Thayer School of Engineering, Dartmouth College, 15 Thayer Drive, Hanover, NH, 03755, USA.
Dartmouth-Hitchcock Medical Center, Section of Urology, 1 Medical Center Drive, Lebanon, NH, 03756, USA.
Int J Comput Assist Radiol Surg. 2022 Jun;17(6):1079-1089. doi: 10.1007/s11548-022-02638-8. Epub 2022 May 5.
Traditional soft tissue registration methods require direct intraoperative visualization of a significant portion of the target anatomy in order to produce acceptable surface alignment. Image guidance is therefore generally not available during the robotic exposure of structures like the kidneys which are not immediately visualized upon entry into the abdomen. This paper proposes guiding surgical exposure with an iterative state estimator that assimilates small visual cues into an a priori anatomical model as exposure progresses, thereby evolving pose estimates for the occluded structures of interest.
Intraoperative surface observations of a right kidney are simulated using endoscope tracking and preoperative tomography from a representative robotic partial nephrectomy case. Clinically relevant random perturbations of the true kidney pose are corrected using this sequence of observations in a particle filter framework to estimate an optimal similarity transform for fitting a patient-specific kidney model at each step. The temporal response of registration error is compared against that of serial rigid coherent point drift (CPD) in both static and simulated dynamic surgical fields, and for varying levels of observation persistence.
In the static case, both particle filtering and persistent CPD achieved sub-5 mm accuracy, with CPD processing observations 75% faster. Particle filtering outperformed CPD in the dynamic case under equivalent computation times due to the former requiring only minimal persistence.
This proof-of-concept simulation study suggests that Bayesian state estimation may provide a viable pathway to image guidance for surgical exposure in the abdomen, especially in the presence of dynamic intraoperative tissue displacement and deformation.
传统的软组织配准方法需要直接在手术过程中可视化目标解剖结构的很大一部分，才能产生可接受的表面配准。因此，在机器人暴露肾脏等结构时，通常无法使用图像引导，因为这些结构在进入腹部时不会立即被可视化。本文提出了一种迭代状态估计器来指导手术暴露，该估计器将小的视觉线索吸收到先验解剖模型中，随着暴露的进行，从而为感兴趣的被遮挡结构进化姿态估计。
使用内窥镜跟踪和来自代表性机器人部分肾切除术病例的术前断层扫描来模拟右肾的术中表面观察。使用该序列观察在粒子滤波器框架中校正临床相关的真实肾脏姿态的随机扰动，以估计在每个步骤中拟合患者特定的肾脏模型的最佳相似性变换。将注册误差的时间响应与串行刚性相干点漂移（CPD）在静态和模拟动态手术场中的响应进行比较，并针对不同的观察持续时间进行比较。
在静态情况下，粒子滤波和持久 CPD 都达到了亚 5 毫米的精度，CPD 处理观察的速度快 75%。由于前者仅需要最小的持久性，因此在等效计算时间下，粒子滤波在动态情况下优于 CPD。
这项概念验证的模拟研究表明，贝叶斯状态估计可能为腹部手术暴露提供可行的图像引导途径，特别是在存在动态术中组织位移和变形的情况下。
