Feasibility of Case-Based Beam Generation for Robotic Radiosurgery

Authors:
Alexander Schlaefer;Sonja Dieterich
Affiliations:
Medical Robotics, University of Lübeck, Lübeck, Germany and Department of Radiation Oncology, Stanford University, Stanford, USA;Department of Radiation Oncology, Stanford University, Stanford, USA
Venue:
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Year:
2009

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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.