Siebold Michael A, Dillon Neal P, Fichera Loris, Labadie Robert F, Webster Robert J, Fitzpatrick J Michael
Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA.
Department of Mechanical Engineering, Vanderbilt University, Nashville, Tennessee, USA.
Int J Med Robot. 2017 Sep;13(3). doi: 10.1002/rcs.1773. Epub 2016 Sep 21.
When robots mill bone near critical structures, safety margins are used to reduce the risk of accidental damage due to inaccurate registration. These margins are typically set heuristically with uniform thickness, which does not reflect the anisotropy and spatial variance of registration error.
A method is described to generate spatially varying safety margins around vital anatomy using statistical models of registration uncertainty. Numerical simulations are used to determine the margin geometry that matches a safety threshold specified by the surgeon.
The algorithm was applied to CT scans of five temporal bones in the context of mastoidectomy, a common bone milling procedure in ear surgery that must approach vital nerves. Safety margins were generated that satisfied the specified safety levels in every case.
Patient safety in image-guided surgery can be increased by incorporating statistical models of registration uncertainty in the generation of safety margins around vital anatomy.
当机器人在关键结构附近磨削骨骼时，安全边界用于降低因配准不准确而导致意外损伤的风险。这些边界通常凭经验设置为均匀厚度，这并未反映配准误差的各向异性和空间变化。
描述了一种使用配准不确定性统计模型在重要解剖结构周围生成空间变化安全边界的方法。数值模拟用于确定与外科医生指定的安全阈值相匹配的边界几何形状。
该算法应用于乳突切除术背景下的五块颞骨CT扫描，乳突切除术是耳部手术中一种常见的骨骼磨削手术，必须靠近重要神经。在每种情况下都生成了满足指定安全水平的安全边界。
通过在重要解剖结构周围生成安全边界时纳入配准不确定性统计模型，可以提高图像引导手术中的患者安全性。
