ArticleProceedings of SPIE--the International Society for Optical Engineering2025
Scalable, reproducible, and cost-effective processing of large-scale medical imaging datasets.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 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
- The role of vascular risk factors in white matter tract microstructure: a multi-cohort study in older adults.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Article
- Genetic architecture of the limbic white matter microstructure in aging and Alzheimer's Disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Lifespan Trajectories of Asymmetry in White Matter Tracts.Human brain mapping · 2026Article
- Article
- 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
- Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts.bioRxiv : the preprint server for biology · 2026Article
- EVALUATION OF NEUROCOMBAT AND DEEP LEARNING HARMONIZATION FOR MULTI-SITE MAGNETIC RESONANCE NEUROIMAGING IN YOUTH WITH PRENATAL ALCOHOL EXPOSURE.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Modeling a Shared Reality of Tractography through Varied Structural Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- 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
- The effect of Alzheimer's disease genetic factors on limbic white matter microstructure.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Curating, processing, and combining large-scale medical imaging datasets from national studies is a non-trivial task due to the intense computation and data throughput required, variability of acquired data, and associated financial overhead. Existing platforms or tools for large-scale data curation, processing, and storage have difficulty achieving a viable cost-to-scale ratio of computation speed for research purposes, either being too slow or too expensive. Additionally, management and consistency of processing large data in a team-driven manner is a non-trivial task. We design a BIDS-compliant method for an efficient and robust data processing pipeline of large-scale diffusion-weighted and T1-weighted MRI data compatible with low-cost, high-efficiency computing systems. Our method accomplishes automated querying of data available for processing and process running in a consistent and reproducible manner that has long-term stability, while using heterogenous low-cost computational resources and storage systems for efficient processing and data transfer. We demonstrate how our organizational structure permits efficiency in a semi-automated data processing pipeline and show how our method is comparable in processing time to cloud-based computation while being almost 20 times more cost-effective. Our design allows for fast data throughput speeds and low latency to reduce the time for data transfer between storage servers and computation servers, achieving an average of 0.60 Gb/s compared to 0.33 Gb/s for using cloud-based processing methods. The design of our workflow engine permits quick process running while maintaining flexibility to adapt to newly acquired data.
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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.