Zhang Yikun, Hu Dianlin, Li Wangyao, Zhang Weijie, Chen Gaoyu, Chen Ronald C, Chen Yang, Gao Hao
IEEE Trans Med Imaging. 2025 Jan;44(1):284-296. doi: 10.1109/TMI.2024.3439573. Epub 2025 Jan 2.
This work demonstrates the feasibility of two-orthogonal-projection-based CBCT (2V-CBCT) reconstruction and dose calculation for radiation therapy (RT) using real projection data, which is the first 2V-CBCT feasibility study with real projection data, to the best of our knowledge. RT treatments are often delivered in multiple fractions, for which on-board CBCT is desirable to calculate the delivered dose per fraction for the purpose of RT delivery quality assurance and adaptive RT. However, not all RT treatments/fractions have CBCT acquired, but two orthogonal projections are always available. The question to be addressed in this work is the feasibility of 2V-CBCT for the purpose of RT dose calculation. 2V-CBCT is a severely ill-posed inverse problem for which we propose a coarse-to-fine learning strategy. First, a 3D deep neural network that can extract and exploit the inter-slice and intra-slice information is adopted to predict the initial 3D volumes. Then, a 2D deep neural network is utilized to fine-tune the initial 3D volumes slice-by-slice. During the fine-tuning stage, a perceptual loss based on multi-frequency features is employed to enhance the image reconstruction. Dose calculation results from both photon and proton RT demonstrate that 2V-CBCT provides comparable accuracy with full-view CBCT based on real projection data.
这项工作展示了基于双正交投影的锥束CT（2V-CBCT）重建和使用真实投影数据进行放射治疗（RT）剂量计算的可行性，据我们所知，这是第一项使用真实投影数据的2V-CBCT可行性研究。RT治疗通常分多次进行，为此，机载CBCT有助于计算每次分割的剂量，以确保RT治疗质量和进行适应性RT。然而，并非所有的RT治疗/分割都有CBCT数据，但两个正交投影总是可用的。这项工作要解决的问题是2V-CBCT用于RT剂量计算的可行性。2V-CBCT是一个严重不适定的逆问题，对此我们提出了一种从粗到细的学习策略。首先，采用一个能够提取和利用切片间和切片内信息的3D深度神经网络来预测初始3D体积。然后，利用一个2D深度神经网络逐片微调初始3D体积。在微调阶段，采用基于多频特征的感知损失来增强图像重建。光子和质子RT的剂量计算结果表明，基于真实投影数据，2V-CBCT与全视野CBCT具有相当的准确性。
