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首页> 外文会议>Conference on Computer-Aided Diagnosis >Detection of a slow-flow component in contrast-enhanced ultrasound of the synovia for the differential diagnosis of arthritis
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Detection of a slow-flow component in contrast-enhanced ultrasound of the synovia for the differential diagnosis of arthritis

机译：滑膜造影增强超声中慢流成分的检测，用于关节炎的鉴别诊断

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摘要

Contrast Enhanced Ultrasound (CEUS) is a sensitive imaging technique to assess tissue vascularity, that can be useful in the quantification of different perfusion patterns. This can particularly important in the early detection and differentiation of different types of arthritis. A Gamma-variate can accurately quantify synovial perfusion and it is flexible enough to describe many heterogeneous patterns. However, in some cases the heterogeneity of the kinetics can be such that even the Gamma model does not properly describe the curve, especially in presence of recirculation or of an additional slow-flow component. In this work we apply to CEUS data both the Gamma-variate and the single compartment recirculation model (SCR) which takes explicitly into account an additional component of slow flow. The models are solved within a Bayesian framework. We also employed the perfusion estimates obtained with SCR to train a support vector machine classifier to distinguish different types of arthritis. When dividing the patients into two groups (rheumatoid arthritis and polyarticular RA-like psoriatic arthritis vs. other arthritis types), the slow component amplitude was significantly different across groups: mean values of a_1 and its variability were statistically higher in RA and RA-like patients (131% increase in mean, p = 0.035 and 73% increase in standard deviation, p = 0.049 respectively). The SVM classifier achieved a balanced accuracy of 89%, with a sensitivity of 100% and a specificity of 78%.
机译：对比增强超声（CEUS）是一种评估组织血管的敏感成像技术，可用于量化不同的灌注模式。这对于早期检测和区分不同类型的关节炎尤其重要。伽玛变量可以准确地量化滑膜灌注，并且足够灵活以描述许多不同类型的模式。但是，在某些情况下，动力学的异质性可能会导致即使Gamma模型也无法正确描述曲线，特别是在存在再循环或其他缓慢流动成分的情况下。在这项工作中，我们将伽玛变量和单室再循环模型（SCR）都应用于CEUS数据，该模型明确考虑了慢流量的其他组成部分。这些模型在贝叶斯框架内求解。我们还利用SCR获得的灌注估计值来训练支持向量机分类器，以区分不同类型的关节炎。将患者分为两组（类风湿关节炎和多关节类RA银屑病关节炎与其他类型的关节炎）时，各组的慢成分振幅显着不同：RA和类RA患者的a_1平均值及其变异性在统计学上较高患者（平均数增加131％，p = 0.035，标准差增加73％，p = 0.049）。 SVM分类器达到了89％的平衡精度，灵敏度为100％，特异性为78％。

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