MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites

Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, it is unclear that solutions can generalize to unseen data acquired at new sites. Here, we introduce the MRI Quality Control tool (MRIQC), a tool for extracting quality measures and fitting a binary (accept/exclude) classifier. Our tool can be run both locally and as a free online service via the OpenNeuro.org portal. The classifier is trained on a publicly available, multi-site dataset (17 sites, N = 1102). We…

• NS
  National Science Foundation
• LA
  Laura and John Arnold Foundation
• SN
  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

  Awards: 158831, 163859