Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
Phys Med Biol. 2012 Sep 7;57(17):5485-508. doi: 10.1088/0031-9155/57/17/5485. Epub 2012 Aug 3.
Surgical targeting of the incorrect vertebral level (wrong-level surgery) is among the more common wrong-site surgical errors, attributed primarily to the lack of uniquely identifiable radiographic landmarks in the mid-thoracic spine. The conventional localization method involves manual counting of vertebral bodies under fluoroscopy, is prone to human error and carries additional time and dose. We propose an image registration and visualization system (referred to as LevelCheck), for decision support in spine surgery by automatically labeling vertebral levels in fluoroscopy using a GPU-accelerated, intensity-based 3D-2D (namely CT-to-fluoroscopy) registration. A gradient information (GI) similarity metric and a CMA-ES optimizer were chosen due to their robustness and inherent suitability for parallelization. Simulation studies involved ten patient CT datasets from which 50 000 simulated fluoroscopic images were generated from C-arm poses selected to approximate the C-arm operator and positioning variability. Physical experiments used an anthropomorphic chest phantom imaged under real fluoroscopy. The registration accuracy was evaluated as the mean projection distance (mPD) between the estimated and true center of vertebral levels. Trials were defined as successful if the estimated position was within the projection of the vertebral body (namely mPD <5 mm). Simulation studies showed a success rate of 99.998% (1 failure in 50 000 trials) and computation time of 4.7 s on a midrange GPU. Analysis of failure modes identified cases of false local optima in the search space arising from longitudinal periodicity in vertebral structures. Physical experiments demonstrated the robustness of the algorithm against quantum noise and x-ray scatter. The ability to automatically localize target anatomy in fluoroscopy in near-real-time could be valuable in reducing the occurrence of wrong-site surgery while helping to reduce radiation exposure. The method is applicable beyond the specific case of vertebral labeling, since any structure defined in pre-operative (or intra-operative) CT or cone-beam CT can be automatically registered to the fluoroscopic scene.
手术中靶向错误的椎体水平(错误水平手术)是较为常见的错误部位手术错误之一,主要归因于中胸段脊柱缺乏独特的可识别的影像学标志。传统的定位方法涉及在透视下手动计数椎体,容易出现人为错误,并且需要额外的时间和剂量。我们提出了一种图像配准和可视化系统(称为 LevelCheck),通过使用 GPU 加速的基于强度的 3D-2D(即 CT 到透视)配准自动标记透视图像中的椎体水平,为脊柱手术提供决策支持。由于其稳健性和内在的并行化适用性,选择了梯度信息(GI)相似性度量和 CMA-ES 优化器。模拟研究涉及十个患者的 CT 数据集,从中生成了 50000 个从选择的 C 臂位置模拟的透视图像,以近似 C 臂操作员和定位变异性。物理实验使用在真实透视下成像的人体胸部模型进行。将配准精度评估为估计的和真实椎体中心之间的平均投影距离(mPD)。如果估计位置在椎体的投影范围内(即 mPD <5mm),则将试验定义为成功。模拟研究显示成功率为 99.998%(50000 次试验中有 1 次失败),在中等 GPU 上的计算时间为 4.7s。失败模式的分析确定了由于椎体结构的纵向周期性而在搜索空间中出现的假局部最优的情况。物理实验证明了算法对量子噪声和 X 射线散射的稳健性。在近乎实时的透视术中自动定位目标解剖结构的能力可以降低错误部位手术的发生,同时有助于减少辐射暴露。该方法不仅适用于椎体标记的具体情况,因为可以将术前(或术中)CT 或锥形束 CT 中定义的任何结构自动配准到透视场景。