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We present an open-source platform to aid medical dosimetrists in preventing collisions between gantry head and patient or couch during photon or particle beam therapy treatment planning. This generic framework uses the native scripting interface of the particular planning software to import STL files of the treatment machine elements. These are visualized in 3D together with the contoured or scanned patient surface. A graphical dialog with sliders allows the interactive rotation of the gantry and couch, with real-time feedback. To prevent a future replanning, treatment planners can assess in advance and exclude beam angles resulting in a potential risk of collision. The software platform is publicly available on GitHub and has been validated for RayStation with actual patient plans. Furthermore, the incorporation of the complete patient geometry was tested with a 3D surface scan of a full-body phantom performed with a handheld smartphone. With this study, we aim at minimizing the risk of replanning due to collisions and thus of treatment delays and unscheduled consumption of manpower. The clinical workflow can be streamlined at no cost already at the treatment planning stage. By ensuring a real-time verification of the plan feasibility, the script might boost the use of optimal couch angles that a planner might shy away from otherwise.

We introduce a new method called NoVo (Noncoplanar VMAT Optimization) to produce volumetric modulated arc therapy (VMAT) treatment plans with noncoplanar trajectories. While the use of noncoplanar beam arrangements for intensity modulated radiation therapy (IMRT), and in particular high fraction stereotactic radiosurgery (SRS), is common, noncoplanar beam trajectories for VMAT are less common as the availability of treatment machines handling these is limited. For both IMRT and VMAT, the beam angle selection problem is highly nonconvex in nature, which is why automated beam angle selection procedures have not entered mainstream clinical usage. NoVo determines a noncoplanar VMAT solution (i.e. the simultaneous trajectories of the gantry and the couch) by first computing a [Formula: see text] solution (beams from every possible direction, suitably discretized) and then eliminating beams by examing fluence contributions. Also all beam angles are scored via geometrical considerations only to find out the usefulness of the whole beam space in a very short time. A custom path finding algorithm is applied to find an optimized, continuous trajectory through the most promising beam angles using the calculated score of the beam space. Finally, using this trajectory a VMAT plan is optimized. For three clinical cases, a lung, brain, and liver case, we compare NoVo to the ideal [Formula: see text] solution, nine beam noncoplanar IMRT, coplanar VMAT, and a recently published noncoplanar VMAT algorithm. NoVo comes closest to the [Formula: see text] solution considering the lung case (brain and liver case: second), as well as improving the solution time by using geometrical considerations, followed by a time effective iterative process reducing the [Formula: see text] solution. Compared to a recently published noncoplanar VMAT algorithm, using NoVo the computation time is reduced by a factor of 2-3 (depending on the case). Compared to coplanar VMAT, NoVo reduces the objective function value by 24%, 49% and 6% for the lung, brain and liver cases, respectively.

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.

Proton pencil beam scanning (PBS) treatment plans are made of numerous unique spots of different weights. These weights are optimized by the treatment planning systems, and sometimes fall below the deliverable threshold set by the treatment delivery system. The purpose of this work is to investigate a Greedy reassignment algorithm to mitigate the effects of these low weight pencil beams. The algorithm is applied during post-processing to the optimized plan to generate deliverable plans for the treatment delivery system. The Greedy reassignment method developed in this work deletes the smallest weight spot in the entire field and reassigns its weight to its nearest neighbor(s) and repeats until all spots are above the minimum monitor unit (MU) constraint. Its performance was evaluated using plans collected from 190 patients (496 fields) treated at our facility. The Greedy reassignment method was compared against two other post-processing methods. The evaluation criteria was the γ-index pass rate that compares the pre-processed and post-processed dose distributions. A planning metric was developed to predict the impact of post-processing on treatment plans for various treatment planning, machine, and dose tolerance parameters. For fields with a pass rate of 90  ±  1% the planning metric has a standard deviation equal to 18% of the centroid value showing that the planning metric and γ-index pass rate are correlated for the Greedy reassignment algorithm. Using a 3rd order polynomial fit to the data, the Greedy reassignment method has 1.8 times better planning metric at 90% pass rate compared to other post-processing methods. As the planning metric and pass rate are correlated, the planning metric could provide an aid for implementing parameters during treatment planning, or even during facility design, in order to yield acceptable pass rates. More facilities are starting to implement PBS and some have spot sizes (one standard deviation) smaller than 5 mm, hence would require small spot spacing. While this is not the only parameter that affects the optimized plan, the perturbation due to the minimum MU constraint increases with decreasing spot spacing. This work could help to design the minimum MU threshold with the goal to keep the γ-index pass rate above an acceptable value.

Purpose: To make the planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between planning objectives and delivery efficiency., Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the planner to navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus organ at risk sparing. The selected plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution quality is maintained. The complete algorithm is called VMERGE., Results: VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal plan is matched almost exactly with the VMAT merging routine, resulting in a high quality plan delivered with a single arc in less than 5 min on average., Conclusions: VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria planning aspect, which greatly speeds up planning time and allows the user to select the plan, which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT plan. Finally, the user can explore the tradeoff between delivery time and plan quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial planning systems.

Purpose: To describe a fast projection algorithm for optimizing intensity modulated proton therapy (IMPT) plans and to describe and demonstrate the use of this algorithm in multicriteria IMPT planning., Methods: The authors develop a projection-based solver for a class of convex optimization problems and apply it to IMPT treatment planning. The speed of the solver permits its use in multicriteria optimization, where several optimizations are performed which span the space of possible treatment plans. The authors describe a plan database generation procedure which is customized to the requirements of the solver. The optimality precision of the solver can be specified by the user., Results: The authors apply the algorithm to three clinical cases: A pancreas case, an esophagus case, and a tumor along the rib cage case. Detailed analysis of the pancreas case shows that the algorithm is orders of magnitude faster than industry-standard general purpose algorithms (MOSEK'S interior point optimizer, primal simplex optimizer, and dual simplex optimizer). Additionally, the projection solver has almost no memory overhead., Conclusions: The speed and guaranteed accuracy of the algorithm make it suitable for use in multicriteria treatment planning, which requires the computation of several diverse treatment plans. Additionally, given the low memory overhead of the algorithm, the method can be extended to include multiple geometric instances and proton range possibilities, for robust optimization.

Purpose: To introduce a method to simultaneously explore a collection of Pareto surfaces. The method will allow radiotherapy treatment planners to interactively explore treatment plans for different beam angle configurations as well as different treatment modalities., Methods: The authors assume a convex optimization setting and represent the Pareto surface for each modality or given beam set by a set of discrete points on the surface. Weighted averages of these discrete points produce a continuous representation of each Pareto surface. The authors calculate a set of Pareto surfaces and use linear programming to navigate across the individual surfaces, allowing switches between surfaces. The switches are organized such that the plan profits in the requested way, while trying to keep the change in dose as small as possible., Results: The system is demonstrated on a phantom pancreas IMRT case using 100 different five beam configurations and a multicriteria formulation with six objectives. The system has intuitive behavior and is easy to control. Also, because the underlying linear programs are small, the system is fast enough to offer real-time exploration for the Pareto surfaces of the given beam configurations., Conclusions: The system presented offers a sound starting point for building clinical systems for multicriteria exploration of different modalities and offers a controllable way to explore hundreds of beam angle configurations in IMRT planning, allowing the users to focus their attention on the dose distribution and treatment planning objectives instead of spending excessive time on the technicalities of delivery.

Purpose: To propose a new measure of target underdose that can be used in the evaluation and optimization of radiotherapy dose distributions., Methods and Materials: We compare various formulations of the equivalent uniform dose (EUD) and introduce a modification of existing EUD definitions, which we call tail EUD. Tail EUD is a measure of "cold spots" below the prescription dose in the target dose distribution, using units of gray (Gy). We investigate the mathematical properties of various target EUD concepts, including tail EUD. We apply the tail EUD measure retrospectively to intensity modulated radiation therapy (IMRT) treatment plans from our plan database. We also use tail EUD as an optimization objective in the optimization of prostate, pancreas, and head-and-neck plans., Results: Tail EUD has desirable mathematical properties. In particular, it is convex and it leads to convex level sets (i.e., no local minima) if the EUD from which it is derived is concave. The tail EUD value is correlated with the subjective degree of target coverage. Constraining tail EUDs to a certain level in plan optimization leads to comparable target coverage in different plans and treatment sites., Conclusions: The newly introduced concept of tail EUD appears to be useful for both plan evaluation and optimization. In addition it can potentially be applied in the design of new clinical protocols.

Purpose: To provide a mathematical approach for quantifying the tradeoff between intensity-modulated radiotherapy (IMRT) complexity and plan quality., Methods and Materials: We solve a multi-objective program that includes IMRT complexity, measured as the number of monitor units (MU) needed to deliver the plan using a multileaf collimator, as an objective. Clinical feasibility of plans is ensured by the use of hard constraints in the formulation. Optimization output is a Pareto surface of treatment plans, which allows the tradeoffs between IMRT complexity, tumor coverage, and tissue sparing to be observed. Paraspinal and lung cases are presented., Results: Although the amount of possible MU reduction is highly dependent on the difficulty of the underlying treatment plan (difficult plans requiring a high degree of intensity modulation are more sensitive to MU reduction), in some cases the number of MU can be reduced more than twofold with a <1% increase in the objective function., Conclusions: The largely increased number of MU and irradiation time in IMRT is sometimes unnecessary. Tools like the one presented should be considered for integration into daily clinical practice to avoid this problem.