3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models.
3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models.
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DOI：
10.1088/0031-9155/60/9/3807
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发表时间：
2015-05-07
影响因子：
3.5
通讯作者：
Lewis JH
中科院分区：
文献类型：
--
作者：
Dhou S;Hurwitz M;Mishra P;Cai W;Rottmann J;Li R;Williams C;Wagar M;Berbeco R;Ionascu D;Lewis JH
3D透视图像以高空间和时间分辨率表示治疗期间的体积患者解剖结构。使用4DCT图像构建的运动模型估计的3D透视图像，在治疗前几天或几周拍摄，不能可靠地代表治疗期间的患者解剖结构。在这项研究中，我们开发和执行技术的初步评价，以开发患者特定的运动模型，从4D锥束CT（4DCBCT）图像，治疗前立即采取，并使用这些模型来估计3D透视图像的基础上，在治疗过程中捕获的2D千伏投影。我们通过与地面真实数字和物理体模图像进行比较来评估3D透视图像的准确性。在代表肿瘤基线偏移或初始患者定位误差的模拟临床情况下，比较了基于4DCBCT和4DCT的运动模型的性能。本研究的结果证明了4DCBCT成像生成运动模型的能力，该模型可以解释基于4DCT的运动模型无法解释的变化。当模拟肿瘤基线偏移和高达5 mm的患者定位误差时，对于基于4DCBCT的运动模型，6个数据集中的平均肿瘤定位误差和第95百分位误差分别为1.20 mm和2.2 mm。应用于相同六个数据集的基于4DCT的运动模型导致平均肿瘤定位误差和第95百分位误差分别为4.18和5.4 mm。还对所有实验进行逐体素强度差异的分析。总之，本研究证明了基于4DCBCT的3D透视图像生成在数字和物理体模中的可行性，并显示了当4DCT成像时间和治疗输送时间之间存在解剖结构变化时，基于4DCBCT的3D透视图像估计的潜在优势。
3D fluoroscopic images represent volumetric patient anatomy during treatment with high spatial and temporal resolution. 3D fluoroscopic images estimated using motion models built using 4DCT images, taken days or weeks prior to treatment, do not reliably represent patient anatomy during treatment. In this study we develop and perform initial evaluation of techniques to develop patient-specific motion models from 4D cone-beam CT (4DCBCT) images, taken immediately before treatment, and use these models to estimate 3D fluoroscopic images based on 2D kV projections captured during treatment. We evaluate the accuracy of 3D fluoroscopic images by comparing to ground truth digital and physical phantom images. The performance of 4DCBCT- and 4DCT- based motion models are compared in simulated clinical situations representing tumor baseline shift or initial patient positioning errors. The results of this study demonstrate the ability for 4DCBCT imaging to generate motion models that can account for changes that cannot be accounted for with 4DCT-based motion models. When simulating tumor baseline shift and patient positioning errors of up to 5 mm, the average tumor localization error and the 95th percentile error in six datasets were 1.20 and 2.2 mm, respectively, for 4DCBCT-based motion models. 4DCT-based motion models applied to the same six datasets resulted in average tumor localization error and the 95th percentile error of 4.18 and 5.4 mm, respectively. Analysis of voxel-wise intensity differences was also conducted for all experiments. In summary, this study demonstrates the feasibility of 4DCBCT-based 3D fluoroscopic image generation in digital and physical phantoms, and shows the potential advantage of 4DCBCT-based 3D fluoroscopic image estimation when there are changes in anatomy between the time of 4DCT imaging and the time of treatment delivery.
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