Visual impression and texture analysis of advanced modeled iterative reconstruction (ADMIRE): improved assessment of image quality in CT for better estimation of dose reduction potential

Journal of radiological protection : official journal of the Society for Radiological Protection(2023)

引用 0|浏览10
暂无评分
摘要
To evaluate the image quality (IQ) of advanced modeled iterative reconstruction (ADMIRE; Siemens Healthcare, Forchheim, Germany) applying image texture and image visual impression as a supplement to physical parameters such as noise level and spatial resolution. An ACR-phantom with four modules was examined at different radiation dose levels. To characterise the image texture, two Haralick texture parameters, contrast and entropy, were assessed at different dose levels and reconstruction algorithms. The visual impression of images and the low-contrast detectability were evaluated by the structural similarity index (SSIM). The spatial resolution was determined by the modulation transfer functions and the line spread function. The Haralick texture parameters, contrast and entropy, decreased with increasing ADMIRE levels. ADMIRE III, IV and V offered a comparable contrast and entropy to those calculated by filtered back projection (FBP) with a radiation dose reduction up to 50%. SSIM (low-contrast detectability) improved with increasing ADMIRE levels. SSIM calculated by ADMIRE IV and V revealed comparable IQ to FBP with a decreased CTDIvol up to 50%. Spatial resolution was retained up to 90% dose reduction. Compared to FBP at the same dose level, the image noise decreased up to 61% with higher ADMIRE levels (& sigma; (FBP) = 17.3 HU and & sigma; (ADMIRE V ) = 10.6 HU at 6.65 mGy). Taking texture analysis and visual perception into account, a more realistic assessment of the dose reduction potential of ADMIRE can be achieved than quality metrics based alone on physical measurements.
更多
查看译文
关键词
ADMIRE,image texture analysis,image visual impression,low-contrast detectability,Haralick texture parameters,structural similarity
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要