Department of Physiology and Biomedical Engineering, Biomedical Imaging Resource, Mayo Clinic College of Medicine, Rochester Minnesota 55905, USA.
Med Phys. 2007 Jul;34(7):2975-84. doi: 10.1118/1.2745936.
Fluoroscopy-based three-dimensional seed localization as a component of intraoperative dosimetry for prostate brachytherapy is an active area of research. A novel adaptive-grouping-based reconstruction approach is developed. This approach can recover overlapped seeds that are not detected from the fluoroscopic images. Two versions of the adaptive-grouping-based reconstruction approach are implemented and compared to an epipolar geometry-based seed reconstruction technique. Simulations based on nine patient datasets are used to validate the algorithms. A total of 2259 reconstructions is performed in which different types of error such as random noise in seed image locations and ambiguities in projection geometry are incorporated. Among those reconstructions, nine of the cases with overlapping seeds and the different types of error are performed. It is demonstrated that the adaptive-grouping-based reconstruction method is more accurate than the epipolar geometry method and allows faster reconstruction. At a random noise level of 0.6 mm, the mean distance error in reconstructed seed locations is approximately 1.0 mm for one of the relevant cases examined in detail. The best adaptive-grouping-based approach successfully recovered overlapped seeds in the majority of simulated cases (89%), with the remainder of cases generating one false positive seed. Phantom validation is also performed, and overlapped seeds are successfully recovered with all 92 seeds correctly localized and reconstructed. The mean distance error between segmented seed images and projected seeds is 0.5 mm in the phantom study.
基于透视的三维种子定位作为前列腺近距离放疗术中剂量学的一个组成部分，是一个活跃的研究领域。提出了一种新的基于自适应分组的重建方法。该方法可以恢复从透视图像中未检测到的重叠种子。实现了两种基于自适应分组的重建方法，并与基于极线几何的种子重建技术进行了比较。基于九个患者数据集的模拟用于验证算法。总共进行了 2259 次重建，其中包括种子图像位置的随机噪声和投影几何的歧义等不同类型的误差。在这些重建中，进行了九个具有重叠种子和不同类型误差的案例。结果表明，基于自适应分组的重建方法比极线几何方法更准确，并且允许更快的重建。在随机噪声水平为 0.6mm 的情况下，在所详细检查的一个相关案例中，重建种子位置的平均距离误差约为 1.0mm。最佳的基于自适应分组的方法成功地恢复了模拟案例中的大多数重叠种子（89%），其余案例生成一个假阳性种子。还进行了体模验证，所有 92 个种子都正确定位和重建，成功地恢复了重叠的种子。在体模研究中，分割种子图像和投影种子之间的平均距离误差为 0.5mm。
