Gafchromic EBT (EBT) films are becoming increasingly popular due to their advantageous properties. When flatbed colour scanners are used for film dosimetry, a good quality control of the scanning device is a crucial step for accurate results. The proposal of this work was to fully assess the performance of the scanner Epson Expression 10000XL in order to quantify all parameters and needed corrections to minimize dose uncertainties. A standard step tablet, with 32 steps and optical densities from 0.06 to 3.8, was used to check the scanner linearity. The scanner warming-up effect and reproducibility were evaluated by performing 30 consecutive scans plus 20 scans in 15 min intervals. The scanning colour modes: 24 and 48 bits and scanning resolutions from 50 to 300 dpi were tested. A Wiener filter with different pixels regions was applied with the purpose of reducing the film noise. All scans were made in transmission mode with a constant film orientation. The red colour channel was posteriorly extracted from the images to maximize readout sensitivity. Two EBT films were irradiated, perpendicularly and parallel to beam incidence, with a 6 MV photon beam with doses that ranged from 0.2 to 3 Gy. A polynomial expression was used to convert optical density into dose. Dose uncertainty was quantified applying error propagation analysis. A correction for the non-uniform response of the scanner was determined using five films irradiated with a uniform dose. The scanner response was linear until an optical density of approximately 1 which corresponds to doses higher than those of clinical interest for EBT films. The scanner signal stabilized after seven readings. Scanner reproducibility around 0.2% was obtained either with the scanner warm or cold. However, reproducibility was significantly reduced when comparing images digitized with the scanner at different temperatures. Neither the colour depth mode, the scanning resolution, the multiscan option nor the Wiener filter had a significant effect on the shape of the calibration curve. However, a reduction in dose uncertainty was possible by selecting appropriate reading parameters. These are a 48 bit colour depth, a 75 dpi resolution and repeating the scan four times. Finally, the two dimensional Wiener filter applied to a 3 x 3 pixel region to the red component of the image reduced the experimental scan uncertainty to about 0.5% for doses higher than 0.5 Gy. Total scan uncertainty was less than 2% for a perpendicular calibration and reduced to less than 1% for a parallel calibration. A dose over-estimation of around 5% for clinical doses may be made if the image acquired is not corrected for the non-uniform response of the scanner. A protocol to read EBT films using the Epson Expression 10000XL scanner was established for IMRT verification. The contribution for the overall uncertainty in film dosimetry coming from the scanning process was estimated to be around 0.5% for doses higher than 0.5 Gy when reading parameters are optimized. Total scan uncertainty achieved is about 2% ...

Generally, the inverse planning of radiation therapy consists mainly of the fluence optimization. The beam angle optimization (BAO) in intensity-modulated radiation therapy (IMRT) consists of selecting appropriate radiation incidence directions and may influence the quality of the IMRT plans, both to enhance better organ sparing and to improve tumor coverage. However, in clinical practice, most of the time, beam directions continue to be manually selected by the treatment planner without objective and rigorous criteria. The goal of this paper is to introduce a novel approach that uses beam's-eye-view dose ray tracing metrics within a pattern search method framework in the optimization of the highly non-convex BAO problem. Pattern search methods are derivative-free optimization methods that require a few function evaluations to progress and converge and have the ability to better avoid local entrapment. The pattern search method framework is composed of a search step and a poll step at each iteration. The poll step performs a local search in a mesh neighborhood and ensures the convergence to a local minimizer or stationary point. The search step provides the flexibility for a global search since it allows searches away from the neighborhood of the current iterate. Beam's-eye-view dose metrics assign a score to each radiation beam direction and can be used within the pattern search framework furnishing a priori knowledge of the problem so that directions with larger dosimetric scores are tested first. A set of clinical cases of head-and-neck tumors treated at the Portuguese Institute of Oncology of Coimbra is used to discuss the potential of this approach in the optimization of the BAO problem.

The selection of appropriate radiation incidence directions in radiation therapy treatment planning is important for the quality of the treatment plan, both for appropriate tumor coverage and for better organ sparing. The objective of this paper is to discuss the benefits of using radial basis functions within a pattern search methods framework in the optimization of the highly non-convex beam angle optimization (BAO) problem. Pattern search methods are derivative-free optimization methods that require few function value evaluations to converge and have the ability to avoid local entrapment. These two characteristics gathered together make pattern search methods suited to address the BAO problem. The pattern search methods framework is composed by a search step and a poll step at each iteration. The poll step performs a local search in a mesh neighborhood and assures convergence to a local minimizer or stationary point. The search step provides the flexibility for a global search since it allows searches away from the neighborhood of the current iterate. Radial basis functions are used and tested in this step both to influence

Intensity Modulated Radiotherapy Treatment (IMRT) is a technique used in the treatment of cancer, where the radiation beams are modulated by a multileaf collimator allowing the irradiation of the patient using non-uniform radiation fields from selected angles. Beam angle optimization consists in trying to find the best set of angles that should be used in IMRT planning. The choice of this set of angles is patient and pathology dependent and, in clinical practice, most of the times it is made using a trial and error procedure or simply using equidistantly distributed angles. In this paper we propose a genetic algorithm that aims at calculating good sets of angles in an automated way, given a predetermined number of angles. We consider the discretization of all possible angles in the interval [0 • , 360 • ], and each individual is represented by a chromosome with 360 binary genes. As the calculation of a given individual's fitness is very expensive in terms of computational time, the genetic algorithm uses a neural network as a surrogate model to calculate the fitness of most of the individuals in the population. To explicitly consider the estimation error that can result from the use of this surrogate model, the fitness of each individual is represented by an interval of values and not by a single crisp value. The genetic algorithm is capable of finding improved solutions, when compared to the usual equidistant solution applied in clinical practice. The genetic algorithm will be described and computational results will be shown.

SPIDERplan enables a fast and consistent evaluation of plan quality considering all targets and organs at risk.

The noncoplanar BAO problem is an extremely challenging multimodal optimization problem that can be successfully addressed through a thoughtful exploration of the continuous highly nonconvex BAO search space. The proposed framework is capable of calculating high quality treatment plans and thus can be an interesting alternative toward automated noncoplanar beam selection in IMRT treatment planning which is nowadays the natural trend in treatment planning.

The selection of appropriate beam directions is decisive for the quality of the treatment, both for maximizing tumor doses and for organs sparing. However, the beam angle optimization (BAO) problem is still an open problem and, most of the time, beam directions continue to be manually selected in clinical practice which requires many trial and error iterations between selecting beam angles and computing fluence patterns until a suitable treatment is achieved. The goal of BAO is to improve the quality of the directions used and, at the same time, release the treatment planner for other tasks. The objective of this paper is to introduce a new approach for the resolution of the BAO problem, using pattern search methods to tackle this highly non-convex optimization problem. Pattern search methods are derivative-free optimization methods with the ability to avoid local entrapment. Moreover, they require few function value evaluations to progress and converge. These two characteristics gathered together make pattern search methods suited to address the BAO problem. A set of clinical examples of head-and-neck cases is used to discuss the benefits of using pattern search methods in the optimization of the BAO problem.

Intensity‐modulated radiation therapy (IMRT) is a modern radiotherapy modality that uses a multileaf collimator to enable the irradiation of the patient with nonuniform maps of radiation from a set of distinct beam irradiation directions. The aim of IMRT is to eradicate all cancerous cells by irradiating the tumor with a prescribed dose while simultaneously sparing, as much as possible, the neighboring tissues and organs. The optimal choice of beam irradiation directions—beam angle optimization (BAO)—can play an important role in IMRT treatment planning by improving organ sparing and tumor coverage, increasing the treatment plan quality. Typically, the BAO search is guided by the optimal value of the fluence map optimization (FMO)—the problem of obtaining the most appropriate radiation intensities for each beam direction. In this paper, a new score to guide the BAO search is introduced and embedded in a parallel multistart derivative‐free optimization framework that is detailed for the extremely challenging continuous BAO problem. For the set of 10 clinical nasopharyngeal tumor cases considered, treatment plans obtained for optimized beam directions clearly outperform the benchmark treatment plans obtained considering equidistant beam directions typically used in clinical practice. Furthermore, treatment plans obtained considering the proposed score clearly improve the quality of the plans resulting from the use of the optimal value of the FMO problem to guide the BAO search.