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In inverse planning processes of radiotherapy, dose-volume histograms (DVH) have been commonly used as a measure of plan quality. The exact mathematical formulation of DVHs involves binary variables and is therefore intrinsically nonconvex. In view of the resulting computational intractability, existing treatment planning systems adopt convex surrogate models that involve many parameters. Manual parameter tweaking is then required to determine reasonable values for those parameters in order to generate plans that meet the target DVHs. However, conventional convex formulations for inverse planning entails non-trivial differences from an exact formulation and, therefore, provide only very limited control over the DVHs. In view of this limitation, this paper presents a new formulation that combines a novel folded concave penalty (FCP)-based constraint and a new kurtosis-based optimization criterion. The former presents a much sharper approximation to the exact DVH constraints than the existing counterparts, and the latter penalizes the occurrence of hot and cold spots in dose distributions. We investigated the effectiveness of the proposed planning formulations on three head-and-neck cases in comparison with conventional DVH-based formulations. Significant outperformance in terms of better distributed doses-at-volume was achieved through the proposed scheme. [ABSTRACT FROM AUTHOR]

An important yet challenging problem in LINAC-based rotational arc radiation therapy is the design of beam trajectory, which requires simultaneous consideration of delivery efficiency and final dose distribution. In this work, we propose a novel trajectory selection strategy by developing a Monte Carlo tree search (MCTS) algorithm during the beam trajectory selection process. To search through the vast number of possible trajectories, the MCTS algorithm was implemented. In this approach, a candidate trajectory is explored by starting from a leaf node and sequentially examining the next level of linked nodes with consideration of geometric and physical constraints. The maximum Upper Confidence Bounds for Trees, which is a function of average objective function value and the number of times the node under testing has been visited, was employed to intelligently select the trajectory. For each candidate trajectory, we run an inverse fluence map optimization with an infinity norm regularization. The ranking of the plan as measured by the corresponding objective function value was then fed back to update the statistics of the nodes on the trajectory. The method was evaluated with a chest wall and a brain case, and the results were compared with the coplanar and noncoplanar 4pi beam configurations. For both clinical cases, the MCTS method found effective and easy-to-deliver trajectories within an hour. As compared with the coplanar plans, it offers much better sparing of the OARs while maintaining the PTV coverage. The quality of the MCTS-generated plan is found to be comparable to the 4pi plans. Artificial intelligence based on MCTS is valuable to facilitate the design of beam trajectory and paves the way for future clinical use of non-coplanar treatment delivery. [ABSTRACT FROM AUTHOR]

-minimization-based sparse optimization was employed to solve the beam angle optimization (BAO) in intensity-modulated radiation therapy (IMRT) planning. The technique approximates the exact BAO formulation with efficiently computable convex surrogates, leading to plans that are inferior to those attainable with recently proposed gradient-based greedy schemes. In this paper, we alleviate/reduce the nontrivial inconsistencies between the -based formulations and the exact BAO model by proposing a new sparse optimization framework based on the most recent developments in group variable selection. We propose the incorporation of the group-folded concave penalty (gFCP) as a substitution to the -minimization framework. The new formulation is then solved by a variation of an existing gradient method. The performance of the proposed scheme is evaluated by both plan quality and the computational efficiency using three IMRT cases: a coplanar prostate case, a coplanar head-and-neck case, and a noncoplanar liver case. Involved in the evaluation are two alternative schemes: the -minimization approach and the gradient norm method (GNM). The gFCP-based scheme outperforms both counterpart approaches. In particular, gFCP generates better plans than those obtained using the -minimization for all three cases with a comparable computation time. As compared to the GNM, the gFCP improves both the plan quality and computational efficiency. The proposed gFCP-based scheme provides a promising framework for BAO and promises to improve both planning time and plan quality. [ABSTRACT FROM AUTHOR]

Traditional inverse planning relies on the use of weighting factors to balance the conflicting requirements of different structures. Manual trial-and-error determination of weighting factors has long been recognized as a time-consuming part of treatment planning. The purpose of this work is to develop an inverse planning framework that parameterizes the dosimetric tradeoff among the structures with physically meaningful quantities to simplify the search for clinically sensible plans. In this formalism, instead of using weighting factors, the permissible variation range of the prescription dose or dose volume histogram (DVH) of the involved structures are used to characterize the ‘importance’ of the structures. The inverse planning is then formulated into a convex feasibility problem, called the dosimetric variation-controlled model (DVCM), whose goal is to generate plans with dosimetric or DVH variations of the structures consistent with the pre-specified values. For simplicity, the dosimetric variation range for a structure is extracted from a library of previous cases which possess similar anatomy and prescription. A two-phase procedure (TPP) is designed to solve the model. The first phase identifies a physically feasible plan to satisfy the prescribed dosimetric variation, and the second phase automatically improves the plan in case there is room for further improvement. The proposed technique is applied to plan two prostate cases and two head-and-neck cases and the results are compared with those obtained using a conventional CVaR approach and with a moment-based optimization scheme. Our results show that the strategy is able to generate clinically sensible plans with little trial and error. In all cases, the TPP generates a very competitive plan as compared to those obtained using the alternative approaches. Particularly, in the planning of one of the head-and-neck cases, the TPP leads to a non-trivial improvement in the resultant dose distribution—the fractional volumes receiving a dose above 20 Gy for the spinal cord are reduced by more than 40% when compared to the alternative schemes, while maintaining the same PTV coverage. With physically more meaningful modeling of the inter-structural tradeoff, the reported technique enables us to substantially reduce the need for trial-and-error adjustment of the model parameters. The new formalism also opens new opportunities for incorporating prior knowledge to facilitate the treatment planning process. [ABSTRACT FROM AUTHOR]