Guo Xiaoyu, Cheng Alexis, Zhang Haichong K, Kang Hyun-Jae, Etienne-Cummings Ralph, Boctor Emad M
Med Image Comput Comput Assist Interv. 2014;17(Pt 2):397-404. doi: 10.1007/978-3-319-10470-6_50.
In ultrasound-guided medical procedures, accurate tracking of interventional tools with respect to the US probe is crucial to patient safety and clinical outcome. US probe tracking requires an unavoidable calibration procedure to recover the rigid body transformation between the US image and the tracking coordinate system. In literature, almost all calibration methods have been performed on passive phantoms. There are several challenges to these calibration methods including dependency on ultrasound image quality and parameters such as frequency, depth, and beam-thickness. In this work, for the first time we introduce an active echo (AE) phantom for US calibration. The phantom actively detects and responds to the US beams from the imaging probe. This active approach allows reliable and accurate identification of the ultrasound image mid-plane independent of the image quality. It also enables automatic point segmentations. Both the target localization and segmentation can be done automatically, so the user dependency is minimized. The AE phantom is compared with a gold standard crosswire (CW) phantom in a robotic US experimental setup. The result indicates that AE calibration phantom provides a localization precision of 223 μm, and an overall reconstruction error of 850 μm. Autosegmentation is also tested and proved to have the similar performance as the manual segmentation.
在超声引导的医疗程序中，相对于超声探头精确跟踪介入工具对于患者安全和临床结果至关重要。超声探头跟踪需要一个不可避免的校准程序来恢复超声图像与跟踪坐标系之间的刚体变换。在文献中，几乎所有校准方法都是在被动体模上进行的。这些校准方法存在几个挑战，包括对超声图像质量以及频率、深度和波束厚度等参数的依赖。在这项工作中，我们首次引入了一种用于超声校准的有源回波（AE）体模。该体模能主动检测并响应来自成像探头的超声束。这种主动方法能够可靠且准确地识别超声图像中平面，而与图像质量无关。它还能实现自动点分割。目标定位和分割都可以自动完成，从而将用户依赖性降至最低。在机器人超声实验装置中，将AE体模与金标准十字线（CW）体模进行了比较。结果表明，AE校准体模的定位精度为223μm，整体重建误差为850μm。还对自动分割进行了测试，结果证明其性能与手动分割相似。
