Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets.

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 visuali

SUBMITTER: Kim ME 

PROVIDER: S-EPMC12316263 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature