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A Standardized Method for Accuracy Study of MRI-Compatible Robots: Case Study- A Body-Mounted Robot

机译:MRI兼容机器人精度研究的标准化方法:案例研究-人体安装机器人

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Here we report on a phantom targeting study for accuracy evaluation of our body-mounted robot for Magnetic Resonance Imaging (MRI) guided arthrography. We use a standardized method developed in a multi-institute effort with the aim of providing an objective method for accuracy and signal-to-noise Ratio (SNR) evaluation of MRI-compatible robots. The medical definition of arthrography is the radiographic visualization of a joint (as the hip or shoulder) after the injection of a radiopaque substance. That procedure provides an evaluation of the joints using two medical imaging modalities, fluoroscopic x-ray imaging and MRI. Conventional arthrography is done in two stages: first the contrast dye injected into the joint (fluoroscopic procedure) and then an MRI to evaluate the joint space. Our MRI-guided compatible robot is intended to enable needle placement in the MRI environment, streamlining the procedure. The targeting study was conducted using the quality assessment mockup phantom and associated software called QARAI that was developed by the URobotics Laboratory at Johns Hopkins and colleagues. The mockup contains four embedded fiducials and an 8 by 8 grid which is used to automatically identify the targeting points with high accuracy. The study was conducted on a Philips Achieva 1.5T MRI system and 10 points were targeted. All targets were reached with an average error of 2.71mm. The targeting algorithm, as well as the control of the robot, were completed using robot control modules developed with the open source software 3D Slicer.
机译:在这里,我们报告了一项针对人体模型的幻影目标研究,以评估我们的磁共振成像(MRI)引导关节成像机器人。我们使用在多个机构共同努力下开发的标准化方法,旨在为MRI兼容机器人的准确性和信噪比(SNR)评估提供一种客观的方法。关节造影的医学定义是注射不透射线的物质后关节(如髋部或肩部)的射线照相可视化。该程序使用两种医学成像模式(荧光X射线成像和MRI)对关节进行评估。传统的关节造影术分两个阶段进行:首先将造影剂注入关节(荧光镜检查程序),然后进行MRI以评估关节间隙。我们的MRI引导兼容机器人旨在在MRI环境中放置针头,从而简化操作过程。靶向研究是使用质量评估模型模型和相关软件QARAI进行的,该软件由Johns Hopkins的URobotics实验室及其同事开发。该模型包含四个嵌入式基准点和一个8 x 8的网格,该网格用于自动高精度地识别目标点。该研究是在Philips Achieva 1.5T MRI系统上进行的,目标是10点。达到所有目标,平均误差为2.71mm。使用由开源软件3D Slicer开发的机器人控制模块,可以完成瞄准算法以及机器人的控制。

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