Ayad Maria S, Lee Junghoon, Prince Jerry L, Fichtinger Gabor
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Proc SPIE Int Soc Opt Eng. 2009 Mar 13;7261:k. doi: 10.1117/12.811225.
The success of prostate brachytherapy depends on the faithful delivery of a dose plan. In turn, intraoperative localization and visualization of the implanted radioactive brachytherapy seeds enables more proficient and informed adjustments to the executed plan during therapy. Prior work has demonstrated adequate seed reconstructions from uncalibrated mobile c-arms using either external tracking devices or image-based fiducials for c-arm pose determination. These alternatives are either time-consuming or interfere with the clinical flow of the surgery, or both. This paper describes a seed reconstruction approach that avoids both tracking devices and fiducials. Instead, it uses the preoperative dose plan in conjunction with a set of captured images to get initial estimates of the c-arm poses followed by an auto-focus technique using the seeds themselves as fiducials to refine the pose estimates. Intraoperative seed localization is achieved through iteratively solving for poses and seed correspondences across images and reconstructing the 3D implanted seeds. The feasibility of this approach was demonstrated through a series of simulations involving variable noise levels, seed densities, image separability and number of images. Preliminary results indicate mean reconstruction errors within 1.2 mm for noisy plans of 84 seeds or fewer. These are attained for additive noise whose standard deviation of the 3D mean error introduced to the plan to simulate the implant is within 3.2 mm.
前列腺近距离放射治疗的成功取决于剂量计划的准确实施。反过来,术中对植入的放射性近距离放射治疗种子进行定位和可视化,能够在治疗过程中更熟练、更明智地调整已执行的计划。先前的工作已经表明,使用外部跟踪设备或基于图像的基准来确定C形臂的姿态,可以从未校准的移动C形臂中对种子进行充分的重建。这些方法要么耗时,要么会干扰手术的临床流程,或者两者兼而有之。本文描述了一种既避免使用跟踪设备也避免使用基准的种子重建方法。相反,它使用术前剂量计划与一组捕获的图像相结合,来获得C形臂姿态的初始估计,然后使用种子本身作为基准的自动聚焦技术来细化姿态估计。术中种子定位是通过迭代求解跨图像的姿态和种子对应关系,并重建三维植入种子来实现的。通过一系列涉及可变噪声水平、种子密度、图像可分离性和图像数量的模拟,证明了该方法的可行性。初步结果表明,对于84颗或更少种子的噪声计划,平均重建误差在1.2毫米以内。这些结果是在模拟植入时引入到计划中的三维平均误差的标准偏差在3.2毫米以内的加性噪声情况下获得的。