Size and volume of kidney stones in computed tomography: Influence of acquisition techniques and image reconstruction parameters.

European journal of radiology(2020)

引用 17|浏览15
暂无评分
摘要
PURPOSE:Computed tomography (CT) is routinely used to assess suspected urolithiasis. Information obtained from CT include presence, location and size of stones, with the latter frequently determining treatment strategy. While there is consensus regarding measurements procedures of kidney stones, influence of radiation dose and reconstruction techniques on stone measurements are unknown. The purpose of this study was to systematically evaluate the influence of these technical determinants on kidney stone size measurements. METHOD:47 kidney stones of different composition were scanned using a 64-row-multi-detector CT in a 3D-printed, semi-anthropomorphic phantom. Reference stone sizes were measured manually with a digital caliper (Man-M). Stones were imaged with 2 and 10 mGy CTDI. Images were reconstructed using filtered-back-projection, hybrid-iterative and model-based-iterative reconstruction algorithms (FBP, HIR, MBIR) in combination with different kernels and denoising levels. All stones underwent semi-automatic, threshold-based segmentation for computation of maximum diameter and volume. Statistics were conducted using ANOVA ± correction for multiple comparisons. RESULTS:Overall stone size as compared to manual measurements was overestimated in CT (10.0 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05) yet showing a good correlation (R2 = 0.66). Radiation dose and denoising levels did not significantly influence measurements (p > 0.05). MBIR and sharp kernels showed closest agreement with Man-M (9.3 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05). Differences within single stones were as high as 40 % (e.g. Man-M: 5.9 mm, CT: 7.3-12.0 mm). CONCLUSIONS:CT-based measurements of kidney stone size appear unaffected by radiation dose and denoising technique, whereas reconstruction algorithms and kernels demonstrate a relevant impact on size measurements. Smallest differences were found using MBIR with a sharp kernel.
更多
查看译文
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