发表机构
University of Lagos; McGill University; Jomo Kenyatta University of Agriculture and Technology; Federal University of Health Sciences, Azare; Muhimbili Orthopaedic Institute; Crestview Radiology Ltd.（拉各斯大学; 麦吉尔大学; 乔莫·肯雅塔农业技术大学; 阿扎雷联邦健康科学大学; 穆希比利骨科研究所; 克雷斯特维尤放射有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
WebMRIQC是一个开源浏览器平台，封装MRIQC引擎，实现零安装的MRI图像质量评估，在资源受限环境中通过共享计算节点和交互式仪表板降低标准化QC门槛，并在BraTS数据集上验证了与原生MRIQC的测量等效性。
AI 中文摘要
磁共振成像（MRI）的可靠质量控制（QC）对于可靠的诊断性神经影像学至关重要，然而标准的人工评估主观且耗时。MRIQC已建立了标准化的图像质量指标（IQMs）自动提取方法，但其依赖于本地计算影像学技能和包括高性能计算在内的计算能力，限制了其在资源受限环境（RCS）中的采用。我们提出了WebMRIQC（此HTTP URL），一个开源的基于浏览器的平台，它将经过验证的MRIQC引擎封装在零安装的Web界面之后。WebMRIQC自动化了去标识化MRI扫描的DICOM到BIDS转换，在由公平共享作业队列管理的共享计算节点上执行未经修改的容器化MRIQC流程，并返回交互式浏览器内仪表板。该仪表板将每个IQM基于已发表的质量阈值，将每次扫描与高资源开放数据集的规范分布进行基准比较，并支持在此HTTP URL中描述的跨站点多中心优化扫描协议的实施。我们描述了系统架构和验证框架，该框架在BraTS-Africa和BraTS 2021数据集上建立了WebMRIQC与原生MRIQC在十三个IQMs上的测量等效性。初步结果表明，基于对比度、信号和噪声的指标具有高度一致性，证明基于Web的实现降低了标准化MRI QC的门槛，并为跨RCS影像站点的协调、区域适应性质量基准提供了基础。代码可在此HTTPS URL公开获取。
英文摘要
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.
CommentsMICCAI CONFERENCE- MIRASOL 2026
