Kavuri Amar, Paul Segars W, Xu Xiaoming, Lynch David A, Page McAdams H, Samei Ehsan, Abadi Ehsan
Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, North Carolina (A.K., W.P.S., H.P.M., E.S., E.A.).
Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina (X.X.).
Acad Radiol. 2025 Aug;32(8):4913-4921. doi: 10.1016/j.acra.2025.04.042. Epub 2025 May 9.
While CT scans can non-invasively diagnose and quantify emphysema, its consistency can be impacted by the variability in the patient's lung volume during the acquisition. Studies to evaluate and mitigate this variability requires imaging of patients under varying inspirations, which is ethically constrained due to possible radiation risk from repeated exposures.
To isolate and quantify the effects of lung volume variability on the consistency of CT-based emphysema measurements using a virtual imaging trial approach.
20 human models with emphysema were created at varying inspirations (70% to 100% of full inspiration) emulating various lung volumes. The models were imaged using a scanner-specific simulator (DukeSim) to generate CT images. Emphysema biomarkers (LAA-950 and Perc15) and global lung density measures were computed from simulated CT images. A linear mixed-effects model was used to analyze the effect of lung volume and total lung volume (TLV) correction methods (physiological and statistical models) on the accuracy of these emphysema measures.
The statistical analysis exhibited a marked influence of inspiration level on both LAA-950 and Perc15 (p-values < 0.001). LAA-950 underestimated the amount of emphysema by 1.44±0.32% (mean ± standard error) for every 1-liter deviation from full inspiration, and the log-log scale analysis revealed that Perc15 underestimated the severity by 1.16±0.02% for every 1% lower lung volume. These deviations were reduced by TLV correction methods.
This study isolated the impact of lung volume variability on the consistency of emphysema quantification by eliminating the confounding factors inherent in clinical studies. Through controlled virtual imaging trial framework, we identified that the physiological model-based TLV correction method was more effective strategy compared to the statistical model for mitigating volume-dependent variability in emphysema assessment. Our study demonstrates an investigation that cannot be readily undertaken with human subjects or simplistic physical phantoms.
虽然CT扫描可以非侵入性地诊断和量化肺气肿,但其一致性可能会受到采集过程中患者肺容积变异性的影响。评估和减轻这种变异性的研究需要对不同吸气状态下的患者进行成像,由于重复照射可能带来辐射风险,这在伦理上受到限制。
使用虚拟成像试验方法分离并量化肺容积变异性对基于CT的肺气肿测量一致性的影响。
创建了20个患有肺气肿的人体模型,模拟不同的肺容积(全吸气的70%至100%),处于不同的吸气状态。使用特定扫描仪模拟器(DukeSim)对模型进行成像以生成CT图像。从模拟的CT图像中计算肺气肿生物标志物(LAA - 950和Perc15)以及全肺密度测量值。使用线性混合效应模型分析肺容积和全肺容积(TLV)校正方法(生理模型和统计模型)对这些肺气肿测量准确性的影响。
统计分析显示吸气水平对LAA - 950和Perc15均有显著影响(p值<0.001)。与全吸气相比,每偏离1升,LAA - 950低估肺气肿量1.44±0.32%(平均值±标准误差),对数 - 对数尺度分析显示,每降低1%的肺容积,Perc15低估严重程度1.16±0.02%。这些偏差通过TLV校正方法得以减少。
本研究通过消除临床研究中固有的混杂因素,分离了肺容积变异性对肺气肿量化一致性的影响。通过受控的虚拟成像试验框架,我们发现与统计模型相比,基于生理模型的TLV校正方法是减轻肺气肿评估中体积依赖性变异性的更有效策略。我们的研究展示了一项难以在人体受试者或简单物理模型上轻易进行的调查。