Cheng Alexis, Guo Xiaoyu, Zhang Haichong K, Kang Hyun Jae, Etienne-Cummings Ralph, Boctor Emad M
Johns Hopkins University, Department of Computer Science, Baltimore, Maryland, United States.
Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, United States.
J Med Imaging (Bellingham). 2017 Jul;4(3):035001. doi: 10.1117/1.JMI.4.3.035001. Epub 2017 Jul 27.
In ultrasound (US)-guided medical procedures, accurate tracking of interventional tools is crucial to patient safety and clinical outcome. This requires a calibration procedure to recover the relationship between the US image and the tracking coordinate system. In literature, calibration has been performed on passive phantoms, which depend on image quality and parameters, such as frequency, depth, and beam-thickness as well as in-plane assumptions. In this work, we introduce an active phantom for US calibration. This phantom actively detects and responds to the US beams transmitted from the imaging probe. This active echo (AE) approach allows identification of the US image midplane independent of image quality. Both target localization and segmentation can be done automatically, minimizing user dependency. The AE phantom is compared with a crosswire phantom in a robotic US setup. An out-of-plane estimation US calibration method is also demonstrated through simulation and experiments to compensate for remaining elevational uncertainty. The results indicate that the AE calibration phantom can have more consistent results across experiments with varying image configurations. Automatic segmentation is also shown to have similar performance to manual segmentation.
在超声(US)引导的医疗程序中,介入工具的精确跟踪对于患者安全和临床结果至关重要。这需要一个校准程序来恢复超声图像与跟踪坐标系之间的关系。在文献中,校准是在被动体模上进行的,被动体模依赖于图像质量和参数,如频率、深度、波束厚度以及平面内假设。在这项工作中,我们引入了一种用于超声校准的主动体模。该体模能主动检测并响应成像探头发射的超声束。这种主动回声(AE)方法允许独立于图像质量识别超声图像中平面。目标定位和分割都可以自动完成,最大限度地减少对用户的依赖。在机器人超声设置中,将AE体模与十字线体模进行了比较。还通过模拟和实验展示了一种平面外估计超声校准方法,以补偿剩余的仰角不确定性。结果表明,AE校准体模在不同图像配置的实验中可以有更一致的结果。自动分割也显示出与手动分割相似的性能。