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Enriching 3D optical surface scans with prior knowledge: tissue thickness computation by exploiting local neighborhoods

  • Tobias Wissel
  • Patrick Stüber
  • Benjamin Wagner
  • Ralf Bruder
  • Achim Schweikard
  • Floris Ernst
Original Article

Abstract

Purpose

Patient immobilization and X-ray-based imaging provide neither a convenient nor a very accurate way to ensure low repositioning errors or to compensate for motion in cranial radiotherapy. We therefore propose an optical tracking device that exploits subcutaneous structures as landmarks in addition to merely spatial registration. To develop such head tracking algorithms, precise and robust computation of these structures is necessary. Here, we show that the tissue thickness can be predicted with high accuracy and moreover exploit local neighborhood information within the laser spot grid on the forehead to further increase this estimation accuracy.

Methods

We use statistical learning with Support Vector Regression and Gaussian Processes to learn a relationship between optical backscatter features and an MR tissue thickness ground truth. We compare different kernel functions for the data of five different subjects. The incident angle of the laser on the forehead as well as local neighborhoods is incorporated into the feature space. The latter represent the backscatter features from four neighboring laser spots.

Results

We confirm that the incident angle has a positive effect on the estimation error of the tissue thickness. The root-mean-square error falls even below 0.15 mm when adding the complete neighborhood information. This prior knowledge also leads to a smoothing effect on the reconstructed skin patch. Learning between different head poses yields similar results. The partial overlap of the point clouds makes the trade-off between novel information and increased feature space dimension obvious and hence feature selection by e.g., sequential forward selection necessary.

Keywords

Head tracking Laser scanning  Statistical learning Tissue thickness 

Notes

Acknowledgments

This work (involving Tobias Wissel, Patrick Stüber, Benjamin Wagner, Ralf Bruder, Achim Schweikard and Floris Ernst) was supported by Varian Medical Systems, Inc. (Palo Alto, CA, USA) and by the Graduate School for Computing in Medicine and Life Sciences, funded by Germany’s Excellence Initiative [DFG GSC 235/1]. The authors further acknowledge the support of Dr. C.-S. Schwarz and Dr. Jacobsen from the clinic of oral and maxillofacial surgery, University Hospital Schleswig-Holstein as well as Dr. Uwe Melchert, Christian Erdmann and Professor Dr. Georg Schramm from the Institute for Neuroradiology.

Compliance with ethical standards

Conflicts of interest

Tobias Wissel, Patrick Stüber, Benjamin Wagner, Ralf Bruder, Achim Schweikard, and Floris Ernst have no conflict of interest to declare.

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Copyright information

© CARS 2015

Authors and Affiliations

  • Tobias Wissel
    • 1
  • Patrick Stüber
    • 1
  • Benjamin Wagner
    • 1
  • Ralf Bruder
    • 1
  • Achim Schweikard
    • 1
  • Floris Ernst
    • 1
  1. 1.Institute for Robotics and Cognitive SystemsUniversity of LübeckLübeckGermany

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