Skip to main content

Treatment Planning

  • Chapter
Medical Robotics
  • 2103 Accesses

Abstract

Most surgical interventions rely on a planning step. For example, in orthopedic surgery, angles or cutting planes must be defined before the intervention. This step requires image data. Given the image data, we could plan the intervention with paper and pencil. However, it seems reasonable to combine planning with navigation, since we must retrieve the geometric results of the planning steps during the intervention.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 64.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Y. Censor, M. D. Altschuler, and W. D. Powlis. On the use of Cimmino’s simultaneous projections method for computing a solution of the inverse problem in radiation therapy treatment planning. Inverse Problems, 4(3):607, 1988. DOI 10.1088/0266-5611/4/3/006.

  2. J. P. Dmochowski, A. Datta, M. Bikson, Y. Su, and L. C. Parra. Optimized multi-electrode stimulation increases focality and intensity at target. Journal of Neural Engineering, 8(4):046011, 2011. DOI 10.1088/1741-2560/8/4/046011.

  3. G. Fichtinger, J. P. Fiene, C. W. Kennedy, G. Kronreif, I. Iordachita, D. Y. Song, E. C. Burdette, and P. Kazanzides. Robotic assistance for ultrasound-guided prostate brachytherapy. Medical Image Analysis, 12(5):535–545, 2008. DOI 10.1016/j.media.2008.06.002.

    Article  Google Scholar 

  4. H. Gottschling, M. Roth, A. Schweikard, and R. Burgkart. Intraoperative, fluoroscopy-based planning for complex osteotomies of the proximal femur. International Journal of Medical Robotics and Computer Assisted Surgery, 1(3): 67–73, 2005. DOI 10.1002/rcs.29.

    Article  Google Scholar 

  5. M. Hauskrecht and H. Fraser. Planning treatment of ischemic heart disease with partially observable Markov decision processes. Artificial Intelligence in Medicine, 18(3):221–244, 2000. DOI 10.1016/s0933-3657(99)00042-1.

    Article  Google Scholar 

  6. A. Jordan, R. Scholz, P. Wust, H. Fähling, and R. Felix. Magnetic fluid hyperthermia (MFH): Cancer treatment with AC magnetic field induced excitation of biocompatible superparamagnetic nanoparticles. Journal of Magnetism and Magnetic Materials, 201(1-3):413–419, 1999. DOI 10.1016/s0304-8853(99)00088-8.

    Article  Google Scholar 

  7. S. Nakajima, H. Atsumi, A. H. Bhalerao, F. A. Jolesz, R. Kikinis, T. Yoshimine, T. M. Moriarty, and P. E. Stieg. Computer-assisted surgical planning for cerebrovascular neurosurgery. Neurosurgery, 2 41: 403–410, 1997.

    Google Scholar 

  8. S. Rossi, M. Di Stasi, E. Buscarini, P. Quaretti, F. Garbagnati, L. Squassante, C. T. Paties, D. E. Silverman, and L. Buscarini. Percutaneous RF interstitial thermal ablation in the treatment of hepatic cancer. American Journal of Roentgenology, 167(3):759–768, 2014. DOI 10.2214/ajr.167.3.8751696.

    Article  Google Scholar 

  9. A. Schlaefer, J. Fisseler, S. Dieterich, H. Shiomi, K. Cleary, and A. Schweikard. Feasibility of four-dimensional conformal planning for robotic radiosurgery. Medical Physics, 32(12):3786–3792, 2005. DOI 10.1118/1.2122607.

    Article  Google Scholar 

  10. A. Schweikard, M. Bodduluri, R. Tombropoulos, and J. R. Adler, Jr. Planning, calibration and collision-avoidance for image-guided radiosurgery. In Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems (IROS’94), 1994, pages 854–861. DOI 10.1109/iros.1994.407492.

  11. A. Schweikard, R. Tombropoulos, L. Kavraki, J. R. Adler, Jr., and J.-C. Latombe. Treatment planning for a radiosurgical system with general kinematics. In IEEE International Conference on Robotics and Automation (ICRA 1994), 1994, pages 1720–1727. DOI 10.1109/robot.1994.351344.

  12. C. M. C. Tempany, E. A. Stewart, N. McDannold, B. J. Quade, F. A. Jolesz, and K. Hynynen. MR imaging-guided focused ultrasound surgery of uterine leiomyomas: A feasibility study. Radiology, 226(3):897–905, 2003. DOI 10.1148/radiol.2271020395.

    Article  Google Scholar 

  13. N. Wilson, K. Wang, R. W. Dutton, and C. Taylor. A software framework for creating patient specific geometric models from medical imaging data for simulation based medical planning of vascular surgery. In W. J. Niessen and M. A. Viergever, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2001, volume 2208 of Lecture Notes in Computer Science, pages 449–456. Springer Berlin Heidelberg, 2001. DOI 10.1007/3-540-45468-3_54.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Schweikard, A., Ernst, F. (2015). Treatment Planning. In: Medical Robotics. Springer, Cham. https://doi.org/10.1007/978-3-319-22891-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22891-4_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22890-7

  • Online ISBN: 978-3-319-22891-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics