Abstract
For training sonographers in navigating, acquiring, and interpreting ultrasound images, virtual-reality based simulation offers a safe, flexible, and standardized environment. In data-based training simulations, images from a-priori acquired volumes are displayed to the trainee. To understand the relationship between organs, it is necessary to allow for free exploration of the entire anatomy, which is often not possible with the limited field-of-view (FOV) of a single ultrasound volume. Thus, large FOV ultrasound volumes are of paramount importance. Combining several volumes into one larger volume has also potential utility in many other applications, such as diagnostic and operative guidance. In this work, we propose a method for combining several ultrasound volumes with tracked positions into a single large volume by stitching them in a seamless fashion. For stitching, we determine an optimal cut interface such that each pixel value comes from a single image; preserving the inherent speckle texture and preventing any blurring and degradation from common mean/median binning approaches to combining volumes. The cut interface is found based on image content using graphical models optimized by graph-cut. We show that our method produces panoramic reconstructions with seamless transitions between individual 3D acquisitions. Regarding standard deviation in homogeneous regions we get 1–19% loss of ultrasound texture compared to small 3D volumes while mean value interpolation gives a loss of 15–68%. The histograms of our reconstruction match the original histograms of the small 3D volumes almost perfectly with a \(\chi ^2\)-distance of less than 0.01.
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Acknowledgments
We thank Prof. Dr. med. Michael Bajka for his help in data acquisition and the Swiss CTI and NSF for funding.
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Flach, B., Makhinya, M., Goksel, O. (2016). PURE: Panoramic Ultrasound Reconstruction by Seamless Stitching of Volumes. In: Tsaftaris, S., Gooya, A., Frangi, A., Prince, J. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2016. Lecture Notes in Computer Science(), vol 9968. Springer, Cham. https://doi.org/10.1007/978-3-319-46630-9_8
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DOI: https://doi.org/10.1007/978-3-319-46630-9_8
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