WE-C-103-06: An Automated CT Quality Control Program.

PURPOSE: Daily image quality assessment is an essential part of a rigorous quality control program. Time constraints; however, make it difficult to fully analyze image quality daily. Therefore, the goal of this work was to develop a robust automated CT quality control program to extract meaningful image quality metrics including artifact analysis, noise texture measurement, and the calculation of a detectability index. METHODS: The ACR CT phantom was scanned on five different CT scanners. The automated algorithm was used to calculate the standard metrics including HU accuracy, CNR, noise, and uniformity as well as more advanced metrics including the MTF, NPS, detectability index, and artifact detection. To validate the automated program, the HU accuracy, CNR, and noise metrics were compared to measurements conducted by a human observer. Additionally, the 10th percentile of the MTF (MTF 10) was compared the high-contrast resolution and the detectability index was compared to the low-contrast detectability recorded by the human observer using the Spearman rank sum correlation. RESULTS: The HU, CNR, and noise determined by the automated algorithm agreed well with the human observer measurements (0.12%, 5.1%, and 7.1% difference for the HU, CNR, and noise respectively). Further, there was a strong correlation between MTF10 and the observer high-contrast resolution as well as the detectability index and the low-contrast detectability (rho=1 in both cases). CONCLUSION: There was a strong agreement between the results of the automated quality control program and the human observer measurements. Further, the Fourier based MTF and detectability index were found to correlate strongly with observer image quality assessment. Therefore, this automated quality control program offers a viable alternative for routine image quality assessment of the ACR phantom.

Duke Scholars

Author Justin Solomon Radiology

Author Ehsan Samei Radiology