An automated technique for global noise level measurement in CT image with a conjunction of image gradient.

Automated assessment of noise level in clinical CT images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10x10 pixel for calculating HU standard deviation (SD) and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson's tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had similar a shape with a close GNI value. In patient's CT image, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the CT scan range with high heterogeneous tissue. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI vs different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom. In other words, shaper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT image with either a smooth or grainy appearance. The study's results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control.