PURE: Panoramic Ultrasound Reconstruction by Seamless Stitching of Volumes

  • Barbara Flach
  • Maxim Makhinya
  • Orcun Goksel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9968)


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.


Coordinate Frame Pressure Correction Input Volume Reconstructed Volume Ultrasound Volume 
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.



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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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Computer-Assisted Applications in MedicineETH ZürichZürichSwitzerland

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