Marleen Balvert

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

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.

Dwell time modulation restrictions do not necessarily improve treatment plan quality for prostate HDR brachytherapy

Inverse planning algorithms for dwell time optimisation in interstitial high-dose-rate (HDR) brachytherapy may produce solutions with large dwell time variations within catheters, which may result in undesirable selective high-dose subvolumes. Extending the dwell time optimisation model with a dwell time modulation restriction (DTMR) that limits dwell time differences between neighboring dwell positions has been suggested to eliminate this problem. DTMRs may additionally reduce the sensitivity for uncertainties in dwell positions that inevitably result from catheter reconstruction errors and afterloader source positioning inaccuracies. This study quantifies the reduction of high-dose subvolumes and the robustness against these uncertainties by applying a DTMR to template-based prostate HDR brachytherapy implants. Three different DTMRs were consecutively applied to a linear dose-based penalty model (LD) and a dose-volume based model (LDV), both obtained from literature. The models were solved with DTMR levels ranging from no restriction to uniform dwell times within catheters in discrete steps. Uncertainties were simulated on clinical cases using in-house developed software, and dose-volume metrics were calculated in each simulation. For the assessment of high-dose subvolumes, the dose homogeneity index (DHI) and the contiguous dose volume histogram were analysed. Robustness was measured by the improvement of the lowest D90% of the planning target volume (PTV) observed in the simulations. For (LD), a DTMR yields an increase in DHI of approximately 30% and reduces the size of the largest high-dose volume by 2-5 cc. However, this comes at a cost of a reduction in D90% of the PTV of 10%, which often implies that it drops below the desired minimum of 100%. For (LDV), none of the DTMRs were able to improve high-dose volume measures. DTMRs were not capable of improving robustness of PTV D90% against uncertainty in dwell positions for both models.Comparison of optimization algorithms for inverse treatment planning requires objective function value evaluation.

Fast approximate delivery of fluence maps for IMRT and VMAT

In this article we provide a method to generate the trade-off between delivery time and fluence map matching quality for dynamically delivered fluence maps. At the heart of our method lies a mathematical programming model that, for a given duration of delivery, optimizes leaf trajectories and dose rates such that the desired fluence map is reproduced as well as possible. We begin with the single fluence map case and then generalize the model and the solution technique to the delivery of sequential fluence maps. The resulting large-scale, non-convex optimization problem was solved using a heuristic approach. We test our method using a prostate case and a head and neck case, and present the resulting trade-off curves. Analysis of the leaf trajectories reveals that short time plans have larger leaf openings in general than longer delivery time plans. Our method allows one to explore the continuum of possibilities between coarse, large segment plans characteristic of direct aperture approaches and narrow field plans produced by sliding window approaches. Exposing this trade-off will allow for an informed choice between plan quality and solution time. Further research is required to speed up the optimization process to make this method clinically implementable.

WE-AB-209-10: Optimizing the Delivery of Sequential Fluence Maps for Efficient VMAT Delivery

PURPOSE To develop an optimization model and solution approach for computing MLC leaf trajectories and dose rates for high quality matching of a set of optimized fluence maps to be delivered sequentially around a patient in a VMAT treatment. METHODS We formulate the fluence map matching problem as a nonlinear optimization problem where time is discretized but dose rates and leaf positions are continuous variables. For a given allotted time, which is allocated across the fluence maps based on the complexity of each fluence map, the optimization problem searches for the best leaf trajectories and dose rates such that the original fluence maps are closely recreated. Constraints include maximum leaf speed, maximum dose rate, and leaf collision avoidance, as well as the constraint that the ending leaf positions for one map are the starting leaf positions for the next map. The resulting model is non-convex but smooth, and therefore we solve it by local searches from a variety of starting positions. We improve solution time by a custom decomposition approach which allows us to decouple the rows of the fluence maps and solve each leaf pair individually. This decomposition also makes the problem easily parallelized. RESULTS We demonstrate method on a prostate case and a head-and-neck case and show that one can recreate fluence maps to high degree of fidelity in modest total delivery time (minutes). CONCLUSION We present a VMAT sequencing method that reproduces optimal fluence maps by searching over a vast number of possible leaf trajectories. By varying the total allotted time given, this approach is the first of its kind to allow users to produce VMAT solutions that span the range of wide-field coarse VMAT deliveries to narrow-field high-MU sliding window-like approaches.

PO-0785 : Inverse planning of beam-on times for precision image-guided 3D small animal radiotherapy treatments

PO-0785 Inverse planning of beam-on times for precision imageguided 3D small animal radiotherapy treatments M. Balvert, S.J. Van Hoof, P.V. Granton, D. Trani, D. Den Hertog, A.L. Hoffmann, F. Verhaegen Center for Economic Research (CentER) Tilburg University, Econometric and Operations Research, Tilburg, The Netherlands Maastricht Radiation Oncology (MAASTRO Clinic), Physics Research, Maastricht, The Netherlands Purpose/Objective: Advances in small animal radiotherapy enable the delivery of increasingly complex heterogeneous dose distributions on the millimeter scale, but methods to plan complicated small animal treatments remain in their infancy. A pre-clinical irradiation plan is usually created based on cone beam CT data with the animal in treatment position under anesthesia. Combined with demands on throughput, fast and easy treatment planning methods and algorithms are required. The purpose of this study is to develop an optimization model that determines beam-on times for a given beam configuration, and to assess the benefits of automated treatment planning for small animal radiotherapy. Materials and Methods: The applied model determines a Pareto-optimal solution based on user-provided weights for objectives. An interactive approach allows the user to select the plan that yields the most preferred trade-offs. Two cases based on cone beam CT data of a rat were used, and manual and model-based optimization results were compared using dose-volume metrics. The kidneys, spine and gastrointestinal tract (GI) were delineated as organs at risk (OARs) and a fictitious planning target volume (PTV) was created around the spine. In case 1, the left kidney was targeted as PTV with four 10x10 mm beams and for case 2, twelve 8x10 mm beams were used to target the PTV around the spine. A PTV dose of 8 Gy was prescribed, with a mean dose between 8 and 10 Gy as constraint. Differences between prescribed and planned PTV dose, as well as OAR doses were included in penalty objectives. The model was integrated in a research version of Monte Carlo based small animal treatment planning system SmART-Plan (v2.0 Precision X-ray). Results: Results show that manual and automated treatment planning yields plans of similar quality as shown in the figure and table. A similar amount of time was needed for manual and model-based optimization. In this period, manual optimization generates a single plan, while a set of Paretooptimal plans is created with automated optimization, allowing for a more substantiated choice on trade-offs. Automated optimization often uses fewer beams than manual optimized plans, therewith lowering treatment delivery time. Additional benefits of automated planning include a decreased dependence on the planning skills of the user (often absent in pre-clinical research), and the potential to improve treatment standardization among institutions. For more complex irradiations, manual planning becomes infeasible, making automation a necessity.