Backward Planning for a Multi-Stage Steerable Needle Lung Robot.
Backward Planning for a Multi-Stage Steerable Needle Lung Robot.
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DOI：
10.1109/lra.2021.3066962
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发表时间：
2021-04
影响因子：
5.2
通讯作者：
Alterovitz R
中科院分区：
文献类型：
--
作者：
Hoelscher J;Fu M;Fried I;Emerson M;Ertop TE;Rox M;Kuntz A;Akulian JA;Webster RJ 3rd;Alterovitz R
肺癌是最致命的癌症之一，早期诊断对成功治疗至关重要。明确诊断肺癌通常需要活检，但目前的方法要么对患者有很高的手术风险，要么无法到达肺的许多临床感兴趣的部位。我们提出了一种新的基于采样的可操纵针肺机器人规划方法，该方法具有准确到达肺大部分区域的潜力。该机器人包括三个阶段：经口部署的支气管镜，磨尖的穿刺管（从气道刺入肺实质），以及能够导航到目标的可操纵针。考虑到健康安全问题，规划所有三个阶段的顺序部署是一项具有挑战性的任务，因为每个阶段都依赖于前一个阶段。我们介绍了一种新的向后规划方法，从目标开始，向后向气道推进，目标是找到支气管镜可到达的穿孔部位。这种新策略通过在整个计算周期内迭代地构建单个搜索树来实现更快的性能，而以前的正向方法依赖于多次重复这种昂贵的树构建过程。此外，我们的方法通过采用基于几何约束的偏抽样和样本拒绝进一步缩短了运行时间。我们在解剖肺模型中使用基于模拟的研究来评估这种方法。与现有技术相比，我们证明了新方法(i)更有可能找到通往目标的路径，（ii）达到目标的平均速度超过5倍，效率更高，（iii）在更短的时间内到达风险更低的路径。
Lung cancer is one of the deadliest types of cancer, and early diagnosis is crucial for successful treatment. Definitively diagnosing lung cancer typically requires biopsy, but current approaches either carry a high procedural risk for the patient or are incapable of reaching many sites of clinical interest in the lung. We present a new sampling-based planning method for a steerable needle lung robot that has the potential to accurately reach targets in most regions of the lung. The robot comprises three stages: a transorally deployed bronchoscope, a sharpened piercing tube (to pierce into the lung parenchyma from the airways), and a steerable needle able to navigate to the target. Planning for the sequential deployment of all three stages under health safety concerns is a challenging task, as each stage depends on the previous one. We introduce a new backward planning approach that starts at the target and advances backwards toward the airways with the goal of finding a piercing site reachable by the bronchoscope. This new strategy enables faster performance by iteratively building a single search tree during the entire computation period, whereas previous forward approaches have relied on repeating this expensive tree construction process many times. Additionally, our method further reduces runtime by employing biased sampling and sample rejection based on geometric constraints. We evaluate this approach using simulation-based studies in anatomical lung models. We demonstrate in comparison with existing techniques that the new approach (i) is more likely to find a path to a target, (ii) is more efficient by reaching targets more than 5 times faster on average, and (iii) arrives at lower-risk paths in shorter time.
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4.8
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Li, BJ;Christensen, GE;Reinhardt, JM
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