Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.
Phys Med Biol. 2022 May 24;67(11). doi: 10.1088/1361-6560/ac6b7b.
Real-time imaging is highly desirable in image-guided radiotherapy, as it provides instantaneous knowledge of patients' anatomy and motion during treatments and enables online treatment adaptation to achieve the highest tumor targeting accuracy. Due to extremely limited acquisition time, only one or few x-ray projections can be acquired for real-time imaging, which poses a substantial challenge to localize the tumor from the scarce projections. For liver radiotherapy, such a challenge is further exacerbated by the diminished contrast between the tumor and the surrounding normal liver tissues. Here, we propose a framework combining graph neural network-based deep learning and biomechanical modeling to track liver tumor in real-time from a single onboard x-ray projection.Liver tumor tracking is achieved in two steps. First, a deep learning network is developed to predict the liver surface deformation using image features learned from the x-ray projection. Second, the intra-liver deformation is estimated through biomechanical modeling, using the liver surface deformation as the boundary condition to solve tumor motion by finite element analysis. The accuracy of the proposed framework was evaluated using a dataset of 10 patients with liver cancer.The results show accurate liver surface registration from the graph neural network-based deep learning model, which translates into accurate, fiducial-less liver tumor localization after biomechanical modeling (<1.2 (±1.2) mm average localization error).The method demonstrates its potentiality towards intra-treatment and real-time 3D liver tumor monitoring and localization. It could be applied to facilitate 4D dose accumulation, multi-leaf collimator tracking and real-time plan adaptation. The method can be adapted to other anatomical sites as well.
实时成像在图像引导放疗中是非常理想的，因为它可以提供患者在治疗过程中解剖结构和运动的即时信息，并能够进行在线治疗调整，以实现最高的肿瘤靶向准确性。由于采集时间极其有限，实时成像只能采集一个或几个 X 射线投影，这给从稀少的投影中定位肿瘤带来了很大的挑战。对于肝脏放疗，由于肿瘤与周围正常肝脏组织之间的对比度降低，这种挑战更加严重。在这里，我们提出了一个结合基于图神经网络的深度学习和生物力学建模的框架，用于从单次机载 X 射线投影中实时跟踪肝脏肿瘤。肝脏肿瘤跟踪分为两步完成。首先，开发了一个深度学习网络，使用从 X 射线投影中学习到的图像特征来预测肝脏表面变形。其次，通过生物力学建模来估计肝脏内的变形，将肝脏表面变形作为边界条件，通过有限元分析来求解肿瘤运动。使用 10 名肝癌患者的数据集评估了所提出框架的准确性。结果表明，基于图神经网络的深度学习模型可以实现准确的肝脏表面配准，经过生物力学建模后，可以实现准确的、无标记的肝脏肿瘤定位（<1.2（±1.2）mm 平均定位误差）。该方法展示了其在治疗过程中实时 3D 肝脏肿瘤监测和定位的潜力。它可以应用于促进 4D 剂量积累、多叶准直器跟踪和实时计划调整。该方法也可以适应其他解剖部位。
