title: Efficient estimation of tissue thicknesses using sparse approximation for Gaussian processes.
abstract: Highly accurate localization of the human skull is vital in cranial radiotherapy. Marker-less optical head tracking provides a fast and accurate way to monitor this motion. Recent research has given evidence that marker-less tracking of the forehead benefits from tissue thickness information in addition to the 3D surface geometry. Using Gaussian Processes (GPs) tissue thickness is determined from optical backscatter of a sweeping laser. However, the computational complexity of the GPs scales cubically with the number of training samples. A full head scan contains 1024 points, whereas scans from several perspectives may be required for a comprehensive model for each subject. In five subjects, we thus evaluate sparse approximation methods to reduce the computational effort. We found a better - computation time versus root mean square error (RMSE) - tradeoff for a simple subset of data (SoD) technique. The increase of RMSE when dropping data was not found steep enough to justify the computational overhead of a better approximation by inducing point methods (namely FITC). Promising results were, however, obtained when clustering the training data before selecting the subset.
authors.forename: Tobias
authors.initials: T
authors.lastname: Wissel
authors.forename: Patrick
authors.initials: P
authors.lastname: Stuber
authors.forename: Benjamin
authors.initials: B
authors.lastname: Wagner
authors.forename: Achim
authors.initials: A
authors.lastname: Schweikard
authors.forename: Floris
authors.initials: F
authors.lastname: Ernst
mesh_terms: D000328:Adult; D005260:Female; D006257:Head / Q000033:anatomy & histology*; D006801:Humans; D021621:Imaging, Three-Dimensional; D007834:Lasers; D008297:Male; D008962:Models, Theoretical; D016011:Normal Distribution; D015203:Reproducibility of Results