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Active Echo: A New Paradigm for Ultrasound Calibration

  • Xiaoyu Guo
  • Alexis Cheng
  • Haichong K. Zhang
  • Hyun-Jae Kang
  • Ralph Etienne-Cummings
  • Emad M. Boctor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

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. Auto-segmentation is also tested and proved to have the similar performance as the manual segmentation.

Keywords

Manual Segmentation Localization Precision Variable Gain Amplifier Rigid Body Transformation Automatic Segmentation Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiaoyu Guo
    • 1
  • Alexis Cheng
    • 1
  • Haichong K. Zhang
    • 1
  • Hyun-Jae Kang
    • 1
  • Ralph Etienne-Cummings
    • 1
  • Emad M. Boctor
    • 1
  1. 1.The Johns Hopkins UniversityBaltimoreUSA

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