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首页> 外文期刊>The international journal of medical robotics + computer assisted surgery: MRCAS >Safety margins in robotic bone milling: from registration uncertainty to statistically safe surgeries
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Safety margins in robotic bone milling: from registration uncertainty to statistically safe surgeries

机译:机器人骨铣削安全边缘:从注册不确定性到统计安全的手术

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Abstract Background 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. Methods 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. Results 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. Conclusions 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.
机译:摘要背景当机器人磨骨近临界结构时,使用安全边距来降低由于登记不准确而导致的意外损坏的风险。这些边距通常以均匀的厚度设定出色,这不会反映登记误差的各向异性和空间方差。方法描述一种方法,用于使用注册不确定度的统计模型在重要的解剖结构周围产生空间不同的安全余量。数值模拟用于确定与外科医生指定的安全阈值匹配的边缘几何。结果在乳腺切除术语境中将算法应用于五个时间骨的CT扫描,耳术中的常见骨铣过程必须接近生命神经。生成安全边距,满足各种情况下的指定安全水平。结论通过在生命解剖学周围的安全利润中纳入注册不确定性的统计模型,可以增加图像引导手术中的患者安全性。

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