Radiotherapy
A markerless beam′s eye view tumor tracking algorithm based on VoxelMorph-a learning-based unsupervised registration framework for images with missing data
Huang Taiming, Zhong Jiajian, Guan Qi, Qiu Minmin, Luo Ning, Deng Yongjin
Published 2022-12-25
Cite as Chin J Radiol Med Prot, 2022, 42(12): 958-965. DOI: 10.3760/cma.j.cn112271-20220628-00272
Abstract
ObjectiveTo propose a machine learning-based markerless beam′s eye view (BEV) tumor tracking algorithm that can be applied to low-quality megavolt (MV) images with multileaf collimator (MLC)-induced occlusion and non-rigid deformation.
MethodsThis study processed the registration of MV images using the window template matching method and end-to-end unsupervised network Voxelmorph and verified the accuracy of the tumor tracking algorithm using dynamic chest models. Phantom QA plans were executed after the treatment offset was manually set on the accelerator, and 682 electronic portal imaging device (EPID) images obtained during the treatment were collected as fixed images. Moreover, the digitally reconstructed radiography (DRR) images corresponding to the portal angles in the planning system were collected as floating images for the study of target volume tracking. In addition, 533 pairs of EPID and DRR images of 21 lung tumor patients treated with radiotherapy were collected to conduct the study of tumor tracking and provide quantitative result of changes in tumor locations during the treatment. Image similarity was used for third-party validation of the algorithm.
ResultsThe algorithm could process images with different degrees (10%-80%) of data missing and performed well in non-rigid registration of images with data missing. As shown by the phantom verification, 86.8% and 80% of the tracking errors were less than 3 mm and less than 2 mm, respectively, and the normalized mutual information (NMI) varied from 1.18 ± 0.02 to 1.20 ± 0.02 after registration (t = -6.78, P = 0.001). The tumor motion of the clinical cases was dominated by translation, with an average displacement of 3.78 mm and a maximum displacement of 7.46 mm. The registration result of the cases showed the presence of non-rigid deformations, and the corresponding NMI varied from 1.21 ± 0.03 before registration to 1.22 ± 0.03 after registration (t = -2.91, P = 0.001).
ConclusionsThe tumor tracking algorithm proposed in this study has reliable tracking accuracy and high robustness and can be used for non-invasive and real-time tumor tracking requiring no additional equipment and radiation dose.
Key words:
Makerless tumor tracking; EPID; Voxelmorph; Nonrigid registration; MLC occlusion
Contributor Information
Huang Taiming
Department of Radiation Oncology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
Zhong Jiajian
Department of Radiation Oncology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
Guan Qi
Department of Radiation Oncology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
Qiu Minmin
Department of Radiation Oncology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
Luo Ning
Department of Radiation Oncology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
Deng Yongjin
Department of Radiation Oncology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
