Med Phys. 2021 Jul 27. doi: 10.1002/mp.15133. Online ahead of print.
ABSTRACT
PURPOSE: The global noise (GN) algorithm has been previously introduced as a method for automatic noise measurement in clinical CT images. Accuracy of the GN algorithm has been assessed in abdomen CT exams, but not in any other body part until now. This work assesses the GN algorithm accuracy in automatic noise measurement in head CT exams.
METHODS: A publicly available image dataset of 99 head CT exams was used to evaluate the accuracy of the GN algorithm in comparison to reference noise values. Reference noise values were acquired using a manual noise measurement procedure. The procedure used a consistent instruction protocol and multiple observers to miti-gate the influence of intra- and inter-observer variation, resulting in precise reference values. Optimal GN algorithm parameter values were determined. The GN algorithm accuracy and the corresponding statistical confidence interval were determined. The GN measurements were compared across the 6 different scan protocols used in this dataset. The correlation of GN to patient head size was also assessed using a linear regression model, and the CT scanner’s x-ray beam quality was inferred from the model fit parameters.
RESULTS: Across all head CT exams in the dataset, the range of reference noise was 2.9 – 10.2 HU. A precision of ±0:33 HU was achieved in the reference noise measurements. After optimization, then GN algorithm had a RMS error 0.34 HU corresponding to a percent RMS error of 6.6%. The GN algorithm had a bias of +3.9%. Statistically significant differences in GN were detected in 11 out of the 15 different pairs of scan protocols. The GN measurements were correlated with head size with a statistically significant regression slope parameter (p < 10-7 ). The CT scanner x-ray beam quality estimated from the slope parameter was 3.5 cm water HVL (2.8{4.8 cm 95% C.I.).
CONCLUSION: The GN algorithm was validated for application in head CT exams. The GN algorithm was accurate in comparison to reference manual measurement, with errors comparable to inter-observer variation in manual measurement. The GN algorithm can detect noise differences in exams performed on different scanner models or using different scan protocols. The trend of GN across patients of different head sizes closely follows that predicted by a physical model of x-ray attenuation.
PMID:34314528 | DOI:10.1002/mp.15133
