Summary
MRI scans need careful quality checks to ensure accurate medical imaging, but doing this manually takes time and can be inconsistent. A tool called MRIQC automates this with computer processing but usually needs complex setup and powerful computers. The authors created WebMRIQC, a version that runs entirely in a web browser and requires no installation, making it easier for places with limited computing resources to use. They tested it on different brain MRI datasets and found it produces quality assessments that closely match the original MRIQC tool. This makes it simpler for clinics and research sites with less tech support to perform important MRI quality control.
What this means in practice
- •For clinic imaging teams: Perform standardized MRI quality checks directly through a web browser without needing high-performance local computers or software installation.
- •For radiology service providers: Provide centralized MRI quality assessment services that support remote or under-resourced sites by running WebMRIQC pipelines on shared servers.$Commercial implications: Enables offering cloud-based MRI QC services improving access for clinics lacking local IT infrastructure.
Authors
Philip Nkwam, Ifeoluwa Oladeji, Sekinat Zurakat-Aderibigbe, Jasmine Cakmak, Harrison Aduluwa, Confidence Raymond, Cliff Mokua, Abdulrazaq Zubair, Daniel Champanda, Tolulope Olusuyi, Maruf Adewole, Udunna Anazodo
Abstract
Reliable quality control (QC) of magnetic resonance imaging (MRI) is essential for reliable diagnostic neuroimaging, yet standard manual assessment is subjective and time-consuming. MRIQC has established standardized automated extraction of image-quality metrics (IQMs), but its reliance on local computational imaging skills and capacity including high-performance computing, limits its adoption in resource-constrained settings (RCS). We present WebMRIQC (webmriqc.mailab.io), an open-source browser-based platform that wraps the validated MRIQC engine behind a zero-installation web interface. WebMRIQC automates the DICOM-to-BIDS conversion of de-identified MRI scans, executes the unmodified containerized MRIQC pipeline on a shared compute node governed by a fair-share job queue, and returns an interactive in-browser dashboard. The dashboard grounds every IQM in published quality thresholds, benchmarks each scan against the normative distribution of high-resource open datasets, and supports cross-site multicentre implementation of optimized scan protocols in RCS.We describe the system architecture and a validation framework establishing measurement equivalence between WebMRIQC and native MRIQC across thirteen IQMs on the BraTS-Africa and BraTS 2021 datasets. Preliminary results indicate strong agreement for contrast-, signal and noise-based metrics, demonstrating that web-based implementation lowers the barrier to standardized MRI QC and provides a foundation for harmonized, regionally adapted quality benchmarks across RCS imaging sites. The code is publicly available here https://github.com/CAMERA-MRI/WebMRIqc.