基于分步子空间映射的无标记膈肌运动预测算法

呼吸会引起体内器官和肿瘤的运动, 这会显著影响放射治疗的过程和效果. 人体内部膈肌和胸腹部外表面是当前两种与呼吸系统高度相关的结构, 本文对其进行系统研究, 提出了一种新的分步子空间映射(Two-step subspace mapping, TSSM)算法, 通过对体外胸腹部表面的测量, 来预测体内膈肌的运动. 首先采用三维图像分割技术对4D CT图像进行分割, 在不使用标记物的情况下, 准确测量体内膈肌和体外胸腹部表面的位移. 为了解决跨空间的预测问题, TSSM首先构造特征子空间, 并将膈肌数据和胸腹外表面数据分别映射到各自的子空间中, 以减少数据的相关性和冗余信息; 然后通过线性岭回归优化过程, 对两个子空间进行二次映射, 从而有效地捕获跨空间数据之间的相关性. 根据训练得到的相关模型, 通过体外胸腹部外表面的运动情况, 对体内膈肌的运动情况进行准确的预测. 为了研究数据之间的非线性关系, 进一步将TSSM推广到了基于核的TSSM (kTSSM)算法. 实验表明, 该方法可以根据腹腔外表面的运动情况, 准确地对体内膈肌位移进行预测, 优于经典的线性模型和ANN模型. 给出了优化算法的解析解, 其运算速度快, 将有助于提高放射治疗中门控技术和跟踪技术的效率和精度.

Markerless Prediction of Diaphragm Displacement Based on Two-step Subspace Mapping

Respiratory induced organ and tumor motion has great influence in radiation therapy. Two highly correlated structures with respiratory are comprehensively investigated including internal diaphragm and external thoracoabdominal surface in this paper. A novel two-step subspace mapping (TSSM) algorithm is proposed to predict the diaphragm displacement by markerless thoracoabdominal surface measurement. TSSM first incorporates 3D image segmentation to accurately measure the displacement of the diaphragm and the thoracoabdominal surface without markers on 4D CT images. To solve the cross-domain estimation problem, TSSM first constructs eigenspaces and projects the two organs＇ displacement data into their corresponding subspaces to reduce the redundancy and irrelevance. Then, TSSM makes a mapping between the subspaces by a Ridge regression optimization to effectively characterize their correlation. Based on the trained correlation model, the diaphragm domain can be adapted to the abdominal surface domain, and then the diaphragm displacement can be estimated by the abdominal surface without markers. In order to investigate the non-linear correlation, TSSM is further extended to kernel TSSM (kTSSM). Experiments show the proposed method can accurately predict the displacement of the internal diaphragm by the external thoracoabdominal surface, and outperform the classical linear model and ANN model. The simple closed form for the optimization algorithm leads to an extremely fast algorithm, which has potential for improving the timing accuracy of surface-guided gating and tracking in radiotherapy.