title: Enriching 3D optical surface scans with prior knowledge: tissue thickness computation by exploiting local neighborhoods.
abstract: OBJECTIVE
  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.
authors.affiliation: Institute for Robotics and Cognitive Systems, University of Lübeck, Ratzeburger Allee 160, Lübeck, Germany. wissel@rob.uni-luebeck.de.
authors.forename: Tobias
authors.initials: T
authors.lastname: Wissel
authors.affiliation: Institute for Robotics and Cognitive Systems, University of Lübeck, Ratzeburger Allee 160, Lübeck, Germany.
authors.forename: Patrick
authors.initials: P
authors.lastname: Stüber
authors.affiliation: Institute for Robotics and Cognitive Systems, University of Lübeck, Ratzeburger Allee 160, Lübeck, Germany.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Wagner
authors.affiliation: Institute for Robotics and Cognitive Systems, University of Lübeck, Ratzeburger Allee 160, Lübeck, Germany.
authors.forename: Ralf
authors.initials: R
authors.lastname: Bruder
authors.affiliation: Institute for Robotics and Cognitive Systems, University of Lübeck, Ratzeburger Allee 160, Lübeck, Germany.
authors.forename: Achim
authors.initials: A
authors.lastname: Schweikard
authors.affiliation: Institute for Robotics and Cognitive Systems, University of Lübeck, Ratzeburger Allee 160, Lübeck, Germany.
authors.forename: Floris
authors.initials: F
authors.lastname: Ernst
mesh_terms: D000465:Algorithms; D003952:Diagnostic Imaging / Q000295:instrumentation*; D004867:Equipment Design; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000295:instrumentation*; D008962:Models, Theoretical; D016011:Normal Distribution; D055096:Optical Devices
keywords: Head tracking; Laser scanning; Statistical learning; Tissue thickness