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基于光声的针尖视觉伺服控制。

Photoacoustic-based visual servoing of a needle tip.

机构信息

Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, MD, 21218, USA.

Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, 21218, USA.

出版信息

Sci Rep. 2018 Oct 19;8(1):15519. doi: 10.1038/s41598-018-33931-9.

Abstract

In intraoperative settings, the presence of acoustic clutter and reflection artifacts from metallic surgical tools often reduces the effectiveness of ultrasound imaging and complicates the localization of surgical tool tips. We propose an alternative approach for tool tracking and navigation in these challenging acoustic environments by augmenting ultrasound systems with a light source (to perform photoacoustic imaging) and a robot (to autonomously and robustly follow a surgical tool regardless of the tissue medium). The robotically controlled ultrasound probe continuously visualizes the location of the tool tip by segmenting and tracking photoacoustic signals generated from an optical fiber inside the tool. System validation in the presence of fat, muscle, brain, skull, and liver tissue with and without the presence of an additional clutter layer resulted in mean signal tracking errors <2 mm, mean probe centering errors <1 mm, and successful recovery from ultrasound perturbations, representing either patient motion or switching from photoacoustic images to ultrasound images to search for a target of interest. A detailed analysis of channel SNR in controlled experiments with and without significant acoustic clutter revealed that the detection of a needle tip is possible with photoacoustic imaging, particularly in cases where ultrasound imaging traditionally fails. Results show promise for guiding surgeries and procedures in acoustically challenging environments with this novel robotic and photoacoustic system combination.

摘要

在术中环境中,来自金属手术工具的声波杂波和反射伪影的存在通常会降低超声成像的效果,并使手术工具尖端的定位复杂化。我们提出了一种替代方法,通过为超声系统增加光源(执行光声成像)和机器人(无论组织介质如何,都能自动且稳健地跟踪手术工具)来跟踪和导航这些具有挑战性的声学环境中的工具。机器人控制的超声探头通过分割和跟踪工具内部光纤产生的光声信号,连续可视化工具尖端的位置。在存在和不存在额外杂波层的情况下,在脂肪、肌肉、大脑、颅骨和肝脏组织中的系统验证导致平均信号跟踪误差<2mm、平均探头中心误差<1mm 以及成功从超声干扰中恢复,代表患者运动或从光声图像切换到超声图像以搜索感兴趣的目标。在有和没有显著声杂波的受控实验中对通道 SNR 进行的详细分析表明,光声成像是可能检测到针尖的,特别是在传统的超声成像失败的情况下。结果表明,该新型机器人和光声系统组合在具有挑战性的声学环境中引导手术和手术具有很大的应用潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88bd/6195562/4dfbd134513d/41598_2018_33931_Fig1_HTML.jpg

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