Kuo Chia-Chun, Guo Ming-Lu, Liao Ai-Ho, Chang Ting-Wei, Yu Hsiao-Wei, Ramanathan Subramanian, Zhou Hong, Boominathan Catherin Meena, Jeng Shiu-Chen, Chiou Jeng-Fong, Ting Lai-Lei, Chuang Ho-Chiao
Department of Radiation Oncology, Taipei Medical University Hospital, Taipei, Taiwan.
Department of Radiation Oncology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
Quant Imaging Med Surg. 2025 Apr 1;15(4):3248-3262. doi: 10.21037/qims-24-1777. Epub 2025 Mar 28.
Accurate tumor localization is crucial in radiation therapy to ensure precise dose delivery and minimize damage to healthy tissues. This study introduces a novel thoracoabdominal phantom designed for predicting tumor positions in radiotherapy. The phantom incorporates the use of mask region-based convolutional neural networks (Mask R-CNN) ultrasound image tracking algorithm (M-UITA) in conjunction with 4-dimensional computed tomography (4DCT) to establish and refine a tumor motion conversion model.
Respiratory Motion Simulation System (RMSS) along with 4DCT was used to track the motion trajectories of the tumor phantom in both the superior-inferior (SI) and medial-lateral (ML) directions, with amplitudes ranging from 30-40 mm. Simultaneously, M-UITA was used to track the motion trajectory of the diaphragm phantom in the SI direction to establish a conversion model to derive the motion of the tumor from the motion of the diaphragm. Subsequently, cone beam computed tomography (CBCT) was used for the verification of the tumor phantom conversion position error.
The results indicated that the absolute error between the estimated and actual motion trajectories of the tumor phantom ranged from 0.35 to 1.35 mm in the SI direction and from 0.73 to 2.26 mm in the ML direction.
This study has redesigned the thoracoabdominal phantom and refined the conversion model. In comparison to previous research, errors in both the SI and ML directions have been reduced. In the future, it can be integrated with a respiratory motion compensation system to minimize radiation dose damage to normal tissues.
在放射治疗中，准确的肿瘤定位对于确保精确的剂量输送以及将对健康组织的损害降至最低至关重要。本研究引入了一种新型的胸腹体模，旨在预测放射治疗中的肿瘤位置。该体模结合了基于掩码区域的卷积神经网络（Mask R-CNN）超声图像跟踪算法（M-UITA）与四维计算机断层扫描（4DCT），以建立和完善肿瘤运动转换模型。
使用呼吸运动模拟系统（RMSS）和4DCT跟踪肿瘤体模在上下（SI）和内外（ML）方向上的运动轨迹，振幅范围为30 - 40毫米。同时，使用M-UITA跟踪膈肌体模在SI方向上的运动轨迹，以建立一个转换模型，从膈肌的运动中推导肿瘤的运动。随后，使用锥束计算机断层扫描（CBCT）验证肿瘤体模转换位置的误差。
结果表明，肿瘤体模估计运动轨迹与实际运动轨迹之间的绝对误差在SI方向上为0.35至1.35毫米，在ML方向上为0.73至2.26毫米。
本研究重新设计了胸腹体模并完善了转换模型。与先前的研究相比，SI和ML方向上的误差均有所降低。未来，它可以与呼吸运动补偿系统集成，以将对正常组织的辐射剂量损害降至最低。
