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Performance evaluation of multi-material electronic cleansing for ultra-low-dose dual-energy CT colonography

机译:超低剂量双能CT结肠扫描的多材料电子清理性能评价

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Accurate electronic cleansing (EC) for CT colonography (CTC) enables the visualization of the entire colonic surface without residual materials. In this study, we evaluated the accuracy of a novel multi-material electronic cleansing (MUMA-EC) scheme for non-cathartic ultra-low-dose dual-energy CTC (DE-CTC). The MUMA-EC performs a water-iodine material decomposition of the DE-CTC images and calculates virtual monochromatic images at multiple energies, after which a random forest classifier is used to label the images into the regions of lumen air, soft tissue, fecal tagging, and two types of partial-volume boundaries based on image-based features. After the labeling, materials other than soft tissue are subtracted from the CTC images. For pilot evaluation, 384 volumes of interest (VOIs), which represented sources of subtraction artifacts observed in current EC schemes, were sampled from 32 ultra-low-dose DE-CTC scans. The voxels in the VOIs were labeled manually to serve as a reference standard. The metric for EC accuracy was the mean overlap ratio between the labels of the reference standard and the labels generated by the MUMA-EC, a dual-energy EC (DE-EC), and a single-energy EC (SE-EC) scheme. Statistically significant differences were observed between the performance of the MUMA/DE-EC and the SE-EC methods (p<0.001). Visual assessment confirmed that the MUMA-EC generated less subtraction artifacts than did DE-EC and SE-EC. Our MUMA-EC scheme yielded superior performance over conventional SE-EC scheme in identifying and minimizing subtraction artifacts on non-cathartic ultra-low-dose DE-CTC images.
机译:用于CT上影(CTC)的精确的电子清洁(EC)使得能够在没有残留材料的情况下可视化整个结肠表面。在这项研究中,我们评估了用于非泻药超低剂量双能CTC(DE-CTC)的新型多材料电子清洁(Muma-EC)方案的准确性。 MUMA-EC对DE-CTC图像进行水碘材料分解,并在多个能量下计算虚拟单色图像,之后使用随机林分类器将图像标记为内腔空气,软组织,粪便标记的区域以及基于基于图像的特征的两种类型的局部跨界边界。在标记之后,从CTC图像中减去软组织以外的材料。对于导频评估,从32个超低剂量DE-CTC扫描中取样了384体积的兴趣(vois),其中代表了在目前的EC方案中观察到的减法伪影子。 VoIS中的体素被手动标记为参考标准。 EC精度的度量是参考标准标签和Muma-EC,双能EC(DE-EC)和单能EC(SE-EC)方案产生的标签之间的平均重叠比。 。在MUMA / DE-EC和SE-EC方法的性能之间观察到统计学显着的差异(P <0.001)。视觉评估证实,MUMA-EC产生的减法伪像比DE-EC和SE-EC更少。我们的Muma-EC方案在鉴定和最小化非泻药超低剂量DE-CTC图像上识别和最小化减法伪像来产生优异的性能。

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