A Study On Planning Optimization In Intensity Modulated Radiation Therapy

Radiotherapy is one of the main methods for tumor treatment.The accurate targeting of tumours with maximal sparing of normal tissues has been the foremost goal of radiotherapy practice.Over the past two decades,the ability to achieve this goal has improved greatly through advances in computer technology and imaging technology,specifically the development of computerized tomography and magnetic resonance imaging.Intensity modulated radiation therapy(IMRT)is a sophisticated type of three-dimensional conformal radiotherapy that has the potential to improve patient outcome by reducing morbidity or increasing local tumour control,which is the mainstream of radiotherapy in twenty-first century.IMRT requires the setting of the relative intensities of tens of thousands of individual beamlets comprising an intensity modulated treatment plan.This task cannot be accomplished manually and requires the use of a multileaf collimator(MLC)and specialized computer assisted optimization methods.Nevertheless,due to its complexity,IMRT has not achieved its potential,still has many problems needed to be dealt with.In this thesis,the researches are done on the the automated IMRT treatment planning method,on the direct aperture optimization(DAO),on the aperture generation,on shorten optimization and saving treatment time,and so on,which are briefly listed below:1.In inverse treatment planning of IMRT,the objective function is typically the sum of the weighted sub-scores,where the weights indicate the importance of the sub-scores.To obtain a high-quality treatment plan,the planner manually adjusts the objective weights using a trial-and-error procedure until an acceptable plan is reached.In this work,a new particle swarm optimization method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment,thereby reducing theworkload of the human planner and contributing to the development of a fully automated planning process.The proposed optimization method consists of three steps.(i)First,a swarm of weighting factors(i.e.,particles)is initialized randomly in the search space,where each particle corresponds to a global objective function.(ii)Then,a plan optimization solver is employed to obtain the optimal solution for each particle,and the values of the evaluation functions used to determine the particle's location and the population global location for the particle swarm optimization are calculated based on these results.(iii)Next,the weighting factors are updated based on the particle's location and the population global location.Step(ii)is performed alternately with step(iii)until the termination condition is reached.In this method,the evaluation function is a combination of several key points on the dose volume histograms.Furthermore,a perturbation strategy – the crossover and mutation operator hybrid approach – is employed to enhance the population diversity,and two arguments are applied to the evaluation function to improve the flexibility of the algorithm.A comparison of the results with the optimized solution obtained using a similar optimization model but with human planner intervention revealed that the proposed algorithm produced optimized plans superior to that developed using the manual plan.The proposed algorithm can generate admissible solutions within reasonable computational times and can be used to develop fully automated IMRT treatment planning methods,thus reducing human planners' workloads during iterative processes.2.Aiming at the disadvantages of traditional DAO method,such as slow convergence rate,prone to stagnation and weak global searching ability,a gradient-based DAO is proposed.In this work,two different optimization methods are used to optimize the shapes and the weights of the apertures.Firstly,in order to improve the validity of the aperture shapes optimization of each search,the traditional simulated annealing(SA)algorithm is improved,the gradient is introduced to the algorithm.The shapes of the apertures are optimized by the gradient based SA algorithm method.At the same time,the constraints between the leaves of multileaf collimator have been fully considered,the optimized aperture shapes are meeting the requirements of clinical radiation therapy.After that,the weights of the apertures areoptimized by the limited-memory BFGS for bound-constrained algorithm,which is simple in calculation,fast in convergence rate,and suitable for solving large scale constrained optimization.Compared with the traditional algorithm,the time cost of this program decreased by 15.90%;the minimum dose for the planning target volume was improved by0.29%,the highest dose for the planning target volume was reduced by 0.45%;the highest dose for the bladder and rectum,which are the organs at risk,decreased by 0.25% and 0.09%,respectively.The results of experiment show that the new algorithm can produce highly efficient treatment planning a short time and can be used in clinical practice.3.To reduce computation and decrease planning time with little or no loss in plan quality compared to the column-generation-based(cg)method,a simple method of aperture generation based on threshold segmentation(agts)is proposed,which can be embedded in a DAO algorithm and used for aperture generation in IMRT planning for step-and-shoot delivery.The proposed agts strategy consists of two steps: a fuzzy controller and the threshold segmentation algorithm.Because of the numerical instabilities,noise always exists in practice.This noise can easily influence the connected region,and always limits the extension of the connected region.First,a fuzzy controller is used for noise cancellation.Then,new apertures are added using the threshold segmentation algorithm,rather than solving the original pricing problem.The experimental results show that the planning time required by the agts method was approximately 58.61% ? 0.40 less than that required by the cg method.The cg method is always time-consuming,because it involves more apertures that require re-optimization,whereas the agts method requires fewer apertures.The results of experiment show that the agts algorithm can decrease planning time with little or no loss in plan quality compared to the cg method.