目的:经皮影像引导介入治疗通常用于癌症的诊断和治疗。在实践中,生理呼吸引起的运动增加了将针头准确插入肿瘤而不损害周围重要结构的难度。在这项工作中,我们提出了一个数据驱动的患者特定的分层呼吸运动估计框架,以实时准确估计肿瘤和周围重要组织的位置。
方法:使用附着在胸部或腹部皮肤上的光学标记物的运动作为替代信号,以基于支持向量回归(sVR)估计肿瘤运动。以估计的肿瘤运动作为输入,开发了一种新颖的呼吸运动模型来估计整个器官(肝脏或肺)的变形场,而无需术中,迭代优化计算。基于Kriging算法在李代数空间中建立整个器官的呼吸运动模型,保证估计的变形场是亚纯的,最优,不偏不倚。通过使用混合差分配准方法在四维计算机断层扫描(4DCT)图像之间进行变形配准,可以获得用于对整个器官的运动进行建模的术前先验知识。
结论:对体内比格犬的实验结果表明,估计的变形场的雅可比行列式的最小值大于零,所以用我们的方法估计的整个肝脏的变形场是不同的。肿瘤的平均位置误差为1.2mm,平均精度提高了76.5%,整个肝脏的平均位置误差为2.1毫米,对应于37.9%的平均精度提高。基于公开人类受试者数据的实验结果表明,肿瘤的平均位置误差为1.1mm,对应于83.1%的平均精度提高,整个肺的平均位置误差为2.1毫米,相应的平均精度提高了41.4%。肿瘤和整个器官的定位误差是分层的并且与临床需求一致。
OBJECTIVE: Percutaneous image-guided interventions are commonly used for the diagnosis and treatment of cancer. In practice, physiological breathing-induced motion increases the difficulty of accurately inserting needles into tumors without impairing the surrounding vital structures. In this work, we propose a data-driven patient-specific hierarchical respiratory motion estimation framework to accurately estimate the position of a tumor and surrounding vital tissues in real time.
METHODS: The motion of optical markers attached to the chest or abdomen skin is used as a surrogate signal to estimate tumor motion based on ɛ-support vector regression (ɛ-SVR). With the estimated tumor motion as the input, a novel respiratory motion model is developed to estimate the diffeomorphic deformation field of the whole organ (liver or lung) without intraoperative, iterative optimization computations. The respiratory motion model of the whole organ is established in Lie algebra space based on the kriging algorithm to ensure that the estimated deformation field is diffeomorphic, optimal, and unbiased. Preoperative prior knowledge for modeling the motion of whole organs is obtained by deformation registration between four-dimensional computed tomography (4D CT) images using a hybrid diffeomorphic registration method.
CONCLUSIONS: Experimental results on an in vivo beagle dog show that the minimum value of the determinant of the Jacobian of the estimated deformation field is greater than zero, so the estimated deformation field of the whole liver with our method is diffeomorphic. The mean position error of the tumor is 1.2 mm corresponding to a mean accuracy improvement of 76.5%, and the mean position error of the whole liver is 2.1 mm, corresponding to a mean accuracy improvement of 37.9%. The experimental results based on public human subject data show that the mean position error of the tumor is 1.1 mm, corresponding to a mean accuracy improvement of 83.1%, and the mean position error of the whole lung is 2.1 mm, corresponding to a mean accuracy improvement of 41.4%. The positioning errors for the tumor and whole organ are hierarchical and consistent with clinical demand.