Dhou Salam, Alkhodari Mohanad, Ionascu Dan, Williams Christopher, Lewis John H
Department of Computer Science and Engineering, College of Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.
Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University, Abu Dhabi 127788, United Arab Emirates.
J Imaging. 2022 Jan 18;8(2):17. doi: 10.3390/jimaging8020017.
A method for generating fluoroscopic (time-varying) volumetric images using patient-specific motion models derived from four-dimensional cone-beam CT (4D-CBCT) images was developed. 4D-CBCT images acquired immediately prior to treatment have the potential to accurately represent patient anatomy and respiration during treatment. Fluoroscopic 3D image estimation is performed in two steps: (1) deriving motion models and (2) optimization. To derive motion models, every phase in a 4D-CBCT set is registered to a reference phase chosen from the same set using deformable image registration (DIR). Principal components analysis (PCA) is used to reduce the dimensionality of the displacement vector fields (DVFs) resulting from DIR into a few vectors representing organ motion found in the DVFs. The PCA motion models are optimized iteratively by comparing a cone-beam CT (CBCT) projection to a simulated projection computed from both the motion model and a reference 4D-CBCT phase, resulting in a sequence of fluoroscopic 3D images. Patient datasets were used to evaluate the method by estimating the tumor location in the generated images compared to manually defined ground truth positions. Experimental results showed that the average tumor mean absolute error (MAE) along the superior-inferior (SI) direction and the 95th percentile in two patient datasets were 2.29 and 5.79 mm for patient 1, and 1.89 and 4.82 mm for patient 2. This study demonstrated the feasibility of deriving 4D-CBCT-based PCA motion models that have the potential to account for the 3D non-rigid patient motion and localize tumors and other patient anatomical structures on the day of treatment.
开发了一种使用从四维锥形束CT（4D-CBCT）图像导出的患者特定运动模型来生成荧光透视（随时间变化）体积图像的方法。在治疗前立即采集的4D-CBCT图像有可能准确地反映治疗期间患者的解剖结构和呼吸情况。荧光透视三维图像估计分两步进行：（1）推导运动模型和（2）优化。为了推导运动模型，使用可变形图像配准（DIR）将4D-CBCT集中的每个相位配准到从同一集中选择的参考相位。主成分分析（PCA）用于将DIR产生的位移向量场（DVF）的维度降低为几个表示DVF中发现的器官运动的向量。通过将锥形束CT（CBCT）投影与从运动模型和参考4D-CBCT相位计算出的模拟投影进行比较，对PCA运动模型进行迭代优化，从而生成一系列荧光透视三维图像。使用患者数据集通过将生成图像中的肿瘤位置与手动定义的真实位置进行比较来评估该方法。实验结果表明，在两个患者数据集中，患者1沿上下（SI）方向的平均肿瘤平均绝对误差（MAE）和第95百分位数分别为2.29和5.79毫米，患者2为1.89和4.82毫米。这项研究证明了推导基于4D-CBCT的PCA运动模型的可行性，该模型有可能考虑三维非刚性患者运动，并在治疗当天对肿瘤和其他患者解剖结构进行定位。
