@article{QIMS30742,
author = {Lai-Lei Ting and Ho-Chiao Chuang and Ai-Ho Liao and Chia-Chun Kuo and Hsiao-Wei Yu and Hsin-Chuan Tsai and Der-Chi Tien and Shiu-Chen Jeng and Jeng-Fong Chiou},
title = {Tumor motion tracking based on a four-dimensional computed tomography respiratory motion model driven by an ultrasound tracking technique},
journal = {Quantitative Imaging in Medicine and Surgery},
volume = {10},
number = {1},
year = {2019},
keywords = {},
abstract = {Background: An ultrasound image tracking algorithm (UITA) was combined with four-dimensional computed tomography (4DCT) to create a real-time tumor motion-conversion model. The real-time position of a lung tumor phantom based on the real-time diaphragm motion trajectories detected by ultrasound imaging in the superior-inferior (SI) and medial-lateral (ML) directions were obtained.
Methods: Three different tumor motion-conversion models were created using a respiratory motion simulation system (RMSS) combined with 4DCT. The tumor tracking error was verified using cone-beam computed tomography (CBCT). The tumor motion-conversion model was produced by using the UITA to monitor the motion trajectories of the diaphragm phantom in the SI direction, and using 4DCT to monitor the motion trajectories of the tumor phantom in the SI and ML directions over the same time period, to obtain parameters for the motion-conversion model such as the tumor center position and the amplitude and phase ratios.
Results: The tumor movement was monitored for 90 s using CBCT to determine the real motion trajectories of the tumor phantom and using ultrasound imaging to simultaneously record the diaphragm movement. The absolute error of the motion trajectories of the real and estimated tumor varied between 0.5 and 2.1 mm in the two directions.
Conclusions: This study has demonstrated the feasibility of using ultrasound imaging to track diaphragmatic motion combined with a 4DCT tumor motion-conversion model to track tumor motion in the SI and ML directions. The proposed method makes tracking a lung tumor feasible in real time, including under different breathing conditions.},
issn = {2223-4306}, url = {https://qims.amegroups.org/article/view/30742}
}