Robust 3D-2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation.
Robust 3D-2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation.
复制标题
强大的3D-2D图像注册：在解剖变形的存在下应用于脊柱干预和椎骨标记。
DOI：
10.1088/0031-9155/58/23/8535
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发表时间：
2013-12-07
影响因子：
3.5
通讯作者：
Siewerdsen JH
中科院分区：
文献类型：
--
作者：
Otake Y;Wang AS;Webster Stayman J;Uneri A;Kleinszig G;Vogt S;Khanna AJ;Gokaslan ZL;Siewerdsen JH
我们提出了一个框架，用于鲁棒地估计3D体积图像和2D投影图像之间的配准，并评估其在存在解剖变形的情况下在脊椎定位的脊柱介入中的精度和鲁棒性。该框架采用归一化梯度信息相似性度量和多起始协方差矩阵自适应局部重启进化策略优化，提高了对变形和内容失配的鲁棒性。并行实现允许在计算时间上的数量级加速，并通过多开始全局优化提高配准的鲁棒性。实验涉及一个尸体标本和两个CT数据集（仰卧位和俯卧位）以及36个C形臂荧光透视图像，这些图像是在四个位置（仰卧位、俯卧位、仰卧位脊柱前凸、俯卧位脊柱后凸）、三个区域（胸部、腹部和腰椎）和三个几何放大率水平（1.7、2.0、2.4）下使用标本采集的。根据投影图像中估计目标点与真实目标点之间的投影距离误差（PDE）评价配准精度，包括14400次随机试验（72种配准场景下的200次试验），初始化误差高达±200 mm和±10°。在所有情况下，所得中值PDE均优于0.1 mm，这在一定程度上取决于输入CT和荧光透视图像的分辨率。尸体实验说明了鲁棒性和计算时间之间的权衡，使用在中档GPU（nVidia，GeForce GTX 690）上在54.0 ± 3.5 s内计算的1，718 664 ± 96 582个功能评估，椎体标记的成功率为99.993%（“成功”定义为PDE <5 mm）。产生更快搜索的参数（例如，较少的多次启动）降低了在大变形和不良初始化条件下的鲁棒性（对于在13.1秒内记录的相同数据，99.535%的成功率），但是给定良好的初始化（例如，±5 mm，假设初始运行稳健），可以在6.3 s内解决相同的配准，成功率为99 993%。以对患者变形鲁棒的方式将CT配准到荧光透视的能力在诸如放射治疗、介入放射学和辅助靶定位（例如，椎骨标记）。
We present a framework for robustly estimating registration between a 3D volume image and a 2D projection image and evaluate its precision and robustness in spine interventions for vertebral localization in the presence of anatomical deformation. The framework employs a normalized gradient information similarity metric and multi-start covariance matrix adaptation evolution strategy optimization with local-restarts, which provided improved robustness against deformation and content mismatch. The parallelized implementation allowed orders-of-magnitude acceleration in computation time and improved the robustness of registration via multi-start global optimization. Experiments involved a cadaver specimen and two CT datasets (supine and prone) and 36 C-arm fluoroscopy images acquired with the specimen in four positions (supine, prone, supine with lordosis, prone with kyphosis), three regions (thoracic, abdominal, and lumbar), and three levels of geometric magnification (1.7, 2.0, 2.4). Registration accuracy was evaluated in terms of projection distance error (PDE) between the estimated and true target points in the projection image, including 14 400 random trials (200 trials on the 72 registration scenarios) with initialization error up to ±200 mm and ±10°. The resulting median PDE was better than 0.1 mm in all cases, depending somewhat on the resolution of input CT and fluoroscopy images. The cadaver experiments illustrated the tradeoff between robustness and computation time, yielding a success rate of 99.993% in vertebral labeling (with `success' defined as PDE <5 mm) using 1,718 664 ± 96 582 function evaluations computed in 54.0 ± 3.5 s on a mid-range GPU (nVidia, GeForce GTX690). Parameters yielding a faster search (e.g., fewer multi-starts) reduced robustness under conditions of large deformation and poor initialization (99.535% success for the same data registered in 13.1 s), but given good initialization (e.g., ±5 mm, assuming a robust initial run) the same registration could be solved with 99 993% success in 6.3 s. The ability to register CT to fluoroscopy in a manner robust to patient deformation could be valuable in applications such as radiation therapy, interventional radiology, and an assistant to target localization (e.g., vertebral labeling) in image-guided spine surgery.