Contribution

It introduces a systematic QC framework applied to over 18,000 scans, revealing failure modes and emphasizing the need for hierarchical QC in dMRI pipelines.

Findings

Visual QC can detect upstream failures not evident in aggregate metrics.

Failure detection requires pipeline-wide inspection due to algorithm-specific failure characteristics.

Large-scale structured QC improves the validity of diffusion MRI analyses.

Abstract

Purpose: Diffusion MRI (dMRI) provides a diverse set of quantitative measures and derived datatypes to assess white matter microstructure and macrostructure. Coupled with the increasing size of imaging studies using dMRI, the number of downstream outputs requiring quality control (QC) will continue to grow. Previous work has shown that failure modes which are often not evident from aggregate metrics or summary statistics can be identified through structured visual inspection. This work aims to better understand common failure modes and the expected characteristics of valid dMRI processing outputs to ensure the validity and interpretability of quantitative findings. Approach: We deployed a structured QC framework to assess 18,328 dMRI scans across nine datasets, visually evaluating the outputs of seven processing pipelines representative of conventional dMRI analyses. Results: Downstream outputs that pass visual QC may still rely on failed upstream dependencies; such failures may only be visually detectable through systematic inspection of the full pipeline hierarchy. Additionally, appropriate QC granularity is algorithm-specific, as the spatial structure of each algorithm's outputs determines whether failures warrant selective or global exclusion. Conclusion: This work demonstrates the feasibility and analytical value of large-scale, structured QC for dMRI processing pipelines. Our results highlight the need for systematic QC spanning the full processing hierarchy to ensure the validity and interpretability of quantitative findings.