Statistical characteristics of streak artifacts on CT images: Relationship between streak artifacts and mA s values

Abstract

The purpose of this study is to investigate how streak artifacts on computed tomography (CT) images vary with reduction in radiation doses by assessing the quantitative relationship between the streak artifacts and milliampere-time product (mA s) values. A commercially available chest phantom was used to measure the streak artifacts on the CT images obtained using a 4- and 16-multidetector-row helical CT scanners with various mA s values at a constant tube voltage of 120 kVp. The cardiac slice image was employed as a target image for evaluating the streak artifacts on the CT image. Eighty parallel line segments with a length of 20 pixels were placed perpendicular to numerous streak artifacts on the cardiac slice image, and the largest difference between adjacent CT values in each of the 80 CT-value profiles of these line segments was employed as a feature variable of streak artifacts; these feature variables have been analyzed by the extreme value theory. The largest difference between adjacent CT values in each CT-value profile can be statistically modeled by a Gumbel distribution. Further, the maximum level of streak artifacts on CT images that will be tolerated for clinical use and low-dose CT screening examination was expected to be estimated using the location parameter in the Gumbel distribution.

Development and applicability of a quality control phantom for dental cone‐beam CT

“…The AAV provides a relevant description of the net effect of streaks on image quality, but provides no information regarding beam hardening and photon starvation artifacts. Imai et al ( 20 ) proposed a statistical analysis of streak artifacts on CT images using the extreme value theory. This approach may provide a partial solution regarding a full artifact analysis for CBCT.…”

Dose reduction and image quality in CT angiography for cerebral aneurysm with various tube potentials and current settings

“…In our earlier investigations, the largest difference between adjacent CT numbers of one CT number profile was attributed to streak artefacts [11,13,14]. Thus, the largest difference between adjacent CT numbers was employed as a feature variable of streak artefacts.…”

“…Here, the largest difference between adjacent CT numbers was obtained from each of the CT number profiles. The 100 largest differences between adjacent CT numbers can be considered to be the largest values among a large set of independent and identically distributed random values, and can be modelled by a Gumbel distribution [11,[13][14][15]. The cumulative probability function of the Gumbel distribution, F(x), is expressed as:…”

“…These results showed that the CT number variations caused by the streak artefacts can be statistically modelled by a Gumbel distribution, which is one of the generalised extreme value distributions; based on these results, we devised a new method of detecting the streak artefacts on CT images, which we called the Gumbel evaluation method. The following is a brief description of this new evaluation method, adapted from our earlier study [11,[13][14][15]. The CT numbers along each line segment with a length of 60 pixels in the rectangular region measuring 606100 pixels in the axial CTA images were measured at 1-pixel intervals (i.e.…”

“…Because there is no quantitative descriptor of streak artefacts on CT images, we analysed the streak artefacts on CT images using the extreme value theory from our previous studies [11,[13][14][15]. These results showed that the CT number variations caused by the streak artefacts can be statistically modelled by a Gumbel distribution, which is one of the generalised extreme value distributions; based on these results, we devised a new method of detecting the streak artefacts on CT images, which we called the Gumbel evaluation method.…”