Suppr超能文献

评估 4D-CT 通气成像的准确性:首次与 Technegas SPECT 通气比较。

Evaluating the accuracy of 4D-CT ventilation imaging: First comparison with Technegas SPECT ventilation.

机构信息

Radiation Physics Laboratory, Faculty of Medicine, Sydney University, Camperdown, NSW, 2006, Australia.

Department of Medical Physics, School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW, 2300, Australia.

出版信息

Med Phys. 2017 Aug;44(8):4045-4055. doi: 10.1002/mp.12317. Epub 2017 Jun 16.

Abstract

PURPOSE

Computed tomography ventilation imaging (CTVI) is a highly accessible functional lung imaging modality that can unlock the potential for functional avoidance in lung cancer radiation therapy. Previous attempts to validate CTVI against clinical ventilation single-photon emission computed tomography (V-SPECT) have been hindered by radioaerosol clumping artifacts. This work builds on those studies by performing the first comparison of CTVI with Tc-carbon ('Technegas'), a clinical V-SPECT modality featuring smaller radioaerosol particles with less clumping.

METHODS

Eleven lung cancer radiotherapy patients with early stage (T1/T2N0) disease received treatment planning four-dimensional CT (4DCT) scans paired with Technegas V/Q-SPECT/CT. For each patient, we applied three different CTVI methods. Two of these used deformable image registration (DIR) to quantify breathing-induced lung density changes (CTVI ), or breathing-induced lung volume changes (CTVI ) between the 4DCT exhale/inhale phases. A third method calculated the regional product of air-tissue densities (CTVI ) and did not involve DIR. Corresponding CTVI and V-SPECT scans were compared using the Dice similarity coefficient (DSC) for functional defect and nondefect regions, as well as the Spearman's correlation r computed over the whole lung. The DIR target registration error (TRE) was quantified using both manual and computer-selected anatomic landmarks.

RESULTS

Interestingly, the overall best performing method (CTVI ) did not involve DIR. For nondefect regions, the CTVI , CTVI , and CTVI methods achieved mean DSC values of 0.69, 0.68, and 0.54, respectively. For defect regions, the respective DSC values were moderate: 0.39, 0.33, and 0.44. The Spearman r-values were generally weak: 0.26 for CTVI , 0.18 for CTVI , and -0.02 for CTVI . The spatial accuracy of CTVI was not significantly correlated with TRE, however the DIR accuracy itself was poor with TRE > 3.6 mm on average, potentially indicative of poor quality 4DCT. Q-SPECT scans achieved good correlations with V-SPECT (mean r > 0.6), suggesting that the image quality of Technegas V-SPECT was not a limiting factor in this study.

CONCLUSIONS

We performed a validation of CTVI using clinically available 4DCT and Technegas V/Q-SPECT for 11 lung cancer patients. The results reinforce earlier findings that the spatial accuracy of CTVI exhibits significant interpatient and intermethod variability. We propose that the most likely factor affecting CTVI accuracy was poor image quality of clinical 4DCT.

摘要

目的

计算机断层扫描通气成像(CTVI)是一种高度普及的功能肺部成像方式,可以挖掘出在肺癌放射治疗中实现功能回避的潜力。先前使用放射性气溶胶聚集伪影验证 CTVI 与临床通气单光子发射计算机断层扫描(V-SPECT)的尝试受到了阻碍。本研究通过首次将 CTVI 与 Tc-碳(“Technegas”)进行比较,进一步推进了这些研究,后者是一种采用具有更小的放射性气溶胶颗粒且不易聚集的临床 V-SPECT 模式。

方法

11 名患有早期(T1/T2N0)疾病的肺癌放疗患者接受了治疗计划的四维 CT(4DCT)扫描,同时还进行了 Technegas V/Q-SPECT/CT 扫描。对于每位患者,我们应用了三种不同的 CTVI 方法。其中两种方法使用变形图像配准(DIR)来量化 4DCT 呼气/吸气阶段之间的呼吸诱导的肺密度变化(CTVI)或呼吸诱导的肺体积变化(CTVI)。第三种方法计算了空气-组织密度的区域乘积(CTVI),并且不涉及 DIR。使用 Dice 相似系数(DSC)比较相应的 CTVI 和 V-SPECT 扫描,以评估功能缺陷和非缺陷区域,并计算整个肺部的 Spearman's 相关系数 r。使用手动和计算机选择的解剖学标志量化 DIR 目标配准误差(TRE)。

结果

有趣的是,整体表现最佳的方法(CTVI)并不涉及 DIR。对于非缺陷区域,CTVI、CTVI和 CTVI 方法的平均 DSC 值分别为 0.69、0.68 和 0.54。对于缺陷区域,相应的 DSC 值为中度:0.39、0.33 和 0.44。Spearman r 值通常较弱:CTVI 为 0.26,CTVI 为 0.18,CTVI 为-0.02。CTVI 的空间准确性与 TRE 没有显著相关性,但是 DIR 本身的准确性很差,平均 TRE>3.6mm,这可能表明 4DCT 质量较差。Q-SPECT 扫描与 V-SPECT 具有良好的相关性(平均 r>0.6),这表明 Technegas V-SPECT 的图像质量在本研究中不是一个限制因素。

结论

我们使用临床可用的 4DCT 和 Technegas V/Q-SPECT 对 11 名肺癌患者进行了 CTVI 的验证。结果强化了早期的发现,即 CTVI 的空间准确性存在显著的个体间和方法间变异性。我们提出,影响 CTVI 准确性的最可能因素是临床 4DCT 的图像质量较差。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验