Xie Jiacheng, Shao Hua-Chieh, Li Yunxiang, Yan Shunyu, Shen Chenyang, Wang Jing, Zhang You
Department of Radiation Oncology, The Advanced Imaging and Informatics for Radiation Therapy (AIRT) Laboratory, Dallas, TX 75390, United States of America.
Department of Radiation Oncology, The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Dallas, TX 75390, United States of America.
Phys Med Biol. 2025 Jun 25;70(12). doi: 10.1088/1361-6560/addf0e.
Deformable liver motion tracking using a single x-ray projection enables real-time motion monitoring and treatment intervention. We introduce a conditional point cloud diffusion (PCD) model-based framework for accurate and robust liver motion tracking from arbitrarily angled single x-ray projections.We propose a conditional PCD model for liver motion tracking (PCD-Liver), which estimates volumetric liver motion by solving deformable vector fields (DVFs) of a prior liver surface point cloud, based on a single x-ray image. It is a patient-specific model of two main components: a rigid alignment model to estimate the liver's overall shifts, and a conditional PCD model that further corrects for the liver surface's deformation. Conditioned on the motion-encoded features extracted from a single x-ray projection by a geometry-informed feature pooling layer, the diffusion model iteratively solves detailed liver surface DVFs in a projection angle-agnostic fashion. The liver surface motion solved by PCD-Liver is subsequently fed as the boundary condition into a U-Net-based biomechanical model to infer the liver's internal motion to localize liver tumors. A dataset of 10 liver cancer patients was used for evaluation. We used the root mean square error (RMSE) and 95-percentile Hausdorff distance (HD95) metrics to examine the liver point cloud motion estimation accuracy, and the center-of-mass error (COME) to quantify the liver tumor localization error.The mean (±s.d.) RMSE, HD95, and COME of the prior liver or tumor before motion estimation were 8.82 mm (±3.58 mm), 10.84 mm (±4.55 mm), and 9.72 mm (±4.34 mm), respectively. After PCD-Liver's motion estimation, the corresponding values were 3.63 mm (±1.88 mm), 4.29 mm (±1.75 mm), and 3.46 mm (±2.15 mm). Under highly noisy conditions, PCD-Liver maintained stable performance.This study presents an accurate and robust framework for liver deformable motion estimation and tumor localization for image-guided radiotherapy.
使用单幅X射线投影进行肝脏可变形运动跟踪能够实现实时运动监测和治疗干预。我们引入了一种基于条件点云扩散（PCD）模型的框架，用于从任意角度的单幅X射线投影中进行准确且稳健的肝脏运动跟踪。我们提出了一种用于肝脏运动跟踪的条件PCD模型（PCD-Liver），该模型基于单幅X射线图像，通过求解先前肝脏表面点云的可变形向量场（DVF）来估计肝脏的体积运动。它是一个针对患者的模型，由两个主要部分组成：一个用于估计肝脏整体位移的刚性对齐模型，以及一个进一步校正肝脏表面变形的条件PCD模型。基于由几何信息特征池化层从单幅X射线投影中提取的运动编码特征，扩散模型以与投影角度无关的方式迭代求解详细的肝脏表面DVF。PCD-Liver求解的肝脏表面运动随后作为边界条件输入到基于U-Net的生物力学模型中，以推断肝脏的内部运动来定位肝脏肿瘤。使用了一个包含10名肝癌患者的数据集进行评估。我们使用均方根误差（RMSE）和95%百分位数豪斯多夫距离（HD95）指标来检查肝脏点云运动估计的准确性，并使用质心误差（COME）来量化肝脏肿瘤定位误差。运动估计前先前肝脏或肿瘤的平均（±标准差）RMSE、HD95和COME分别为8.82毫米（±3.58毫米）、10.84毫米（±4.55毫米）和9.72毫米（±4.34毫米）。经过PCD-Liver的运动估计后，相应的值分别为3.63毫米（±1.88毫米）、4.29毫米（±1.75毫米）和3.46毫米（±2.15毫米）。在高噪声条件下，PCD-Liver保持了稳定的性能。本研究提出了一个用于图像引导放射治疗的肝脏可变形运动估计和肿瘤定位的准确且稳健的框架。
