Large-Scale Deployment and Analytical Implications of Structured Quality Control in Diffusion Magnetic Resonance Imaging
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arXiv
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2026
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| author | Kim, Michael E. Gao, Chenyu Ramadass, Karthik Rudravaram, Gaurav McMaster, Elyssa M. Saunders, Adam M. Yang, Yisu Levy, Elias Kanakaraj, Praitayini Newlin, Nancy R. Li, Zhiyuan Khairi, Nazirah Mohd Dewey, Blake E. Team, The HABS-HD Study Initiative, Alzheimer's Disease Neuroimaging Schilling, Kurt G. Archer, Derek Hohman, Timothy J. Landman, Bennett A. Liu, Yihao |
| author_facet | Kim, Michael E. Gao, Chenyu Ramadass, Karthik Rudravaram, Gaurav McMaster, Elyssa M. Saunders, Adam M. Yang, Yisu Levy, Elias Kanakaraj, Praitayini Newlin, Nancy R. Li, Zhiyuan Khairi, Nazirah Mohd Dewey, Blake E. Team, The HABS-HD Study Initiative, Alzheimer's Disease Neuroimaging Schilling, Kurt G. Archer, Derek Hohman, Timothy J. Landman, Bennett A. Liu, Yihao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21799 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Large-Scale Deployment and Analytical Implications of Structured Quality Control in Diffusion Magnetic Resonance Imaging Kim, Michael E. Gao, Chenyu Ramadass, Karthik Rudravaram, Gaurav McMaster, Elyssa M. Saunders, Adam M. Yang, Yisu Levy, Elias Kanakaraj, Praitayini Newlin, Nancy R. Li, Zhiyuan Khairi, Nazirah Mohd Dewey, Blake E. Team, The HABS-HD Study Initiative, Alzheimer's Disease Neuroimaging Schilling, Kurt G. Archer, Derek Hohman, Timothy J. Landman, Bennett A. Liu, Yihao Image and Video Processing 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. |
| title | Large-Scale Deployment and Analytical Implications of Structured Quality Control in Diffusion Magnetic Resonance Imaging |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2605.21799 |