ArticlePloS one2025
Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Charting confidence in white matter brain charts: enabling study planning through stability validation.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Enhancing reproducibility in neuroimaging with containerized archival.Brain imaging and behavior · 2026Article
- A geometric-to-neural cascade for cerebral microbleed detection in susceptibility-weighted MRI.bioRxiv : the preprint server for biology · 2026Article
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- Automated Segmentation of Brainstem and Subcortical White Matter: Mapping the Deep Tegmental Core with BundleParc.bioRxiv : the preprint server for biology · 2026Article
- Lifespan Trajectories of Asymmetry in White Matter Tracts.Human brain mapping · 2026Article
- Modeling a Shared Reality of Tractography through Varied Structural Imaging.bioRxiv : the preprint server for biology · 2026Article
- Characterizing Continuous and Discrete Hybrid Latent Spaces for Structural Connectomes.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Phenotype discovery of traumatic brain injury segmentations from heterogeneous multi-site data.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Assessing Open-world Foundation Models for Zero-shot Skin Segmentation in Clinical Dermatological Photographs.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Lifespan Pancreas Morphology for Control Versus Type 2 Diabetes Using AI on Largescale Clinical Imaging.Clinical anatomy (New York, N.Y.) · 2026Article
- Modeling a Shared Reality of Tractography through Varied Structural Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Harmonizing the National Lung Screening Trial: Kernel resolution and field of view compensation.PloS one · 2026Article
- Data-driven abdominal phenotypes of type 2 diabetes in lean, overweight, and obese cohorts from computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Harmonizing 10,000 connectomes: site-invariant representation learning for multi-site analysis of network connectivity and cognitive impairment.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Lifespan Trajectories of Asymmetry in White Matter Tracts.bioRxiv : the preprint server for biology · 2025Article
- White Matter Abnormalities and Cognition in Aging and Alzheimer Disease.JAMA neurology · 2025Article
- White matter microstructure and macrostructure brain charts across the human lifespan.bioRxiv : the preprint server for biology · 2025Article
- Data quality biases normative models derived from fetal brain MRI.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
20 authors.
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Abstract
Thorough quality control (QC) can be time consuming when working with large-scale medical imaging datasets, yet necessary, as poor-quality data can lead to erroneous conclusions or poorly trained machine learning models. Most efforts to reduce data QC time rely on quantitative outlier detection, which cannot capture every instance of algorithm failure. Thus, there is a need to visually inspect every output of data processing pipelines in a scalable manner. We design a QC pipeline that allows for low time cost and effort across a team setting for a large database of diffusion-weighted and structural magnetic resonance images. Our proposed method satisfies the following design criteria: 1.) a consistent way to perform and manage quality control across a team of researchers, 2.) quick visualization of preprocessed data that minimizes the effort and time spent on the QC process without compromising the condition/caliber of the QC, and 3.) a way to aggregate QC results across pipelines and datasets that can be easily shared. In addition to meeting these design criteria, we also provide a comparison experiment of our method to an automated QC method for a T1-weighted dataset of [Formula: see text] images and an inter-rater variability experiment for several processing pipelines. The experiments show mostly high agreement among raters and slight differences with the automated QC method. While researchers must spend time on robust visual QC of data, there are mechanisms by which the process can be streamlined and efficient.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.