Author: Sean L. Berry, Weixing Cai, Laura I. Cervino, Yusuf Emre Erdi, Yabo Fu, Yiming Gao, Daphna Gelblum, Wendy B. Harris, Xiuxiu He, Tianfang Li, Xiang Li, Seng Boh Gary Lim, Jean M. Moran, Mitchell Yu, Hao Zhang 👨🔬
Affiliation: Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center 🌍
Purpose: Gated CBCT (gCBCT) is commonly employed for respiratory gating lung cancer patients to ensure precise patient setup. However, the scan is time-consuming on C-arm linear accelerators (LINAC) due to frequent interruptions in gantry rotation dictated by respiratory gating signals. This study aims to implement a novel nonstop gated CBCT (ngCBCT) technique on LINAC and quantitatively compare its scan efficiency and imaging dose to the current clinical gCBCT.
Methods: gCBCT was acquired in the clinical mode of a C-arm LINAC, while ngCBCT was implemented via a customized XML file in the developer mode. Both techniques utilize the same thorax imaging protocol (half fan and full trajectory). The Cone-Beam Dose Index (CBDI) was quantified using a standard CTDI body phantom and two pencil chambers that measure central and peripheral imaging doses. Respiratory patterns, including Cos4 motion (3-6 seconds cycle periods) and three clinical patient breathing patterns, were simulated using a CIRS surrogate motion platform. Different gating duty cycles (30%-60%) were tested on Cos4 motion, while one gating duty cycle was reproduced for each patient's breathing pattern. Scan times were determined by analyzing the timestamps of the projection data.
Results: Unlike gCBCT, where scan time ranges from 1.8 to 5 minutes—depending on the gating duty cycle and slightly on breathing period—ngCBCT scan times remain consistently around 1 minute. For imaging dose, the weighted CBDI (CBDIw) for ngCBCT is reduced to 26.7%-60.1% (nearly identical to selected gating duty cycle) of gCBCT (approximately 5.2 mGy across all gCBCT scans).
Conclusion: The ngCBCT technique provides a transformative boost in scan efficiency and dose reduction over current clinical gCBCT. Empowered by our deep learning-based reconstruction framework, ngCBCT maintains image quality comparable to that of gCBCT. This innovation will not only improve the patient experience but also expand access to advanced respiratory gating radiotherapy.