Year: 2023
Type: preprint
Abstract: Despite substantial efforts toward improving the tools to carry out the visual assessment of quality, as well as automation, the quality control (QC) of imaging data remains an onerous, yet critical step of analysis workflows, especially within large-scale studies. Indeed, the reliability and reproducibility of results can be improved by implementing QC checkpoints throughout the workflow (Niso et al. 2022, Provins et al. 2023). Here, we introduce Q’kay, a web service to deploy rigorous QC protocols on large datasets leveraging the individual reports generated by tools like MRIQC (Esteban et al., 2017) and fMRIPrep (Esteban et al., 2019).
Authors: Elodie Savary, Céline Provins, Thomas Sanchez, Oscar Esteban
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: National Science Foundation, École Polytechnique Fédérale de Lausanne, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Université de Genève, Université de Lausanne, Centre d'Imagerie BioMédicale, Centre Hospitalier Universitaire Vaudois, National Institute of Mental Health
Awards: Uncovering the interplay of structure, function, and dynamics of brain connectivity using MRI, NIPreps: integrating neuroimaging preprocessing workflows across modalities, populations, and species, NCCR 'SYNAPSY - The synaptic bases of mental diseases', Multicentric study of Fetal Abnormal Cortical Trajectory with standardised and privacy-preserving method on fetal MRI
OpenAlex ID: W4317396392