Beam orientation optimization for IMRT by a hybrid method of the genetic algorithm and the simulated dynamics

We have developed a new method for beam orientation optimization in intensity-modulated radiation therapy (IMRT). The problem of beam orientation optimization in IMRT is solved by a decoupled two-step iterative process: (1) optimization of the intensity profiles for given beam configurations; (2) selection of optimal beam configurations based on the ranking by an objective function score for the results of the intensity profile optimization. The simulated dynamics algorithm is used for the intensity profile optimization. This algorithm enforces both the hard constraints and dose-volume constraints. A genetic algorithm is used to select beam orientation configurations. The method has been tested for both a simulated and clinical case, and the results show that beam orientation optimization significantly improved IMRT plans within a time period that is clinically acceptable. The results also show the dependence of the optimal orientation configurations on the prescribed constraints. In addition, beam orientation optimization by the method described here can provide multiple plans with similar dose distributions. This degeneracy characteristic can be exploited to our advantage in introducing additional planning objectives, e.g., the smoothness of intensity profiles, for the selection of the optimal plan among the degenerate configurations for treatment delivery.     

Direct-aperture optimization applied to selection of beam orientations in intensity-modulated radiation therapy

Direct-aperture optimization (DAO) was applied to iterative beam-orientation selection in intensity-modulated radiation therapy (IMRT), so as to ensure a realistic segmental treatment plan at each iteration. Nested optimization engines dealt separately with gantry angles, couch angles, collimator angles, segment shapes, segment weights and wedge angles. Each optimization engine performed a random search with successively narrowing step sizes. For optimization of segment shapes, the filtered backprojection (FBP) method was first used to determine desired fluence, the fluence map was segmented, and then constrained direct-aperture optimization was used thereafter. Segment shapes were fully optimized when a beam angle was perturbed, and minimally re-optimized otherwise. The algorithm was compared with a previously reported method using FBP alone at each orientation iteration. An example case consisting of a cylindrical phantom with a hemi-annular planning target volume (PTV) showed that for three-field plans, the method performed better than when using FBP alone, but for five or more fields, neither method provided much benefit over equally spaced beams. For a prostate case, improved bladder sparing was achieved through the use of the new algorithm. A plan for partial scalp treatment showed slightly improved PTV coverage and lower irradiated volume of brain with the new method compared to FBP alone. It is concluded that, although the method is computationally intensive and not suitable for searching large unconstrained regions of beam space, it can be used effectively in conjunction with prior class solutions to provide individually optimized IMRT treatment plans.     

Feasibility study of beam orientation class-solutions for prostate IMRT

IMRT is being increasingly used for treatment of prostate cancer. In practice, however, the beam orientations used for the treatments are still selected empirically, without any guideline. The purpose of this work was to investigate interpatient variation of the optimal beam configuration and to facilitate intensity modulated radiation therapy (IMRT) prostate treatment planning by proposing a set of beam orientation class-solutions for a range of numbers of incident beams. We used fifteen prostate cases to generate the beam orientation class-solutions. For each patient and a given number of incident beams, a multiobjective optimization engine was employed to provide optimal beam directions. For the fifteen cases considered, the gantry angle of any of the optimized plans were all distributed within a certain range The angular distributions of the optimal beams were analyzed and the most selected directions are identified as optimal directions. The optimal directions for all patients are averaged to obtain the class-solution. The class-solution gantry angles for prostate IMRT were found to be: three beams (0 degrees, 120 degrees, 240 degrees), five beams (35 degrees, 110 degrees, 180 degrees, 250 degrees, 325 degrees), six beams (0 degrees, 60 degrees, 120 degrees, 180 degrees, 240 degrees, 300 degrees), seven beams (25 degrees, 75 degrees, 130 degrees, 180 degrees, 230 degrees, 285 degrees, 335 degrees), eight beams (20 degrees, 70 degrees, 110 degrees, 150 degrees, 200 degrees, 250 degrees, 290 degrees, 340 degrees), and nine beams (20 degrees, 60 degrees, 100 degrees, 140 degrees, 180 degrees, 220 degrees, 260 degrees, 300 degrees, 340 degrees). The level of validity of the class-solutions was tested using an additional clinical prostate case by comparing with the individually optimized beam configurations. The difference between the plans obtained with class-solutions and patient-specific optimizations was found to be clinically insignificant.     

A particle swarm optimization algorithm for beam angle selection in intensity-modulated radiotherapy planning

Automatic beam angle selection is an important but challenging problem for intensity-modulated radiation therapy (IMRT) planning. Though many efforts have been made, it is still not very satisfactory in clinical IMRT practice because of overextensive computation of the inverse problem. In this paper, a new technique named BASPSO (Beam Angle Selection with a Particle Swarm Optimization algorithm) is presented to improve the efficiency of the beam angle optimization problem. Originally developed as a tool for simulating social behaviour, the particle swarm optimization (PSO) algorithm is a relatively new population-based evolutionary optimization technique first introduced by Kennedy and Eberhart in 1995. In the proposed BASPSO, the beam angles are optimized using PSO by treating each beam configuration as a particle (individual), and the beam intensity maps for each beam configuration are optimized using the conjugate gradient (CG) algorithm. These two optimization processes are implemented iteratively. The performance of each individual is evaluated by a fitness value calculated with a physical objective function. A population of these individuals is evolved by cooperation and competition among the individuals themselves through generations. The optimization results of a simulated case with known optimal beam angles and two clinical cases (a prostate case and a head-and-neck case) show that PSO is valid and efficient and can speed up the beam angle optimization process. Furthermore, the performance comparisons based on the preliminary results indicate that, as a whole, the PSO-based algorithm seems to outperform, or at least compete with, the GA-based algorithm in computation time and robustness. In conclusion, the reported work suggested that the introduced PSO algorithm could act as a new promising solution to the beam angle optimization problem and potentially other optimization problems in IMRT, though further studies need to be investigated.     

Simultaneous optimization of beam orientations, wedge filters and field weights for inverse planning with anatomy-based MLC fields

As an alternative between manual planning and beamlet-based IMRT, we have developed an optimization system for inverse planning with anatomy-based MLC fields. In this system, named Ballista, the orientation (table and gantry), the wedge filter and the field weights are simultaneously optimized for every beam. An interesting feature is that the system is coupled to Pinnacle3 by means of the PinnComm interface, and uses its convolution dose calculation engine. A fully automatic MLC segmentation algorithm is also included. The plan evaluation is based on a quasi-random sampling and on a quadratic objective function with penalty-like constraints. For efficiency, optimal wedge angles and wedge orientations are determined using the concept of the super-omni wedge. A bound-constrained quasi-Newton algorithm performs field weight optimization, while a fast simulated annealing algorithm selects the optimal beam orientations. Moreover, in order to generate directly deliverable plans, the following practical considerations have been incorporated in the system: collision between the gantry and the table as well as avoidance of the radio-opaque elements of a table top. We illustrate the performance of the new system on two patients. In a rhabdomyosarcoma case, the system generated plans improving both the target coverage and the sparing of the parotide, as compared to a manually designed plan. In the second case presented, the system successfully produced an adequate plan for the treatment of the prostate while avoiding both hip prostheses. For the many cases where full IMRT may not be necessary, the system efficiently generates satisfactory plans meeting the clinical objectives, while keeping the treatment verification much simpler.     

Algorithm and performance of a clinical IMRT beam-angle optimization system

This paper describes the algorithm and examines the performance of an intensity-modulated radiation therapy (IMRT) beam-angle optimization (BAO) system. In this algorithm successive sets of beam angles are selected from a set of predefined directions using a fast simulated annealing (FSA) algorithm. An IMRT beam-profile optimization is performed on each generated set of beams. The IMRT optimization is accelerated by using a fast dose calculation method that utilizes a precomputed dose kernel. A compact kernel is constructed for each of the predefined beams prior to starting the FSA algorithm. The IMRT optimizations during the BAO are then performed using these kernels in a fast dose calculation engine. This technique allows the IMRT optimization to be performed more than two orders of magnitude faster than a similar optimization that uses a convolution dose calculation engine. Any type of optimization criterion present in the IMRT system can be used in this BAO system. An objective function based on clinically-relevant dose-volume (DV) criteria is used in this study. This facilitates the comparison between a BAO plan and the corresponding plan produced by a planner since the latter is usually optimized using a DV-based objective function. A simple prostate case and a complex head-and-neck (HN) case were used to evaluate the usefulness and performance of this BAO method. For the prostate case we compared the BAO results for three, five and seven coplanar beams with those of the same number of equispaced coplanar beams. For the HN case we compare the BAO results for seven and nine non-coplanar beams with that for nine equispaced coplanar beams. In each case the BAO algorithm was allowed to search up to 1000 different sets of beams. The BAO for the prostate cases were finished in about 1-2 h on a moderate 400 MHz workstation while that for the head-and-neck cases were completed in 13-17 h on a 750 MHz machine. No a priori beam-selection criteria have been used in achieving this performance. In both the prostate and the head-and-neck cases, BAO is shown to provide improvements in plan quality over that of the equispaced beams. The use of DV-based objective function also allows us to study the dependence of the improvement of plan quality offered by BAO on the DV criteria used in the optimization. We found that BAO is especially useful for cases that require strong DV criteria. The main advantages of this BAO system are its speed and its direct link to a clinical IMRT system.     

A simple geometric algorithm to predict optimal starting gantry angles using equiangular-spaced beams for intensity modulated radiation therapy of prostate cancer

A fast, geometric beam angle optimization (BAO) algorithm for clinical intensity-modulated radiation therapy (IMRT) was implemented on ten localized prostate cancer patients on the Radiation Therapy Oncology Group (RTOG) 0126 protocol. The BAO algorithm computed the beam intersection volume (BIV) within the rectum and bladder using five and seven equiangular-spaced beams as a function of starting gantry angle for comparison to the V 75 Gy and V 70 Gy. A mathematical theory was presented to explain the correlation of BIV with dose and dose-volume metrics. The class solution 'W' pattern in the rectal V 75 Gy and V 70 Gy as a function of starting gantry angle using five equiangular-spaced beams (with two separate minima centered near 20 degrees and 50 degrees) was reproduced by the 5 BIV within the rectum. A strong correlation was found between the rectal 5 BIV and the rectal V 75 Gy and V 70 Gy as a function of starting gantry angle. The BAO algorithm predicted the location of the two dosimetric minima in rectal V 75 Gy and V 70 Gy (optimal starting gantry angles) to within 5 degrees. It was demonstrated that the BIV geometric variations for seven equiangular-spaced beams were too small to translate into a strong dosimetric effect in the rectal V 75 Gy and V 70 Gy. The relatively flat distribution with starting gantry angle of the bladder V 75 Gy and V 70 Gy was reproduced by the bladder five and seven BIV for each patient. A geometric BAO method based on BIV has the advantage over dosimetric BAO methods of simplicity and rapid computation time. This algorithm can be used as a standalone optimization method or act as a rapid calculation filter to reduce the search space for a dosimetric BAO method. Given the clinically infeasible computation times of many dosimetric beam orientation optimization algorithms, this robust geometric BIV algorithm has the potential to facilitate beam angle selection for prostate IMRT in clinical practice.     

Computer-assisted selection of coplanar beam orientations in intensity-modulated radiation therapy

In intensity-modulated radiation therapy (IMRT), the incident beam orientations are often determined by a trial and error search. The conventional beam's-eye view (BEV) tool becomes less helpful in IMRT because it is frequently required that beams go through organs at risk (OARs) in order to achieve a compromise between the dosimetric objectives of the planning target volume (PTV) and the OARs. In this paper, we report a beam's-eye view dosimetrics (BEVD) technique to assist in the selection of beam orientations in IMRT. In our method, each beam portal is divided into a grid of beamlets. A score function is introduced to measure the 'goodness' of each beamlet at a given gantry angle. The score is determined by the maximum PTV dose deliverable by the beamlet without exceeding the tolerance doses of the OARs and normal tissue located in the path of the beamlet. The overall score of the gantry angle is given by a sum of the scores of all beamlets. For a given patient. the score function is evaluated for each possible beam orientation. The directions with the highest scores are then selected as the candidates for beam placement. This procedure is similar to the BEV approach used in conventional radiation therapy, except that the evaluation by a human is replaced by a score function to take into account the intensity modulation. This technique allows one to select beam orientations without the excessive computing overhead of computer optimization of beam orientation. It also provides useful insight into the problem of selection of beam orientation and is especially valuable for complicated cases where the PTV is surrounded by several sensitive structures and where it is difficult to select a set of 'good' beam orientations. Several two-dimensional (2D) model cases were used to test the proposed technique. The plans obtained using the BEVD-selected beam orientations were compared with the plans obtained using equiangular spaced beams. For all the model cases investigated, the use of BEVD-selected beam orientations improved the dose distributions significantly. These examples indicate that the technique has considerable potential for simplifying the IMRT treatment planning process and allows for better utilization of the technical capacity of IMRT.     

An algorithm for systematic selection of beam directions for IMRT

Selection of the number of beams and their directions can be an important problem in radiation therapy, especially when a tumor surrounds a critical organ or is surrounded by multiple critical organs. Beam directions, in this sense, are chosen to not only avoid critical organs, but also to achieve better target dose uniformity. In intensity-modulated radiation therapy (IMRT), optimization of beam directions is further complicated due to the dependence of one beam direction on its corresponding beamlet intensities and the beamlet intensities of all other beam directions. The result is an excessively enlarged search space, even when the number of beams is small (two to three). Until now, only a handful of publications exist regarding beam direction optimization in IMRT. Here, we report a new systematic approach that determines a suitable number of "more optimal" beam directions without optimizing a complicated objective function or resorting to brute force. We start by assuming that beam directions chosen for an N-beam plan are candidates for beam directions in the search for an (N + 1)-beam plan. Knowing that beam directions in an N-beam plan are not always the best choices for the (N + 1)-beam plan, we introduce into the beam direction selection process an analysis of the beamlet weights of every beam direction set sampled. If the relative weights of any particular beam compared to other beams are insignificant and hence have no significant effect on the quality of the treatment plan, then we eliminate this beam from the plan. The algorithm terminates basically when the relative weights of the last beam compared to other beams are insignificant or the replacement of an eliminated beam does not improve the plan. This concept was applied to three two-dimensional phantoms and each plan was compared to a standard equally spaced IMRT plan in terms of dose distributions, dose-volume histograms, and objective function values. The results show improvements in both target dose uniformity and critical organ sparing often with a fewer number of beams than standard equally spaced beam plans.     

Beam orientation optimization for intensity-modulated radiation therapy using mixed integer programming

The purpose of this study is to extend an algorithm proposed for beam orientation optimization in classical conformal radiotherapy to intensity-modulated radiation therapy (IMRT) and to evaluate the algorithm's performance in IMRT scenarios. In addition, the effect of the candidate pool of beam orientations, in terms of beam orientation resolution and starting orientation, on the optimized beam configuration, plan quality and optimization time is also explored. The algorithm is based on the technique of mixed integer linear programming in which binary and positive float variables are employed to represent candidates for beam orientation and beamlet weights in beam intensity maps. Both beam orientations and beam intensity maps are simultaneously optimized in the algorithm with a deterministic method. Several different clinical cases were used to test the algorithm and the results show that both target coverage and critical structures sparing were significantly improved for the plans with optimized beam orientations compared to those with equi-spaced beam orientations. The calculation time was less than an hour for the cases with 36 binary variables on a PC with a Pentium IV 2.66 GHz processor. It is also found that decreasing beam orientation resolution to 10 degrees greatly reduced the size of the candidate pool of beam orientations without significant influence on the optimized beam configuration and plan quality, while selecting different starting orientations had large influence. Our study demonstrates that the algorithm can be applied to IMRT scenarios, and better beam orientation configurations can be obtained using this algorithm. Furthermore, the optimization efficiency can be greatly increased through proper selection of beam orientation resolution and starting beam orientation while guaranteeing the optimized beam configurations and plan quality.     

Automatic beam angle selection in IMRT planning using genetic algorithm

The selection of suitable beam angles in external beam radiotherapy is at present generally based upon the experience of the human planner. The requirement to automatically select beam angles is particularly highlighted in intensity-modulated radiation therapy (IMRT), in which a smaller number of modulated beams is hoped to be used, in comparison with conformal radiotherapy. It has been proved by many researchers that the selection of suitable beam angles is most valuable for a plan with a small number of beams (< or = 5). In this paper an efficient method is presented to investigate how to improve the dose distributions by selecting suitable coplanar beam angles. In our automatic beam angle selection (ABAS) algorithm, the optimal coplanar beam angles correspond to the lowest objective function value of the dose distributions calculated using the intensity-modulated maps of this group of candidate beams. Due to the complexity of the problem and the large search space involved, the selection of beam angles and the optimization of intensity maps are treated as two separate processes and implemented iteratively. A genetic algorithm (GA) incorporated with an immunity operation is used to select suitable beam angles, and a conjugate gradient (CG) method is used to quickly optimize intensity maps for each selected beam combination based on a dose-based objective function. A pencil-beam-based three-dimensional (3D) full scatter convolution (FSC) algorithm is employed for the dose calculation. Two simulated cases with obvious optimal beam angles are used to verify the validity of the presented technique, and a more complicated case simulating a prostate tumour and two clinical cases are employed to test the efficiency of ABAS. The results show that ABAS is valid and efficient and can improve the dose distributions within a clinically acceptable computation time.     

Comparative analysis of Pareto surfaces in multi-criteria IMRT planning

In the multi-criteria optimization approach to IMRT planning, a given dose distribution is evaluated by a number of convex objective functions that measure tumor coverage and sparing of the different organs at risk. Within this context optimizing the intensity profiles for any fixed set of beams yields a convex Pareto set in the objective space. However, if the number of beam directions and irradiation angles are included as free parameters in the formulation of the optimization problem, the resulting Pareto set becomes more intricate. In this work, a method is presented that allows for the comparison of two convex Pareto sets emerging from two distinct beam configuration choices. For the two competing beam settings, the non-dominated and the dominated points of the corresponding Pareto sets are identified and the distance between the two sets in the objective space is calculated and subsequently plotted. The obtained information enables the planner to decide if, for a given compromise, the current beam setup is optimal. He may then re-adjust his choice accordingly during navigation. The method is applied to an artificial case and two clinical head neck cases. In all cases no configuration is dominating its competitor over the whole Pareto set. For example, in one of the head neck cases a seven-beam configuration turns out to be superior to a nine-beam configuration if the highest priority is the sparing of the spinal cord. The presented method of comparing Pareto sets is not restricted to comparing different beam angle configurations, but will allow for more comprehensive comparisons of competing treatment techniques (e.g., photons versus protons) than with the classical method of comparing single treatment plans.     

Dosimetry validation of treatment room shielding design

Intensity-modulated radiation therapy (IMRT) can lead to an increase in leakage radiation. The total number of monitor units (MUs) for IMRT is typically 2-5 times that for conventional treatments [the ratio of the two is used to derive the effective modulation scaling factor (MSFeff)]. Shielding calculations for IMRT can be done by applying the MSFeff to measured exposures under conservative conditions (standard beam setup 40 cm x 40 cm field, 45 degrees collimator angle) to account for the increased leakage. In this work, we verified this approach for two existing vaults housing a Siemens Primart 6 MV linac and a Varian 21Ex 10 MV linac. We measured the cumulative exposures at various locations around the vaults for typical IMRT cases and for the standard beam setup using the same MUs. For the standard beam setup, the IMRT gantry angles and eight equally spaced angles were used. Estimations of weekly exposures for IMRT were carried out using exposure rates measured under standard beam setup and the MSFeff averaged over 20 treatment cases. The accumulated exposures under realistic IMRT conditions were 30%-50% lower than the estimated values using equally spaced gantry angles except for two locations where the real IMRT leakage was higher than the estimated value by approximately 10%. Measurements using the same gantry angles yielded similar results. Our results indicate that it is adequate to use the MSFeff and previously measured exposures to estimate the leakage increase due to IMRT for an existing vault. Different approaches should be followed when considering primary or secondary barriers since the standard beam setup is overestimating the exposures behind primary barriers compared to IMRT. In such cases, a 10 cm x 10 cm field can be used for more accurate shielding evaluation.     

Beam orientation optimization for intensity modulated radiation therapy using adaptive l(2,1)-minimization

Beam orientation optimization (BOO) is a key component in the process of intensity modulated radiation therapy treatment planning. It determines to what degree one can achieve a good treatment plan in the subsequent plan optimization process. In this paper, we have developed a BOO algorithm via adaptive l(2, 1)-minimization. Specifically, we introduce a sparsity objective function term into our model which contains weighting factors for each beam angle adaptively adjusted during the optimization process. Such an objective function favors a small number of beam angles. By optimizing a total objective function consisting of a dosimetric term and the sparsity term, we are able to identify unimportant beam angles and gradually remove them without largely sacrificing the dosimetric objective. In one typical prostate case, the convergence property of our algorithm, as well as how beam angles are selected during the optimization process, is demonstrated. Fluence map optimization (FMO) is then performed based on the optimized beam angles. The resulting plan quality is presented and is found to be better than that of equiangular beam orientations. We have further systematically validated our algorithm in the contexts of 5-9 coplanar beams for five prostate cases and one head and neck case. For each case, the final FMO objective function value is used to compare the optimized beam orientations with the equiangular ones. It is found that, in the majority of cases tested, our BOO algorithm leads to beam configurations which attain lower FMO objective function values than those of corresponding equiangular cases, indicating the effectiveness of our BOO algorithm. Superior plan qualities are also demonstrated by comparing DVH curves between BOO plans and equiangular plans.     

Volumetric modulated arc therapy: IMRT in a single gantry arc

In this work a novel plan optimization platform is presented where treatment is delivered efficiently and accurately in a single dynamically modulated arc. Improvements in patient care achieved through image-guided positioning and plan adaptation have resulted in an increase in overall treatment times. Intensity-modulated radiation therapy (IMRT) has also increased treatment time by requiring a larger number of beam directions, increased monitor units (MU), and, in the case of tomotherapy, a slice-by-slice delivery. In order to maintain a similar level of patient throughput it will be necessary to increase the efficiency of treatment delivery. The solution proposed here is a novel aperture-based algorithm for treatment plan optimization where dose is delivered during a single gantry arc of up to 360 deg. The technique is similar to tomotherapy in that a full 360 deg of beam directions are available for optimization but is fundamentally different in that the entire dose volume is delivered in a single source rotation. The new technique is referred to as volumetric modulated arc therapy (VMAT). Multileaf collimator (MLC) leaf motion and number of MU per degree of gantry rotation is restricted during the optimization so that gantry rotation speed, leaf translation speed, and dose rate maxima do not excessively limit the delivery efficiency. During planning, investigators model continuous gantry motion by a coarse sampling of static gantry positions and fluence maps or MLC aperture shapes. The technique presented here is unique in that gantry and MLC position sampling is progressively increased throughout the optimization. Using the full gantry range will theoretically provide increased flexibility in generating highly conformal treatment plans. In practice, the additional flexibility is somewhat negated by the additional constraints placed on the amount of MLC leaf motion between gantry samples. A series of studies are performed that characterize the relationship between gantry and MLC sampling, dose modeling accuracy, and optimization time. Results show that gantry angle and MLC sample spacing as low as 1 deg and 0.5 cm, respectively, is desirable for accurate dose modeling. It is also shown that reducing the sample spacing dramatically reduces the ability of the optimization to arrive at a solution. The competing benefits of having small and large sample spacing are mutually realized using the progressive sampling technique described here. Preliminary results show that plans generated with VMAT optimization exhibit dose distributions equivalent or superior to static gantry IMRT. Timing studies have shown that the VMAT technique is well suited for on-line verification and adaptation with delivery times that are reduced to approximately 1.5-3 min for a 200 cGy fraction.     

Leaf-sequencing for intensity-modulated arc therapy using graph algorithms

Intensity-modulated arc therapy (IMAT) is a rotational IMRT technique. It uses a set of overlapping or nonoverlapping arcs to create a prescribed dose distribution. Despite its numerous advantages, IMAT has not gained widespread clinical applications. This is mainly due to the lack of an effective IMAT leaf-sequencing algorithm that can convert the optimized intensity patterns for all beam directions into IMAT treatment arcs. To address this problem, we have developed an IMAT leaf-sequencing algorithm and software using graph algorithms in computer science. The input to our leaf-sequencing software includes (1) a set of (continuous) intensity patterns optimized by a treatment planning system at a sequence of equally spaced beam angles (typically 10 degrees apart), (2) a maximum leaf motion constraint, and (3) the number of desired arcs, k. The output is a set of treatment arcs that best approximates the set of optimized intensity patterns at all beam angles with guaranteed smooth delivery without violating the maximum leaf motion constraint. The new algorithm consists of the following key steps. First, the optimized intensity patterns are segmented into intensity profiles that are aligned with individual MLC leaf pairs. Then each intensity profile is segmented into k MLC leaf openings using a k-link shortest path algorithm. The leaf openings for all beam angles are subsequently connected together to form 1D IMAT arcs under the maximum leaf motion constraint using a shortest path algorithm. Finally, the 1D IMAT arcs are combined to form IMAT treatment arcs of MLC apertures. The performance of the implemented leaf-sequencing software has been tested for four treatment sites (prostate, breast, head and neck, and lung). In all cases, our leaf-sequencing algorithm produces efficient and highly conformal IMAT plans that rival their counterpart, the tomotherapy plans, and significantly improve the IMRT plans. Algorithm execution times ranging from a few seconds to 2 min are observed on a laptop computer equipped with a 2.0 GHz Pentium M processor.     

Incorporating multi-leaf collimator leaf sequencing into iterative IMRT optimization

Intensity modulated radiation therapy (IMRT) treatment planning typically considers beam optimization and beam delivery as separate tasks. Following optimization, a multi-leaf collimator (MLC) or other beam delivery device is used to generate fluence patterns for patient treatment delivery. Due to limitations and characteristics of the MLC, the deliverable intensity distributions often differ from those produced by the optimizer, leading to differences between the delivered and the optimized doses. Objective function parameters are then adjusted empirically, and the plan is reoptimized to achieve a desired deliverable dose distribution. The resulting plan, though usually acceptable, may not be the best achievable. A method has been developed to incorporate the MLC restrictions into the optimization process. Our in-house IMRT system has been modified to include the calculation of the deliverable intensity into the optimizer. In this process, prior to dose calculation, the MLC leaf sequencer is used to convert intensities to dynamic MLC sequences, from which the deliverable intensities are then determined. All other optimization steps remain the same. To evaluate the effectiveness of deliverable-based optimization, 17 patient cases have been studied. Compared with standard optimization plus conversion to deliverable beams, deliverable-based optimization results show improved isodose coverage and a reduced dose to critical structures. Deliverable-based optimization results are close to the original nondeliverable optimization results, suggesting that IMRT can overcome the MLC limitations by adjusting individual beamlets. The use of deliverable-based optimization may reduce the need for empirical adjustment of objective function parameters and reoptimization of a plan to achieve desired results.     

鼻咽癌放射治疗正向调强计划设计方法

目的利用正向调强计划实现鼻咽癌放射治疗计划设计,总结出适合笔者单位的计划设计方案。方法选择2009年1月至2010年5月40例鼻咽癌患者,其中男性26例,女性14例,年龄30～70岁,平均年龄45岁。进行鼻咽癌治疗计划设计,讨论鼻咽癌计划设计的技巧。根据靶区和危及器官的形态和位置、预设射野方向,人工设置射野形状,根据处方剂量做射野权重的优化,再人为增加子野并调整子野形状,再优化,循环调整和优化,最后得到合适的计划。结果在所有病例中,一般预设7个大野,角度是210°、260°、300°、0°、60°、100°、150°,实际射野角度可以在预设方向附近做调整。根据处方剂量设计计划,大体靶体积(GTV1)为(6636.45±126.43)cGy,均匀度为1.0660±0.0217;临床靶体积(CTV1)为(6396.08±106.01)cGy,均匀度为1.1440±0.0306;CTV2为(5647.16±197.53)cGy,均匀度为(1.0860±0.0456)。并且所有计划基本满足90%CTV达到100%处方剂量,95%以上GTV达到100%处方剂量。结论调强放射治疗(IMRT)是先进的放射治疗技术,但正向调强计划的优劣很大程度上取决于操作者的经验,掌握合适技巧,运用合理方案,才可以得到符合要求的计划,总体说来鼻咽癌的正向调强计划比逆向调强计划有更加灵活的射野形状设置和权重给予,更有助于满足临床要求。     

Speed and convergence properties of gradient algorithms for optimization of IMRT

Gradient algorithms are the most commonly employed search methods in the routine optimization of IMRT plans. It is well known that local minima can exist for dose-volume-based and biology-based objective functions. The purpose of this paper is to compare the relative speed of different gradient algorithms, to investigate the strategies for accelerating the optimization process, to assess the validity of these strategies, and to study the convergence properties of these algorithms for dose-volume and biological objective functions. With these aims in mind, we implemented Newton's, conjugate gradient (CG), and the steepest decent (SD) algorithms for dose-volume- and EUD-based objective functions. Our implementation of Newton's algorithm approximates the second derivative matrix (Hessian) by its diagonal. The standard SD algorithm and the CG algorithm with "line minimization" were also implemented. In addition, we investigated the use of a variation of the CG algorithm, called the "scaled conjugate gradient" (SCG) algorithm. To accelerate the optimization process, we investigated the validity of the use of a "hybrid optimization" strategy, in which approximations to calculated dose distributions are used during most of the iterations. Published studies have indicated that getting trapped in local minima is not a significant problem. To investigate this issue further, we first obtained, by trial and error, and starting with uniform intensity distributions, the parameters of the dose-volume- or EUD-based objective functions which produced IMRT plans that satisfied the clinical requirements. Using the resulting optimized intensity distributions as the initial guess, we investigated the possibility of getting trapped in a local minimum. For most of the results presented, we used a lung cancer case. To illustrate the generality of our methods, the results for a prostate case are also presented. For both dose-volume and EUD based objective functions, Newton's method far outperforms other algorithms in terms of speed. The SCG algorithm, which avoids expensive "line minimization," can speed up the standard CG algorithm by at least a factor of 2. For the same initial conditions, all algorithms converge essentially to the same plan. However, we demonstrate that for any of the algorithms studied, starting with previously optimized intensity distributions as the initial guess but for different objective function parameters, the solution frequently gets trapped in local minima. We found that the initial intensity distribution obtained from IMRT optimization utilizing objective function parameters, which favor a specific anatomic structure, would lead to a local minimum corresponding to that structure. Our results indicate that from among the gradient algorithms tested, Newton's method appears to be the fastest by far. Different gradient algorithms have the same convergence properties for dose-volume- and EUD-based objective functions. The hybrid dose calculation strategy is valid and can significantly accelerate the optimization process. The degree of acceleration achieved depends on the type of optimization problem being addressed (e.g., IMRT optimization, intensity modulated beam configuration optimization, or objective function parameter optimization). Under special conditions, gradient algorithms will get trapped in local minima, and reoptimization, starting with the results of previous optimization, will lead to solutions that are generally not significantly different from the local minimum.