Automated Technique to Measure Noise in Clinical CT Examinations

The purpose of this study was to develop and validate an automated method to measure noise in clinical CT examinations.

An automated algorithm was developed to measure noise in CT images. To assess its validity, the global noise level was compared with image noise measured using an image subtraction technique in an anthropomorphic phantom. The global noise level was further compared with image noise values from clinical patient CT images obtained by an observer study. Finally, the clinical utility of the global noise level was shown by assessing variability of image noise across scanner models for abdominopelvic CT examinations performed in 2358 patients.

The global noise level agreed well with the phantom-based and clinical image-based noise measurements, with an average difference of 3.4% and 4.7% from each of these measures, respectively. No significant difference was detected between the global noise level and the validation dataset in either case. It further indicated differences across scanners, with the median global noise level varying significantly between different scanner models (15-35%).

The global noise level provides an accurate, robust, and automated method to measure CT noise in clinical examinations for quality assurance programs. The significant difference in noise across scanner models indicates the unexploited potential to efficiently assess and subsequently improve protocol consistency. Combined with other automated characterization of imaging performance (e.g., dose monitoring), the global noise level may offer a promising platform for the standardization and optimization of CT protocols.