Feasibility of case-based beam generation for robotic radiosurgery

Alexander Schlaefer*, Sonja Dieterich

*Corresponding author for this work
8 Citations (Scopus)


Objective: 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. We study an approach to make the search for treatment beams more efficient by considering previous treatment plans. Methods and material: Conventionally, a beam generation heuristic based on randomly selected candidate beams has been proven to be most robust in clinical practice. However, for prevalent types of cancer similarities in patient anatomy and dose prescription exist. We present a case-based approach that introduces a problem specific measure of similarity and allows to generate candidate beams from a database of previous treatment plans. Similarity between treatments is established based on projections of the organs and structures considered during planning, and the desired dose distribution. Solving the inverse planning problem a subset of treatment beams is determined and adapted to the new clinical case. Results: Preliminary experimental results indicate that the new approach leads to comparable plan quality for substantially fewer candidate beams. For two prostate cases, the dose homogeneity in the target region and the sparing of critical structures is similar for plans based on 400 and 600 candidate beams generated with the novel and the conventional method, respectively. However, the runtime for solving the inverse planning problem for could be reduced by up to 47%, i.e., from approximately 19. min to less than 11. min. Conclusion: We have shown the feasibility of case-based beam generation for robotic radiosurgery. For prevalent clinical cases with similar anatomy the cased-based approach could substantially reduce planning time while maintaining high plan quality.

Original languageEnglish
JournalArtificial Intelligence in Medicine
Issue number2
Pages (from-to)67-75
Number of pages9
Publication statusPublished - 01.06.2011


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