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



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.


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.


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.


Head tracking Laser scanning  Statistical learning Tissue thickness 



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.


  1. 1.
    Fuss M, Salter BJ, Cheek D, Sadeghi A, Hevezi JM, Herman TS (2004) Repositioning accuracy of a commercially available thermoplastic mask system. Radiother Oncol 71:339–345CrossRefPubMedGoogle Scholar
  2. 2.
    Minniti G, Clarke E, Cavallo L, Osti MF, Esposito V, Cantore G, Cappabianca P, Enrici RM (2011) Fractionated stereotactic conformal radiotherapy for large benign skull base meningiomas. Radiat Oncol 6:1CrossRefGoogle Scholar
  3. 3.
    Tryggestad E, Christian M, Ford E, Kut C, Le Y, Sanguineti YG, Song DY, Kleinberg L (2011) Inter- and intrafraction patient positioning uncertainties for intracranial radiotherapy: a study of four frameless, thermoplastic mask-based immobilization strategies using daily cone-beam CT. Int J Radiat Oncol* Biol* Phys 80:281–290Google Scholar
  4. 4.
    Kurup G (2010) CyberKnife: a new paradigm in radiotherapy. J Med Phys 35:63–64CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Peng JL, Kahler D, Li JG, Samant S, Yan G, Amdur R, Liu C (2010) Characterization of a real-time surface image-guided stereotactic positioning system. Med Phys 37:5421–5433Google Scholar
  6. 6.
    Cervino LI, Detorie N, Taylor M, Lawson JD, Harry T, Murphy KT, Mundt AJ, Jiang SB, Pawlicki TA (2012) Initial clinical experience with a frameless and maskless stereotactic radiosurgery treatment. Pract Radiat Oncol 2:54–62CrossRefPubMedGoogle Scholar
  7. 7.
    Gopan O, Wu Q (2012) Evaluation of the accuracy of a 3D surface imaging system for patient setup in head and neck cancer radiotherapy. Int J Radiat Oncol* Biol* Phys 84:547–552CrossRefGoogle Scholar
  8. 8.
    Kim Y, Li R, Na YH, Lee R, Xing L (2014) Accuracy of surface registration compared to conventional volumetric registration in patient positioning for head-and-neck radiotherapy: a simulation study using patient data. Med Phys 41:121701CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Ernst F, Bruder R, Wissel T, Stüber P, Wagner B, Schweikard A (2013) Real time contact-free and non-invasive tracking of the human skull first light and initial validation. In: Applications of digital image processing XXXVI, proceedings of SPIE 8856, San Diego, pp 88561G-1–88561G-8Google Scholar
  10. 10.
    Wissel T, Stüber P, Wagner B, Bruder R, Schweikard A, Ernst F (2013) Preliminary study on optical feature detection for head tracking in radiation therapy, 13th IEEE international conference on bioInformatics and bioengineering (BIBE). IEEE, Chania, Greece, pp 1–5Google Scholar
  11. 11.
    Wagner B, Stüber P, Wissel T, Bruder R, Schweikard A, Ernst F (2014) Accuracy analysis for triangulation and tracking based on time-multiplexed structured light. Med Phys 41(8):82701-1-9Google Scholar
  12. 12.
    Wissel T, Bruder R, Schweikard A, Ernst F (2013) Estimating soft tissue thickness from light-tissue interactions—a simulation study. Biomed Opt Express 4:1176–1187CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Wissel T, Stueber P, Wagner B, Dürichen R, Bruder R, Schweikard A, Ernst F (2014) Tissue thickness estimation for high precision head-tracking using a galvanometric laser scanner—a case study. In: 36th annual international conference of the IEEE engineering in medicine and biology society (EMBC‘14). IEEE, Chicago, USA, pp 3106–3109Google Scholar
  14. 14.
    Wissel T, Stüber P, Wagner B, Bruder R, Schweikard A, Ernst F (2014) Angle influence and compensation for marker-less head tracking based on laser scanners. In: Proceedings of the 28th international congress and exhibition on computer assisted radiology and surgery (CARS’14), Fukuoka, Japan, vol 9, no S1, pp 62–63Google Scholar
  15. 15.
    Abdel-Aziz YI, Karara HM (1971) Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. Proceedings of the symposium on close-range photogrammetry, Falls Church, VAGoogle Scholar
  16. 16.
    Besl PJ, Keil ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal 14:239–256CrossRefGoogle Scholar
  17. 17.
    Smola A, Schölkopf B (2004) A tutorial on Support Vector Regression. Stat Comput 14:199–222Google Scholar
  18. 18.
    Rasmussen CE, Williams CKI (2006) Gaussian Processes for machine learning. MIT Press.
  19. 19.
    Verleysen M, Francois D (2005) The curse of dimensionality in data mining and time series prediction. In: Proceedings of the 8th international workshop on artificial neural networks (IWANN 2005) Barcelona, Spain, pp 758–770Google Scholar

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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