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

Ye-Jun Gong; Yue-Ke Li; Rongrong Zhou; Zhan Liang; Yingying Zhang; Tingting Cheng; Zi-Jian Zhang

doi:10.1016/j.compmedimag.2024.102385

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

Comput Med Imaging Graph. 2024 Apr 18:115:102385. doi: 10.1016/j.compmedimag.2024.102385. Online ahead of print.

Authors

Ye-Jun Gong¹, Yue-Ke Li¹, Rongrong Zhou², Zhan Liang², Yingying Zhang², Tingting Cheng³, Zi-Jian Zhang⁴

Affiliations

¹ MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan 410081, PR China.
² Department of Radiation Oncology, Xiangya Hospital Central South University, Changsha, Hunan, PR China; Xiangya Lung Cancer Center, Xiangya Hospital Central South University, Changsha, Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, PR China.
³ Xiangya Lung Cancer Center, Xiangya Hospital Central South University, Changsha, Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, PR China; Department of general practice, Xiangya Hospital Central South University, Changsha, Hunan, PR China. Electronic address: chengtingting@csu.edu.cn.
⁴ Department of Radiation Oncology, Xiangya Hospital Central South University, Changsha, Hunan, PR China; Xiangya Lung Cancer Center, Xiangya Hospital Central South University, Changsha, Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, PR China. Electronic address: wanzzj@csu.edu.cn.

PMID: 38663077
DOI: 10.1016/j.compmedimag.2024.102385

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).

Keywords: 4D-CT; Dynamic mode decomposition; Lung tumor; Respiratory motion; Uniform angular interval sampling.