Hybrid method for estimating lung ventilation from CT by combining intensity and motion information.

Medical physics Pub Date : 2025-03-30 DOI:10.1002/mp.17787
Paris Tzitzimpasis, Mario Ries, Bas W Raaymakers, Cornel Zachiu
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引用次数: 0

Abstract

Background: Functional lung imaging modalities allow for capturing regional lung ventilation information. Computed Tomography based ventilation imaging (CTVI) has been proposed as a surrogate modality that relies on time-resolved anatomical data and image processing. However, generating accurate ventilation maps using solely computed tomography (CT) image information remains a challenging task, due to the need to derive functional information of ventilation from anatomical observations.

Purpose: We introduce the hybrid estimation of computed tomography obtained respiratory function (HECTOR) method that consists of two components: a volume- and a density-based ventilation estimate. For the first component, a deformable image registration (DIR)-based solution for accurate volumetric CTVI generation is proposed, integrating the physical characteristics of the lung deformations in its design. For the second component, an already established air-tissue density model is used. Furthermore, a novel method is developed for combining the two components.

Methods: The proposed method consists of four principal steps: (1) Application of a specially tailored DIR algorithm to estimate respiratory motion between inhale and exhale phases. (2) Conversion of the motion information to volumetric change maps using a variation of the Jacobian determinant method. (3) Computation of a HU-based method that estimates the local product of air-tissue densities. (4) Combination of the metrics estimated in steps 2 and 3 by means of a smooth minimum function. The proposed approach is validated using the publicly available VAMPIRE dataset consisting of two subgroups: 25 subjects scanned with Galligas 4DPET/CT and 21 subjects scanned with DTPA-SPECT. Another dataset of 18 patients available at The Cancer Imaging Archive (TCIA) was used for further validation. All datasets contain inhale/exhale CT scans paired with ground-truth ventilation images (RefVIs). The CTVIs generated by the proposed HECTOR method were tested against the RefVIs using the Spearman correlation coefficient and Dice overlap of low- and high-function lung (DSC-low and DSC-high, respectively).

Results: The proposed method achieved mean Spearman, DSC-high and DSC-low coefficients of 0.62, 0.55, and 0.59 on the Galligas PET subgroup and 0.49,0,48, and 0.50 on the DTPA-SPECT subgroup of the VAMPIRE dataset. This performance was better than the highest performing method reported in the original challenge. The same metrics for the TCIA dataset were 0.66, 0.60, and 0.60. The proposed hybrid ventilation method achieved higher Spearman correlation scores than the individual volume- and density-based components in all datasets. Additionally, the use of the specially tailored DIR algorithm was found to achieve higher scores than previously reported volume-based methods.

Conclusions: Our work provides a novel processing workflow for CT ventilation imaging that can consistently generate ventilation maps with high fidelity compared to reference approaches. This study also provides further insights into the benefits of combining different types of information to model the complex dynamics of respiratory function. Such information can be useful for potential applications in radiation therapy treatment planning and thoracic dose-response assessment.

结合强度和运动信息从 CT 估算肺通气量的混合方法。
背景:肺功能成像模式允许捕获区域肺通气信息。基于计算机断层扫描的通风成像(CTVI)已被提出作为依赖于时间分辨解剖数据和图像处理的替代模式。然而,由于需要从解剖观察中获得通气的功能信息,仅使用计算机断层扫描(CT)图像信息生成准确的通气图仍然是一项具有挑战性的任务。目的:我们介绍了计算机断层扫描获得呼吸功能(HECTOR)的混合估计方法,该方法由两个组成部分组成:基于容积和基于密度的通气估计。对于第一个组件,提出了一种基于变形图像配准(DIR)的精确体积CTVI生成方案,在其设计中集成了肺部变形的物理特征。对于第二个分量,使用已经建立的空气组织密度模型。在此基础上,提出了一种结合两组分的新方法。方法:提出的方法包括四个主要步骤:(1)应用专门定制的DIR算法来估计吸气和呼气阶段之间的呼吸运动。(2)利用雅可比行列式方法的一种变体将运动信息转换为体积变化图。(3)计算一种基于hu的空气组织密度局部积估计方法。(4)通过光滑最小函数将步骤2和步骤3中估计的度量组合起来。该方法使用公开可用的VAMPIRE数据集进行验证,该数据集由两个亚组组成:25名受试者使用Galligas 4DPET/CT扫描,21名受试者使用DTPA-SPECT扫描。另一个来自癌症影像档案(TCIA)的18例患者数据集用于进一步验证。所有数据集包含吸气/呼气CT扫描与地面真实通气图像(RefVIs)配对。利用低功能肺和高功能肺(分别为DSC-low和DSC-high)的Spearman相关系数和Dice重叠,对HECTOR方法生成的CTVIs与RefVIs进行测试。结果:该方法在Galligas PET亚组上的平均Spearman、DSC-high和DSC-low系数分别为0.62、0.55和0.59,在VAMPIRE数据集的DTPA-SPECT亚组上的平均Spearman、DSC-high和DSC-low系数分别为0.49、0、48和0.50。这种性能优于原始挑战中报告的最高性能方法。TCIA数据集的相同指标分别为0.66、0.60和0.60。在所有数据集中,所提出的混合通风方法比单独的基于体积和密度的成分获得更高的Spearman相关评分。此外,发现使用专门定制的DIR算法比以前报道的基于体积的方法获得更高的分数。结论:我们的工作为CT通风成像提供了一种新的处理工作流程,与参考方法相比,它可以始终如一地生成高保真度的通风图。这项研究还提供了进一步的见解,结合不同类型的信息,模拟呼吸功能的复杂动态的好处。这些信息可以用于放射治疗计划和胸部剂量反应评估的潜在应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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