Abstract
Robotic radiosurgery uses the kinematic flexibility of a robotic arm to target tumors and lesions from many different directions. This approach allows to focus the dose to the target region while sparing healthy surrounding tissue. However, the flexibility in the placement of treatment beams is also a challenge during treatment planning. So far, a randomized beam generation heuristic has been proven to be most robust in clinical practice. Yet, for prevalent types of cancer similarities in patient anatomy and dose prescription exist. We propose a case-based method to solve the planning problem for a new patient by adapting beam sets from successful previous treatments. Preliminary experimental results indicate that the novel method could lead to faster treatment planning.
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References
Schweikard, A., Bodduluri, M., Adler, J.R.: Planning for camera-guided robotic radiosurgery. IEEE transactions on robotics and automation 14(6), 951–962 (1998)
Stein, J., Mohan, R., Wang, X.-H., Bortfeld, T., Wu, Q., Preiser, K., Ling, C.C., Schlegel, W.: Number and orientations of beams in intensity-modulated radiation treatments. Med. Phys. 24(2), 149–160 (1997)
Schlaefer, A., Schweikard, A.: Stepwise multi-criteria optimization for robotic radiosurgery. Medical Physics 35(5), 2094–2103 (2008)
Schlaefer, A., Blanck, O., Schweikard, A.: Interactive multi-criteria inverse planning for robotic radiosurgery. In: Proceedings of the XVth International Conference on the Use of Computers in Radiation Therapy (ICCR) (2007)
Craft, D., Halabi, T., Bortfeld, T.: Exploration of tradeoffs in intensity-modulated radiotherapy. Phys. Med. Biol. 50, 5857–5868 (2005)
Rosen, I., Liu, H.H., Childress, N., Liao, Z.: Interactively exploring optimized treatment plans. Int. J. Radiation Oncology Biol. Phys. 61(2) (2005)
Wang, X., Zhang, X., Dong, L., Liu, H., Wu, Q., Mohan, R.: Development of methods for beam angle optimization for IMRT using an accelerated exhaustive search strategy. Int. J. Radiation Oncology Biol. Phys. 60(4), 1325–1337 (2004)
Li, Y., Yao, D., Yao, J., Chen, W.: A particle swarm optimization algorithm for beam angle selection in intensity-modulated radiotherapy planning. Phys. Med. Biol. 50, 3491–3514 (2005)
Schreibmann, E., Xing, L.: Feasibility study of beam orientation class-solutions for prostate IMRT. Med. Phys. 31(10), 2863–2870 (2004)
Mott, J.H., Livsey, J.E., Logue, J.P.: Development of a simultaneous boost IMRT class solution for a hypofractionated prostate cancer protocol. Br. J. Radiol. 77, 377–386 (2004)
Arránsa, R., Gallardob, M.I., Rosellóc, J., Sánchez-Doblado, F.: Computer optimization of class solutions designed on a beam segmentation basis. Radiother Oncol. 69(3), 315–321 (2003)
Khoo, V.S., Bedford, J.L., Webb, S., Dearnaley, D.P.: Class solutions for conformal external beam prostate radiotherapy. Int. J. Radiation Oncology Biol. Phys. 55(4), 1109–1120 (2003)
Wells, D.M., Niederer, J.: A medical expert system approach using artificial neural networks for standardized treatment planning. Int. J. Radiation Oncology Biol. Phys. 41(1), 173–182 (1998)
Schweikard, A., Schlaefer, A., Adler, J.R.: Resampling: An optimization method for inverse planning in robotic radiosurgery. Med. Phys. 33(11), 4005–4011 (2006)
Burkhard, H.D.: Similarity and distance in case based reasoning. Fundam. Inf. 47(3-4), 201–215 (2001)
Goitein, M., Abrams, M., Rowell, D., Pollari, H., Wiles, J.: Multi-dimensional treatment planning: II. beam’s eye-view, back projection, and projection through CT sections. Int. J. Radiation Oncology Biol. Phys. 9(6), 789–797 (1983)
Kalet, I.J., Austin-Seymour, M.M.: The use of medical images in planning and delivery of radiation therapy. J. Am. Med. Inform. Assoc. 4(5), 327–339 (1997)
Holt, A., Bichindaritz, I., Schmidt, R., Perner, P.: Medical applications in case-based reasoning. Knowl. Eng. Rev. 20(3), 289–292 (2005)
Pantazi, S.V., Arocha, J.F., Moehr, J.R.: Case-based medical informatics. BMC Med. Inform. Decis. Mak. 4, 19 (2004)
Fritsche, L., Schlaefer, A., Budde, K., Schroeter, K., Neumayer, H.H.: Recognition of critical situations from time series of laboratory results by case-based reasoning. J. Am. Med. Inform. Assoc. 9(5), 520–528 (2002)
Song, X., Petrovic, S., Sundar, S.: A case-based reasoning approach to dose planning in radiotherapy. In: Wilson, D., Khemani, D. (eds.) Workshop Proceedings, The Seventh International Conference on Case-Based Reasoning (ICCBR 2007), pp. 348–357 (2007)
Berger, J.: Roentgen: radiation therapy and case-based reasoning. In: Proceedings of the Tenth Conference on Artificial Intelligence for Applications, pp. 171–177 (1994)
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Schlaefer, A., Dieterich, S. (2009). Feasibility of Case-Based Beam Generation for Robotic Radiosurgery. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds) Artificial Intelligence in Medicine. AIME 2009. Lecture Notes in Computer Science(), vol 5651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02976-9_15
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DOI: https://doi.org/10.1007/978-3-642-02976-9_15
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