This study demonstrates that the algorithm can be effectively applied to IMRT scenarios to get fast and case specific beam angle configurations.

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Purpose: A new algorithm for automated determination of Objective Function Parameters (OFPs) in inverse planning is proposed. Method and Materials: While in theory, optimization in inverse planning is a one‐step automatic process, in practice, planner intervention is often required to carry out a multiple trial‐and‐error process where several parameters are sequentially varied until an acceptable compromise is achieved. We propose an algorithm for automated determination of IMRT Objective Function Parameters (OFPs). The algorithm is based on a new approach called “Adapted Dose Prescription (ADP)” wherein the dose prescriptions are automatically tailored to the sensitivity of target and OARs, which immediately results in a treatment plan meeting the clinical goals. The sensitivity of a structure is estimated by calculating the difference between the expected and obtained dose values after the end of an optimization trial. We incorporated the proposed algorithm with Fast Simulated Annealing (FSA) scheme using MATLAB software package to generate Aperture‐based IMRT plans for various complex patient cases. The beam placements, aperture shaping and dose calculations were done using CMS XiO planning system in our clinic. Results: So far, three patients planned using the proposed algorithm has been treated in our clinic. Our observation is that the algorithm automatically fetches a set of OFPs that immediately results in a clinically acceptable dose distribution. This approach significantly reduces the time taken for optimization by reducing the no. of optimization trials, while providing dose distribution that is comparable to that of plans obtained using KonRad inverse planning system. Conclusion: The proposed algorithm facilitates the production of inverse solutions which, without the planner's intervention, precisely satisfy the specified constraints. Moreover, the results demonstrate that the proposed algorithm can be effectively used for clinical applications.

This study aims to evaluate the performance of a new algorithm for optimization of beam weights in anatomy-based intensity modulated radiotherapy (IMRT). The algorithm uses a numerical technique called Gaussian-Elimination that derives the optimum beam weights in an exact or non-iterative way. The distinct feature of the algorithm is that it takes only fraction of a second to optimize the beam weights, irrespective of the complexity of the given case. The algorithm has been implemented using MATLAB with a Graphical User Interface (GUI) option for convenient specification of dose constraints and penalties to different structures. We have tested the numerical and clinical capabilities of the proposed algorithm in several patient cases in comparison with KonRad® inverse planning system. The comparative analysis shows that the algorithm can generate anatomy-based IMRT plans with about 50% reduction in number of MUs and 60% reduction in number of apertures, while producing dose distribution comparable to that of beamlet-based IMRT plans. Hence, it is clearly evident from the study that the proposed algorithm can be effectively used for clinical applications.

The study aims to introduce a hybrid optimization algorithm for anatomy-based intensity modulated radiotherapy (AB-IMRT). Our proposal is that by integrating an exact optimization algorithm with a heuristic optimization algorithm, the advantages of both the algorithms can be combined, which will lead to an efficient global optimizer solving the problem at a very fast rate. Our hybrid approach combines Gaussian elimination algorithm (exact optimizer) with fast simulated annealing algorithm (a heuristic global optimizer) for the optimization of beam weights in AB-IMRT. The algorithm has been implemented using MATLAB software. The optimization efficiency of the hybrid algorithm is clarified by (i) analysis of the numerical characteristics of the algorithm and (ii) analysis of the clinical capabilities of the algorithm. The numerical and clinical characteristics of the hybrid algorithm are compared with Gaussian elimination method (GEM) and fast simulated annealing (FSA). The numerical characteristics include convergence, consistency, number of iterations and overall optimization speed, which were analyzed for the respective cases of 8 patients. The clinical capabilities of the hybrid algorithm are demonstrated in cases of (a) prostate and (b) brain. The analyses reveal that (i) the convergence speed of the hybrid algorithm is approximately three times higher than that of FSA algorithm; (ii) the convergence (percentage reduction in the cost function) in hybrid algorithm is about 20% improved as compared to that in GEM algorithm; (iii) the hybrid algorithm is capable of producing relatively better treatment plans in terms of Conformity Index (CI) [~ 2% - 5% improvement] and Homogeneity Index (HI) [~ 4% - 10% improvement] as compared to GEM and FSA algorithms; (iv) the sparing of organs at risk in hybrid algorithm-based plans is better than that in GEM-based plans and comparable to that in FSA-based plans; and (v) the beam weights resulting from the hybrid algorithm are about 20% smoother than those obtained in GEM and FSA algorithms. In summary, the study demonstrates that hybrid algorithms can be effectively used for fast optimization of beam weights in AB-IMRT.