Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Phys Med Biol. 2011 Aug 7;56(15):5011-27. doi: 10.1088/0031-9155/56/15/022. Epub 2011 Jul 19.
The success of prostate brachytherapy critically depends on delivering adequate dose to the prostate gland, and the capability of intraoperatively localizing implanted seeds provides potential for dose evaluation and optimization during therapy. REDMAPS is a recently reported algorithm that carries out seed localization by detecting, matching and reconstructing seeds in only a few seconds from three acquired x-ray images (Lee et al 2011 IEEE Trans. Med. Imaging 29 38-51). In this paper, we present an automatic pose correction (APC) process that is combined with REDMAPS to allow for both more accurate seed reconstruction and the use of images with relatively large pose errors. APC uses a set of reconstructed seeds as a fiducial and corrects the image pose by minimizing the overall projection error. The seed matching and APC are iteratively computed until a stopping condition is met. Simulations and clinical studies show that APC significantly improves the reconstructions with an overall average matching rate of ⩾99.4%, reconstruction error of ⩽0.5 mm, and the matching solution optimality of ⩾99.8%.
前列腺近距离放射治疗的成功在很大程度上取决于将足够的剂量递送至前列腺,而术中定位植入种子的能力为治疗期间的剂量评估和优化提供了潜力。REDMAPS 是一种最近报道的算法,它通过仅从三个获取的 X 射线图像中检测、匹配和重建几秒钟内的种子来执行种子定位(Lee 等人,2011 年 IEEE 医学成像汇刊 29 38-51)。在本文中,我们提出了一种自动姿态校正(APC)过程,该过程与 REDMAPS 结合使用,以允许更准确地重建种子,并使用具有相对较大姿态误差的图像。APC 使用一组重建的种子作为基准,并通过最小化整体投影误差来校正图像姿态。种子匹配和 APC 是迭代计算的,直到满足停止条件。模拟和临床研究表明,APC 显著改善了重建,整体平均匹配率 ⩾99.4%,重建误差 ⩽0.5 毫米,匹配解决方案最优性 ⩾99.8%。