Advanced Mobile-Enabled CT Quality Control Server
Abstract
Purpose
To develop an automated CT quality control (QC) query system that retrieves, analyzes, and trends QC data and enables image artifact review across iPhone, iPad, and Computer platforms for 71 GE and Siemens CT scanners spanning nuclear medicine, interventional radiology, and diagnostic radiology within a hospital network. This development eliminates the major bottleneck of “phone tagging” between physicists and technologists when Siemens CT QC data are not manually submitted to the server.
Methods
The hospital network comprises 71 CT scanners from two vendors of GE and Siemens, including 14 SPECT/CT, 11 PET/CT, 12 interventional radiology CT, and 34 diagnostic radiology CT systems across five geographic locations. An automated query system was developed to retrieve QC data from all scanners. The system centers QC phantom images, detects air bubbles in the QC phantom, calculates mean CT number, image uniformity, and image noise against specifications, and assigns a pass/fail status to each scanner. This design eliminates the need for manual transfer of Siemens QC data and improves the efficiency of QC review. The platform provides longitudinal trending of CT number measurements and enables CT artifact review on mobile devices (iPhone and iPad). The tool is built on open-source Ubuntu and Python and is scalable to accommodate additional scanners.
Results
The system has been in clinical use for over two years and has effectively eliminated “phone tagging” between technologists and physicists during daily QC review when QC data are not manually submitted. Mobile review of CT image artifacts on iPhone and iPad is a highly valued feature that is frequently used by physicists.
Conclusion
An automated CT QC data retrieval, analysis, trending, and artifact review system for 71 CT scanners has been successfully implemented across mobile and computer platforms, streamlining morning QA and eliminating phone tagging due to missing Siemens QC data.