Active phantoms: a paradigm for ultrasound calibration using phantom feedback

Alexis Cheng; Xiaoyu Guo; Haichong K. Zhang; Hyun J. Kang; Ralph Etienne-Cummings; Emad M. Boctor

doi:10.1117/1.JMI.4.3.035001

27 July 2017 Active phantoms: a paradigm for ultrasound calibration using phantom feedback

Alexis Cheng, Xiaoyu Guo, Haichong K. Zhang, Hyun J. Kang, Ralph Etienne-Cummings, Emad M. Boctor

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 3, 035001 (July 2017). https://doi.org/10.1117/1.JMI.4.3.035001

Abstract

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.

Citation Download Citation

Alexis Cheng, Xiaoyu Guo, Haichong K. Zhang, Hyun J. Kang, Ralph Etienne-Cummings, and Emad M. Boctor "Active phantoms: a paradigm for ultrasound calibration using phantom feedback," Journal of Medical Imaging 4(3), 035001 (27 July 2017). https://doi.org/10.1117/1.JMI.4.3.035001

Received: 4 August 2016; Accepted: 7 July 2017; Published: 27 July 2017

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