Advertisement

A simulation and training environment for robotic radiosurgery

  • Alexander Schlaefer
  • Jakub Gill
  • Achim Schweikard
Original Article

Abstract

Objective

To provide a software environment for simulation of robotic radiosurgery, particularly to study the effective robot workspace with respect to the treatment plan quality, and to illustrate the concepts of robotic radiosurgery.

Materials and methods

A simulation environment for a robotic radiosurgery system was developed using Java and Java3D. The kinematics and the beam characteristics were modeled and linked to a treatment planning module. Simulations of different robot workspace parameters for two example radiosurgical patient cases were performed using the novel software tool. The first case was an intracranial lesion near the left inner ear, the second case was a spinal lesion.

Results

The planning parameters for both cases were visualized with the novel simulation environment. An incremental extension of the robot workspace had limited effect for the intracranial case, where the original workspace already covered the left side of the patient. For the spinal case, a larger workspace resulted in a noticeable improvement in plan quality and a large portion of the beams being delivered from the extended workspace.

Conclusion

The new software environment is useful to simulate and analyze parameters and configurations for robotic radiosurgery. An enlarged robot workspace may result in improved plan quality depending on the location of the target region.

Keywords

Robotic radiosurgery Treatment planning Beam selection Simulation Virtual reality Training Visualization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adler JR, Murphy MJ, Chang SD, Hancock SL (1999) Image-guided robotic radiosurgery. Neurosurgery 44(6): 1299–1306PubMedCrossRefGoogle Scholar
  2. 2.
    Hamza-Lup FG, Davis L, Zeidan OA (2006) Web-based 3d planning tool for radiation therapy treatment. In: Web3D’06: Proceedings of the eleventh international conference on 3D web technology, pp 159–162 ACM, New YorkGoogle Scholar
  3. 3.
    Romanelli P, Schweikard A, Schlaefer A, Adler JR (2006) Computer aided robotic radiosurgery. Comput Aided Surg 11(4): 161–174PubMedCrossRefGoogle Scholar
  4. 4.
    Schlaefer A, Blanck O, Schweikard A (2007) Interactive multi-criteria inverse planning for robotic radiosurgery. In: Proceedings of the XVth international conference on the use of computers in radiation therapy (ICCR)Google Scholar
  5. 5.
    Schlaefer A, Blanck O, Shiomi H, Schweikard A (2006) Radiosurgery: identification of efficient treatment beams guided by autostereoscopic visualization. GMS CURAC 2006 1:Doc14Google Scholar
  6. 6.
    Schweikard A, Bodduluri M, Adler JR (1998) Planning for camera-guided robotic radiosurgery. IEEE Trans Robot Autom 14(6): 951–962CrossRefGoogle Scholar
  7. 7.
    Schweikard A, Schlaefer A, Adler JR (2006) Resampling: an optimization method for inverse planning in robotic radiosurgery. Med. Phys 33(11): 4005–4011PubMedCrossRefGoogle Scholar
  8. 8.
    Ward JW, Phillips R, Williams T, Shang C, Page L, Prest C, Beavis AW (2007) Immersive visualization with automated collision detection for radiotherapy treatment planning. Stud Health Technol Inform 125: 491–496PubMedGoogle Scholar

Copyright information

© CARS 2008

Authors and Affiliations

  • Alexander Schlaefer
    • 1
    • 2
  • Jakub Gill
    • 1
  • Achim Schweikard
    • 1
  1. 1.Institute for Robotics and Cognitive SystemsUniversity of LuebeckLuebeckGermany
  2. 2.Department of Radiation OncologyStanford UniversityStanfordUSA

Personalised recommendations