Dupuis Andrew, Boyacioglu Rasim, Keenan Kathryn E, Griswold Mark A
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Department of Radiology, Case Western Reserve University, Cleveland, OH, USA.
MAGMA. 2024 Oct 3. doi: 10.1007/s10334-024-01205-3.
This work presents an automated quality control (QC) system within quantitative MRI (qMRI) workflows. By leveraging the ISMRM/NIST quantitative MRI system phantom, we establish an open-source pipeline for rapid, repeatable, and accurate validation and stability tracking of sequence quantification performance across diverse clinical settings.
A microservice-based QC system for automated vial segmentation from quantitative maps was developed and tested across various MRF acquisition and protocol designs, with reports generated and returned to the scanner in real time.
The system demonstrated consistent and repeatable value segmentation and reporting, successfully extracted all 252 T1 and T2 vial samples tested. Values extracted from the same sequence were found to be repeatable with 0.09% ± 1.23% and - 0.26% ± 2.68% intersession error, respectively.
By providing real-time quantification performance assessment, this easily deployable automated QC approach streamlines sequence validation and long-term performance monitoring, vital for the broader acceptance of qMRI as a standard component of clinical protocols.
本研究展示了定量磁共振成像（qMRI）工作流程中的自动化质量控制（QC）系统。通过利用国际磁共振医学学会（ISMRM）/美国国家标准与技术研究院（NIST）的定量MRI系统体模，我们建立了一个开源管道，用于在不同临床环境中对序列定量性能进行快速、可重复且准确的验证和稳定性跟踪。
开发了一种基于微服务的QC系统，用于从定量图中自动分割样本瓶，并在各种磁共振弹性成像（MRF）采集和协议设计中进行了测试，实时生成报告并返回给扫描仪。
该系统展示了一致且可重复的值分割和报告，成功提取了测试的所有252个T1和T2样本瓶。从相同序列中提取的值在不同扫描之间的误差分别为0.09%±1.23%和-0.26%±2.68%，具有可重复性。
通过提供实时定量性能评估，这种易于部署的自动化QC方法简化了序列验证和长期性能监测，这对于qMRI作为临床方案的标准组成部分被更广泛接受至关重要。
