Program Information
An Automated Technique to Measure CT Noise in Patient Images
O Christianson*, J Winslow, E Samei, Duke University Medical Center, Durham, NC
TU-C-103-10 Tuesday 10:30AM - 12:30PM Room: 103Purpose: Phantoms are routinely used for image quality assessment of CT scanners. The image quality in phantom images, however, may not be representative of the image quality in patient images due to differences in physical composition and scan parameters. Access to image quality for every scan conducted would facilitate standardization of image quality across the broad range of CT scanner models present at many medical centers. Similarly, this data would aid in matching image quality between institutions. Therefore, the goal of this work was to develop an automated technique capable of measuring the noise for every CT scan conducted at a major medical center.
Methods: The standard deviation was calculated by convolving the patient image with a 6mm square filter. Uniform regions of the patient anatomy were identified through histogram analysis and the standard deviations of the uniform regions were averaged to calculate the global noise of the image. The automated CT noise detection algorithm was applied to six clinical CT images. The results were compared to the noise measured by four observers placing ROIs in what they considered to be uniform sections of patient anatomy.
Results: There was a one-to-one relationship between the noise measured by the computer algorithm and the mean observer noise with an R2 of 0.98. The average difference between the computer and observer measurements (4.7%) was less than the inter-observer variability (4.8%). Running on a standard Lenovo Thinkpad T420 laptop, the computing time was less than 0.05 seconds per image.
Conclusion: The noise measured using this automated technique agrees well with human observer measurements. The speed of the calculation indicates that it may be applied to every patient scan to monitor image quality on a per scan basis facilitating quality control on a level previously unachievable.
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