Program Information

Automated CT QC Program with Analytics, Archival, and Notification Capabilities

J Winslow*, O Christianson , E Samei , Duke University Medical Center, Durham, NC

Presentations

SU-F-18C-7 Sunday 4:00PM - 6:00PM Room: 18C

Purpose: Tracking metrics over time is a well-established means of establishing a quality control program. The number of metrics followed and testing frequency is limited by available resources. Automating the image analysis and data archival of a QC program enables objective and efficient tracking of performance metrics. The purpose of this study was to develop such a QC method and to assess its utility at a large clinical facility.

Methods: The QC program at our institution is based on the acquisition of daily water-phantom scans, and biweekly ACR-phantom scans for each CT system. We developed a QC program to analyze these data. The QC software operates on the images sent directly to our server. The relevant information from DICOM headers was extracted, data analyzed, and a database was populated. The measurements performed on the water-phantom included water CT-number, uniformity, noise, and artifact. The measurements performed on the ACR-phantom included the MTF, NPS, detectability, artifact, uniformity, CNR, and the CT-numbers for water, polyethylene, bone, air, and acrylic. Email notifications and criteria limits were directly based upon ACR accreditation requirements and developing threshold values.

Results: Across ten clinical CT scanners, the daily water CT-number was -0.2+/-1.4 HU(mean+/-standard deviation). The corresponding numbers for 10%MTF, uniformity, CNR squared normalized to CTDI, and detectability squared normalized to CTDI were 0.69+/-0.01 (1/mm), 0.93+/-0.29, 0.06+/-0.02 (1/mGy), and 3.3+/-0.7 (1/mGy), respectively. For all ACR-phantom inserts, the largest standard deviation for any individual scanner was 1.9 HU. Artifact analysis triggers successfully identified problematic images.

Conclusion: Automating image analysis allows one to frequently track meaningful metrics that would be impractical to follow otherwise. System inconsistencies are more likely to be identified and corrected earlier. Much tighter system specific criteria limits are possible.

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