A framework for inverse planning of beam-on times for 3D small animal radiotherapy using interactive multi-objective optimisation. (6th July 2015)

• Record Type: Journal Article
• Authors: Balvert, Marleen; van Hoof, Stefan J; Granton, Patrick V; Trani, Daniela; den Hertog, Dick; Hoffmann, Aswin L; Verhaegen, Frank
• Is Part Of: Physics in medicine & biology. Volume 60:Number 14(2015:Jul.)
• Page Start: 5681
• Page End: 5698
• Publication Date: 2015-07-06
• Subjects: small animal radiotherapy -- inverse planning -- optimisation; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/0031-9155/60/14/5681
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Advances in precision small animal radiotherapy hardware enable the delivery of increasingly complicated dose distributions on the millimeter scale. Manual creation and evaluation of treatment plans becomes difficult or even infeasible with an increasing number of degrees of freedom for dose delivery and available image data. The goal of this work is to develop an optimisation model that determines beam-on times for a given beam configuration, and to assess the feasibility and benefits of an automated treatment planning system for small animal radiotherapy. The developed model determines a Pareto optimal solution using operator-defined weights for a multiple-objective treatment planning problem. An interactive approach allows the planner to navigate towards, and to select the Pareto optimal treatment plan that yields the most preferred trade-off of the conflicting objectives. This model was evaluated using four small animal cases based on cone-beam computed tomography images. Resulting treatment plan quality was compared to the quality of manually optimised treatment plans using dose-volume histograms and metrics. Results show that the developed framework is well capable of optimising beam-on times for 3D dose distributions and offers several advantages over manual treatment plan optimisation. For all cases but the simple flank tumour case, a similar amount of time was needed for manual and automated beam-on time optimisation. In this time frame, manual optimisation generates a single treatment plan, while the inverse planning system yields a set of Pareto optimal solutions which provides quantitative insight on the sensitivity of conflicting objectives. Treatment planning automation decreases the dependence on operator experience and allows for the use of class solutions for similar treatment scenarios. This can shorten the time required for treatment planning and therefore increase animal throughput. In addition, this can improve treatment standardisation and comparability of research data within studies and among different institutes.