基于iOCT制导的自主机器人视网膜下注射实时变形感知控制。

Demir Arikan, Peiyao Zhang, Michael Sommersperger, Shervin Dehghani, Mojtaba Esfandiari, Russel H Taylor, M Ali Nasseri, Peter Gehlbach, Nassir Navab, Iulian Iordachita
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引用次数: 0

摘要

机器人平台提供一致和精确的工具定位,显著提高视网膜显微手术。将这些系统与术中光学相干断层扫描(iOCT)相结合,可以实现图像引导的机器人干预,允许自主执行高级治疗,例如向视网膜下间隙注射治疗剂。然而,由于工具-组织相互作用导致的组织变形构成了自主的ioct引导机器人视网膜下注射的重大挑战。这种相互作用影响正确的针头定位和手术结果。本文提出了一种在iOCT引导下进行自主视网膜下注射的新方法,该方法在插入过程中考虑了组织变形。该技术通过对密集采样的iOCT b扫描(我们称之为b5扫描)进行手术场景的实时分割和3D重建来实现。使用b5扫描,我们监测仪器相对于ILM和RPE之间的虚拟目标层的位置。我们在离体猪眼上的实验表明,与之前的自主插入方法相比,该方法可以动态调整插入深度,总体上提高了针头定位的准确性。与以往方法产生视网膜下气泡的成功率35%相比,我们的方法在90%的实验中可靠地产生了视网膜下气泡。本研究中使用的源代码和数据在GitHub上是公开的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time Deformation-aware Control for Autonomous Robotic Subretinal Injection under iOCT Guidance.

Robotic platforms provide consistent and precise tool positioning that significantly enhances retinal microsurgery. Integrating such systems with intraoperative optical coherence tomography (iOCT) enables image-guided robotic interventions, allowing autonomous performance of advanced treatments, such as injecting therapeutic agents into the subretinal space. However, tissue deformations due to tool-tissue interactions constitute a significant challenge in autonomous iOCT-guided robotic subretinal injections. Such interactions impact correct needle positioning and procedure outcomes. This paper presents a novel method for autonomous subretinal injection under iOCT guidance that considers tissue deformations during the insertion procedure. The technique is achieved through real-time segmentation and 3D reconstruction of the surgical scene from densely sampled iOCT B-scans, which we refer to as B5-scans. Using B5-scans we monitor the position of the instrument relative to a virtual target layer between the ILM and RPE. Our experiments on ex-vivo porcine eyes demonstrate dynamic adjustment of the insertion depth and overall improved accuracy in needle positioning compared to prior autonomous insertion approaches. Compared to a 35% success rate in subretinal bleb generation with previous approaches, our method reliably created subretinal blebs in 90% our experiments. The source code and data used in this study are publicly available on GitHub.

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CiteScore
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