A framework for inverse planning of beam-on times for 3D small animal radiotherapy using interactive multi-objective optimisation

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.

A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications

“…Both should be available but conversion from one to the other introduces uncertainties [27]. As small animal planning will become more complicated, beam optimization will be needed [37,38]. Finally, intrairradiation motion is a largely understudied aspect in preclinical radiotherapy which may be addressed by time-resolved imaging, gated beam delivery, and/or target tracking [39,91].…”

A kernel-based dose calculation algorithm for kV photon beams with explicit handling of energy and material dependencies

“… 14 – 16 The available planning tools for pre-clinical radiotherapy currently support forward-planned dose delivery techniques with open treatment fields from multiple beam directions or arcs, with the recent addition of beam-on time optimization. 17 …”

Radiation planning for image guided preclinical radiotherapy

“…The commercially available platforms have gone through ongoing improvements over the years. For example, the SARRP and X-RAD 225Cx were extended with integrated bioluminescence optical imaging [45,46], and more advanced collimation and animal positioning systems have been developed. The work of this thesis uses the X-RAD 225Cx micro-IGRT platform which is installed at the University of Maastricht.…”

“…GPU computers are now also available to run simulations very efficiently [44] which will probably allow Monte Carlo simulations to play an important role in small animal treatment planning. A restriction could be in inverse planning [45] where a great number of dose calculations for individual beamlets need to be performed. It is imaginable that the beamlet dose calculations could be performed by a faster, less accurate method (e.g.…”

“…To advance treatment planning for small animals many techniques can be borrowed from the mature field of treatment planning for human radiotherapy. An example is inverse treatment optimisation [45], whereby certain dose objectives and constraints are defined and then the most optimal plan is designed. Planning and radiation dose delivery to small animal models of unprecedented accuracy will be available in the near future, at a comparable level to human radiotherapy, enabling a wealth of studies.…”