Abstract
We introduce an Iterative Closest Point framework for ultrasound calibration based on a hollow-line phantom. The main novelty of our approach is the application of a hollow-tube fiducial made from hyperechoic material, which allows for highly accurate fiducial localization via both manual and automatic segmentation. By reducing fiducial localization error, this framework is able to achieve sub-millimeter target registration error. The calibration phantom introduced can be manufactured inexpensively and precisely. Using a Monte Carlo approach, our calibration framework achieved 0.5 mm mean target registration error, with a standard deviation of 0.24 mm, using 12 or more tracked ultrasound images. This suggests that our framework is approaching the accuracy limit imposed by the tracking device used.
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Chen, E.C.S., McLeod, A.J., Baxter, J.S.H., Peters, T.M. (2015). An Iterative Closest Point Framework for Ultrasound Calibration. In: Linte, C., Yaniv, Z., Fallavollita, P. (eds) Augmented Environments for Computer-Assisted Interventions. AE-CAI 2015. Lecture Notes in Computer Science(), vol 9365. Springer, Cham. https://doi.org/10.1007/978-3-319-24601-7_8
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