A phantom study comparing low-dose CT physical image quality from five different CT scanners

Yali Li; Yaojun Jiang; Huilong Liu; Xi Yu; Sihui Chen; Duoshan Ma; Jianbo Gao; Yan Wu

doi:10.21037/qims-21-245

A phantom study comparing low-dose CT physical image quality from five different CT scanners

Quant Imaging Med Surg. 2022 Jan;12(1):766-780. doi: 10.21037/qims-21-245.

Authors

Yali Li^#¹, Yaojun Jiang^#¹, Huilong Liu¹, Xi Yu¹, Sihui Chen¹, Duoshan Ma¹, Jianbo Gao¹, Yan Wu¹

Affiliation

¹ Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

^# Contributed equally.

Abstract

Background: To systematically evaluate the physical image quality of low-dose computed tomography (LDCT) on CT scanners from 5 different manufacturers using a phantom model.

Methods: CT images derived from a Catphan 500 phantom were acquired using manufacturer-specific iterative reconstruction (IR) algorithms and deep learning image reconstruction (DLIR) on CT scanners from 5 different manufacturers and compared using filtered back projection with 2 radiation doses of 0.25 and 0.75 mGy. Image high-contrast spatial resolution and image noise were objectively characterized by modulation transfer function (MTF) and noise power spectrum (NPS). Image high-contrast spatial resolution and image low-contrast detectability were compared directly by visual evaluation. CT number linearity and image uniformity were compared with intergroup differences using one-way analysis of variance (ANOVA).

Results: The CT number linearity of 4 insert materials were as follows: acrylic (95% CI: 120.35 to 121.27; P=0.134), low-density polyethylene (95% CI: -98.43 to -97.43; P=0.070), air (95% CI: -996.16 to -994.51; P=0.018), and Teflon (95% CI: 984.40 to 986.87; P=0.883). The image uniformity values of GE Healthcare (95% CI: 3.24 to 3.83; P=0.138), Philips (95% CI: 2.62 to 3.70; P=0.299), Siemens (95% CI: 2.10 to 3.59; P=0.054), Minfound (95% CI: 2.35 to 3.65; P=0.589), and Neusoft (95% CI: 2.63 to 3.37; P=0.900) were evaluated and found to be within ±4 Hounsfield units (HU), with a range of 0.99-2.76 HU for standard deviations. There was no statistically significant difference in CT number linearity and image uniformity across the 5 CT scanners under different radiation doses with IR and DLIR algorithms (P>0.05). The resolution level at 10% MTF was 6.98 line-pairs-per-centimeter (lp/cm) on average, which was similar to the subjective evaluation results (mostly up to 7 lp/cm). DLIR at all 3 levels had the highest 50% MTF values among all reconstruction algorithms. For image low-contrast detectability, the minimum diameter of distinguishable contrast holes reached 4 mm at a 0.5% resolution. Increasing the radiation dose and IR strength reduced the image noise and NPS curve peak frequency while improving image low-contrast detectability.

Conclusions: This study demonstrated that the image quality of CT scanners from 5 different manufacturers in LDCT is comparable and that the CT number linearity is unbiased and can contribute to accurate bone mineral density quantification.

Keywords: Low-dose computed tomography (LDCT); deep learning; image quality; iterative reconstruction (IR); phantom; quantitative computed tomography (QCT).