Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study.
Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study.
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根据4D-CBCT患者数据集从患者特定的PCA运动模型生成透视3D图像：一项可行性研究。
DOI：
10.3390/jimaging8020017
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发表时间：
2022-01-18
影响因子：
3.2
通讯作者：
Lewis JH
中科院分区：
文献类型：
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作者：
Dhou S;Alkhodari M;Ionascu D;Williams C;Lewis JH
开发了一种使用从四维锥束CT(4D-CBCT)图像导出的患者特定运动模型来生成透视(时变)体积图像的方法。在治疗前立即获得的4D-CBCT图像有可能准确地反映患者在治疗期间的解剖和呼吸。透视三维图像估计分为两个步骤：(1)运动模型的推导和(2)优化。为了得到运动模型，使用可变形图像配准(DIR)将4D-CBCT集合中的每个相位配准到从相同集合中选择的参考相位。主成分分析(PCA)被用来将DIR产生的位移矢量场(DVF)降维为在DVF中发现的代表器官运动的几个矢量。通过将锥束CT(CBCT)投影与根据运动模型和参考4D-CBCT相位计算的模拟投影进行比较，迭代地优化PCA运动模型，从而产生透视3D图像序列。患者数据集被用来评估该方法，方法是在生成的图像中估计肿瘤位置，并与手动定义的地面真实位置进行比较。实验结果表明，在两个患者数据集中，患者1的肿瘤平均绝对误差(MAE)分别为2.29 mm和5.79 mm，患者2的平均MAE分别为1.89 mm和4.82 mm。本研究证明了基于4D-CBCT的PCA运动模型的可行性，该模型可能解释治疗当天患者的3D非刚性运动并定位肿瘤和其他解剖结构。
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.