A novel approach for estimating lung tumor motion based on dynamic features in 4D-CT

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL Computerized Medical Imaging and Graphics Pub Date : 2024-04-18 DOI:10.1016/j.compmedimag.2024.102385
Ye-Jun Gong , Yue-Ke Li , Rongrong Zhou , Zhan Liang , Yingying Zhang , Tingting Cheng , Zi-Jian Zhang
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Abstract

Due to the high expenses involved, 4D-CT data for certain patients may only include five respiratory phases (0%, 20%, 40%, 60%, and 80%). This limitation can affect the subsequent planning of radiotherapy due to the absence of lung tumor information for the remaining five respiratory phases (10%, 30%, 50%, 70%, and 90%). This study aims to develop an interpolation method that can automatically derive tumor boundary contours for the five omitted phases using the available 5-phase 4D-CT data. The dynamic mode decomposition (DMD) method is a data-driven and model-free technique that can extract dynamic information from high-dimensional data. It enables the reconstruction of long-term dynamic patterns using only a limited number of time snapshots. The quasi-periodic motion of a deformable lung tumor caused by respiratory motion makes it suitable for treatment using DMD. The direct application of the DMD method to analyze the respiratory motion of the tumor is impractical because the tumor is three-dimensional and spans multiple CT slices. To predict the respiratory movement of lung tumors, a method called uniform angular interval (UAI) sampling was developed to generate snapshot vectors of equal length, which are suitable for DMD analysis. The effectiveness of this approach was confirmed by applying the UAI-DMD method to the 4D-CT data of ten patients with lung cancer. The results indicate that the UAI-DMD method effectively approximates the lung tumor’s deformable boundary surface and nonlinear motion trajectories. The estimated tumor centroid is within 2 mm of the manually delineated centroid, a smaller margin of error compared to the traditional BSpline interpolation method, which has a margin of 3 mm. This methodology has the potential to be extended to reconstruct the 20-phase respiratory movement of a lung tumor based on dynamic features from 10-phase 4D-CT data, thereby enabling more accurate estimation of the planned target volume (PTV).

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基于 4D-CT 动态特征估计肺肿瘤运动的新方法
由于费用高昂,某些患者的 4D-CT 数据可能只包括五个呼吸阶段(0%、20%、40%、60% 和 80%)。由于缺乏其余五个呼吸相位(10%、30%、50%、70% 和 90%)的肺部肿瘤信息,这一局限性会影响后续放疗计划的制定。本研究旨在开发一种插值方法,利用现有的五相 4D-CT 数据自动推导出被遗漏的五个阶段的肿瘤边界轮廓。动态模式分解(DMD)方法是一种数据驱动的无模型技术,可从高维数据中提取动态信息。它只需使用有限的时间快照就能重建长期动态模式。由呼吸运动引起的可变形肺肿瘤的准周期运动使其适合使用 DMD 进行治疗。由于肿瘤是三维的,且跨越多个 CT 切片,因此直接应用 DMD 方法分析肿瘤的呼吸运动是不切实际的。为了预测肺部肿瘤的呼吸运动,研究人员开发了一种称为均匀角间隔(UAI)采样的方法,以生成适合 DMD 分析的等长快照矢量。通过将 UAI-DMD 方法应用于 10 名肺癌患者的 4D-CT 数据,证实了这种方法的有效性。结果表明,UAI-DMD 方法能有效逼近肺部肿瘤的可变形边界面和非线性运动轨迹。估计的肿瘤中心点与人工划定的中心点相差 2 毫米以内,与传统的 BSpline 插值法相比误差较小,后者的误差为 3 毫米。这种方法有望扩展到根据 10 相 4D-CT 数据的动态特征重建肺部肿瘤的 20 相呼吸运动,从而更准确地估计计划靶体积(PTV)。
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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
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