Development of methods for beam angle optimization for IMRT using an accelerated exhaustive search strategy

An IMRT planning technique for treating whole breast or chest wall with regional lymph nodes on Halcyon and Ethos

PURPOSE/OBJECTIVE

Field size limitations on Halcyon and Ethos treatment machines largely preclude use of the conventional monoisocentric three-field technique for breast/chest wall and regional lymph nodes. We present an alternative, IMRT-based planning approach that facilitates treatment on Halcyon and Ethos while preserving plan quality.

MATERIALS/METHODS

Eight breast and regional node cases (four left-sided, four right-sided) were planned for an Ethos machine using a 15-17 field IMRT technique. Institutional plan quality metrics for CTV and PTV coverage and OAR sparing were assessed. Five plans (four right-sided, one left-sided) were also planned using a hybrid 3D multisocenter technique. CTV coverage and OAR sparing were compared to the IMRT plans. Eclipse scripting tools were developed to aid in beam placement and plan evaluation through a set of dosimetric scorecards, and both are shared publicly.

RESULTS

On average, the IMRT plans achieved breast CTV and PTV coverage at 50 Gy of 97.9% and 95.7%, respectively. Supraclavicular CTV and PTV coverages at 45 Gy were 100% and 95.5%. Axillary lymph node CTV and PTV coverages at 45 Gy were 100% and 97.1%, and IMN CTV coverage at 45 Gy was 99.2%. Mean ipsilateral lung V20 Gy was 19.3%, and average mean heart dose was 1.6 Gy for right-sided cases and 3.0 Gy for left-sided. In comparison to the hybrid 3D plans, IMRT plans achieved higher breast and supraclavicular CTV coverage (99.9% vs. 98.6% and 99.9% vs. 93.4%), higher IMN coverage (99.6% vs. 78.2%), and lower ipsilateral lung V20 Gy (19.6% vs. 28.2%).

CONCLUSION

Institutional plan quality benchmarks were achieved for all eight cases using the IMRT-based planning approach. The IMRT-based planning approach offered superior conformity and OAR sparing than a competing hybrid 3D approach.

Implementation and evaluation of an iterative-based algorithm for automatic beam angle optimization in breast cancer treatment planning

INTRODUCTION

A beam angle optimization (BAO) algorithm was developed to evaluate its clinical feasibility and investigate the impact of varying BAO constraints on breast cancer treatment plans.

MATERIALS AND METHODS

A two-part study was designed. In part 1, we retrospectively selected 20 patients treated with radiotherapy after breast-conserving surgery. For each patient, BAO plans were designed using beam angles optimized by the BAO algorithm and the same optimization constraints as manual plans. Dosimetric indices were compared between BAO and manual plans. In part 2, fifteen patients with left breast cancer were included. For each patient, three distinct cardiac constraints (mean heart dose < 5 Gy, 3 Gy or 1 Gy) were established during the BAO process to obtain three optimized beam sets which were marked as BAO_H1, BAO_H3, BAO_H5, respectively. These sets of beams were then utilized under identical IMRT constraints for planning. Comparative analysis was conducted among the three groups of plans.

RESULTS

For part 1, no significant differences were observed between BAO plans and manual plans in all dosimetric indices, except for ipsilateral lung V5, where BAO plans performed slightly better than manual plans (35.5% ± 5.6% vs 36.9% ± 4.3%, p = 0.034). For part 2, Stricter BAO heart constraints resulted in more perpendicular beams. However, there was no significant difference between BAO_H1, BAO_H3 and BAO_H5 with the same IMRT constraint in the heart dose. Meanwhile, the left lung dose was increased while the right breast and lung doses were decreased with stricter heart constraints in BAO. When mean heart dose < 5 Gy in IMRT constraint, the mean dose to the right lung was decreased from 0.46 Gy for BAO_H5 to 0.33 Gy for BAO_H1 (p = 0.027).

CONCLUSIONS

The BAO algorithm can achieve quality plans comparable to manual plans. IMRT constraints dominate the final plan dose, while varying BAO constraints alter the trade-off among structures, providing an additional degree of freedom in planning design.

A unified path seeking algorithm for IMRT and IMPT beam orientation optimization

Objective. Fully automated beam orientation optimization (BOO) for intensity-modulated radiotherapy and intensity modulated proton therapy (IMPT) is gaining interest, since achieving optimal plan quality for an unknown number of fixed beam arrangements is tedious. Fast group sparsity-based optimization methods have been proposed to find the optimal orientation, but manual tuning is required to eliminate the exact number of beams from a large candidate set. Here, we introduce a fast, automated gradient descent-based path-seeking algorithm (PathGD), which performs fluence map optimization for sequentially added beams, to visualize the dosimetric benefit of one added field at a time.Approach. Several configurations of 2-4 proton and 5-15 photon beams were selected for three head-and-neck patients using PathGD, which was compared to group sparsity-regularized BOO solved with the fast iterative shrinkage-thresholding algorithm (GS-FISTA), and manually selected IMPT beams or one coplanar photon VMAT arc (MAN). Once beams were chosen, all plans were compared on computational efficiency, dosimetry, and for proton plans, robustness.Main results. With each added proton beam, Clinical Target Volume (CTV) and organs at risk (OAR) dosimetric cost improved on average across plans by [1.1%, 13.6%], and for photons, [0.6%, 2.0%]. Comparing algorithms, beam selection for PathGD was faster than GS-FISTA on average by 35%, and PathGD matched the CTV coverage of GS-FISTA plans while reducing OAR mean and maximum dose in all structures by an average of 13.6%. PathGD was able to improve CTV [Dmax, D95%] by [2.6%, 5.2%] and reduced worst-case [max, mean] dose in OARs by [11.1%, 13.1%].Significance. The benefit of a path-seeking algorithm is the beam-by-beam analysis of dosimetric cost. PathGD was shown to be most efficient and dosimetrically desirable amongst group sparsity and manual BOO methods, and highlights the sensitivity of beam addition for IMPT in particular.

History of Technological Advancements towards MR-Linac: The Future of Image-Guided Radiotherapy

Image-guided radiotherapy (IGRT) enables optimal tumor targeting and sparing of organs-at-risk, which ultimately results in improved outcomes for patients. Magnetic resonance imaging (MRI) revolutionized diagnostic imaging with its superior soft tissue contrast, high spatiotemporal resolution, and freedom from ionizing radiation exposure. Over the past few years there has been burgeoning interest in MR-guided radiotherapy (MRgRT) to overcome current challenges in X-ray-based IGRT, including but not limited to, suboptimal soft tissue contrast, lack of efficient daily adaptation, and incremental exposure to ionizing radiation. In this review, we present an overview of the technologic advancements in IGRT that led to MRI-linear accelerator (MRL) integration. Our report is organized in three parts: (1) a historical timeline tracing the origins of radiotherapy and evolution of IGRT, (2) currently available MRL technology, and (3) future directions and aspirations for MRL applications.

Reflections on beam configuration optimization for intensity-modulated proton therapy

Presumably, intensity-modulated proton radiotherapy (IMPT) is the most powerful form of proton radiotherapy. In the current state of the art, IMPT beam configurations (i.e. the number of beams and their directions) are, in general, chosen subjectively based on prior experience and practicality. Beam configuration optimization (BCO) for IMPT could, in theory, significantly enhance IMPT’s therapeutic potential. However, BCO is complex and highly computer resource-intensive. Some algorithms for BCO have been developed for intensity-modulated photon therapy (IMRT). They are rarely used clinically mainly because the large number of beams typically employed in IMRT renders BCO essentially unnecessary. Moreover, in the newer form of IMRT, volumetric modulated arc therapy, there are no individual static beams. BCO is of greater importance for IMPT because it typically employs a very small number of beams (2-4) and, when the number of beams is small, BCO is critical for improving plan quality. However, the unique properties and requirements of protons, particularly in IMPT, make BCO challenging. Protons are more sensitive than photons to anatomic changes, exhibit variable relative biological effectiveness along their paths, and, as recently discovered, may spare the immune system. Such factors must be considered in IMPT BCO, though doing so would make BCO more resource intensive and make it more challenging to extend BCO algorithms developed for IMRT to IMPT. A limited amount of research in IMPT BCO has been conducted; however, considerable additional work is needed for its further development to make it truly effective and computationally practical. This article aims to provide a review of existing BCO algorithms, most of which were developed for IMRT, and addresses important requirements specific to BCO for IMPT optimization that necessitate the modification of existing approaches or the development of new effective and efficient ones.

A data-driven approach to optimal beam/arc angle selection for liver stereotactic body radiation therapy treatment planning

BACKGROUND

Stereotactic body radiation therapy (SBRT) for liver cancer has shown promising therapeutic effects. Effective treatment relies not only on the precise delivery provided by image-guided radiation therapy (IGRT) but also high dose gradient formed around the treatment volume to spare functional liver tissue, which is highly dependent on the beam/arc angle selection. In this study, we aim to develop a decision support model to learn human planner's beam navigation approach for beam angle/arc angle selection for liver SBRT.

METHODS

A total of 27 liver SBRT/HIGRT patients (10 IMRT, 17 VMAT/DCA) were included in this study. A dosimetric budget index was defined for each beam angle/control point considering dose penetration through the patient body and liver tissue. Optimal beam angle setting (beam angles for IMRT and start/terminal angle for VMAT/DCA) was determined by minimizing the loss function defined as the sum of total dosimetric budget index and beam span penalty function. Leave-one-out validation was exercised on all 27 cases while weighting coefficients in the loss function was tuned in nested cross validation. To compare the efficacy of the model, a model plan was generated using automatically generated beam setting while retaining the original optimization constraints in the clinical plan. Model plan was normalized to the same planning target volume (PTV) V100% as the clinical plans. Dosimetric endpoints including PTV D98%, D2%, liver V20Gy and total MU were compared between two plan groups. Wilcoxon Signed-Rank test was performed with the null hypothesis being that no difference exists between two plan groups.

RESULTS

Beam setting prediction was instantaneous. Mean PTV D98% was 91.3% and 91.3% (P=0.566), while mean PTV D2% was 107.9% and 108.1% (P=0.164) for clinical plan and model plan respectively. Liver V20Gy showed no significant difference (P=0.590) with 23.3% for clinical plan and 23.4% for the model plan. Total MU is comparable (P=0.256) between the clinical plan (avg. 2,389.6) and model plan (avg. 2,319.6).

CONCLUSIONS

The evidence driven beam setting model yielded similar plan quality as hand-crafted clinical plan. It is capable of capturing human's knowledge in beam selection decision making. This model could facilitate decision making for beam angle selection while eliminating lengthy trial-and-error process of adjusting beam setting during liver SBRT treatment planning.

Temporal lobe microstructural abnormalities in patients with nasopharyngeal carcinoma quantitatively evaluated by high-resolution DWI and DKI after concurrent chemoradiotherapy

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DKI could detect early radiation-induced microstructural abnormalities after CCRT.

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The ADC, Dmean, and FA of temporal lobe showed a unique time-dependent trajectory.

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Kmean might be more sensitive to detection of effects in the late delayed phases.

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White and grey matter all underwent microstructural changes after radiotherapy.

Purpose

To investigate temporal lobe microstructural abnormalities and neurocognitive function impairment after concurrent chemoradiotherapy (CCRT) in patients with nasopharyngeal carcinoma (NPC).

Methods

NPC patients who underwent CCRT were enrolled. High-resolution diffusion-weighted imaging (DWI) magnetic resonance imaging (MRI) and diffusion-kurtosis imaging (DKI) MRI, were performed 5 times per patient (once pre-CCRT, 1 week post-CCRT, 3 months post-CCRT, 6 months post-CCRT, and 12 months post-CCRT). Neurocognitive function was evaluated by Montreal Neurocognitive Assessment (MoCA) twice per patient, once pre-CCRT, and once 12-months after CCRT.

Results

Of 111 patients, 56 completed the entire protocol. The MRI derived apparent diffusion coefficient (ADC), mean of diffusion coefficient (Dmean) and fractional anisotropy (FA) values were significantly decreased (p < 0.05) over the 0–3 month period following CCRT and significantly increased (p < 0.05) over the 3–12 month period following CCRT. The mean of kurtosis coefficient (Kmean) continued to decline over a year post-CCRT. All parameters reveal more pronounced changes in white matter (WM) than in grey matter (GM). MoCA also declined after CCRT (p < 0.001). MoCA showed significant positive correlation with Kmean-WM-6 m, Kmean-WM-12 m and ΔKmean-WM.

Conclusions

High-resolution DWI and DKI should be considered as a promising method for the investigation of temporal lobe microstructural change in NPC patients after CCRT.

Robust beam orientation optimization for intensity-modulated proton therapy

PURPOSE

Dose conformality and robustness are equally important in intensity modulated proton therapy (IMPT). Despite the obvious implication of beam orientation on both dosimetry and robustness, an automated, robust beam orientation optimization algorithm has not been incorporated due to the problem complexity and paramount computational challenge. In this study, we developed a novel IMPT framework that integrates robust beam orientation optimization (BOO) and robust fluence map optimization (FMO) in a unified framework.

METHODS

The unified framework is formulated to include a dose fidelity term, a heterogeneity-weighted group sparsity term, and a sensitivity regularization term. The L2, 1/2-norm group sparsity is used to reduce the number of active beams from the initial 1162 evenly distributed noncoplanar candidate beams, to between two and four. A heterogeneity index, which evaluates the lateral tissue heterogeneity of a beam, is used to weigh the group sparsity term. With this index, beams more resilient to setup uncertainties are encouraged. There is a symbiotic relationship between the heterogeneity index and the sensitivity regularization; the integrated optimization framework further improves beam robustness against both range and setup uncertainties. This Sensitivity regularization and Heterogeneity weighting based BOO and FMO framework (SHBOO-FMO) was tested on two skull-base tumor (SBT) patients and two bilateral head-and-neck (H&N) patients. The conventional CTV-based optimized plans (Conv) with SHBOO-FMO beams (SHBOO-Conv) and manual beams (MAN-Conv) were compared to investigate the beam robustness of the proposed method. The dosimetry and robustness of SHBOO-FMO plan were compared against the manual beam plan with CTV-based voxel-wise worst-case scenario approach (MAN-WC).

RESULTS

With SHBOO-FMO method, the beams with superior range robustness over manual beams were selected while the setup robustness was maintained or improved. On average, the lowest [D95%, V95%, V100%] of CTV were increased from [93.85%, 91.06%, 70.64%] in MAN-Conv plans, to [98.62%, 98.61%, 96.17%] in SHBOO-Conv plans with range uncertainties. With setup uncertainties, the average lowest [D98%, D95%, V95%, V100%] of CTV were increased from [92.06%, 94.83%, 94.31%, 78.93%] in MAN-Conv plans, to [93.54%, 96.61%, 97.01%, 91.98%] in SHBOO-Conv plans. Compared with the MAN-WC plans, the final SHBOO-FMO plans achieved comparable plan robustness and better OAR sparing, with an average reduction of [Dmean, Dmax] of [6.31, 6.55] GyRBE for the SBT cases and [1.89, 5.08] GyRBE for the H&N cases from the MAN-WC plans.

CONCLUSION

We developed a novel method to integrate robust BOO and robust FMO into IMPT optimization for a unified solution of both BOO and FMO, generating plans with superior dosimetry and good robustness.

Evaluation of optimization workflow using design of experiment (DoE) for various field configurations in volumetric-modulated arc therapy

PURPOSE

In volumetric-modulated arc therapy (VMAT), field configurations such as couch or arc angles are defined manually or using a template. A field configuration is reselected through trial-and-error in the case of undesirable resultant planning. To efficiently plan for desirable quality, configurations should be assessed before dose calculation. Design of experiments (DoE) is an optimization technique that efficiently reveals the influence of inputs on outputs. We developed an original tool using DoE to determine the field configuration selection and evaluated the efficacy of this workflow for clinical practice.

METHODS

Computed-tomography scans of 17 patients and target structures were acquired retrospectively from a brain tumor treated using a dual-arc VMAT plan. The configurations of the couch, arc, collimator angles, field sizes, and beam energy were determined using DoE. The resultant dose distributions obtained using the DoE-selected configuration were compared with the clinical plan.

RESULTS

The averaged differences between the DoE and clinical plan for 17 patients of doses to 50% of the planning target volume (PTV-D50%), Brain-D60%, Brain-D30%, Brain stem-D1%, Left eye-D1%, Right eye-D1%, Optic nerve-D1%, and Chiasm-D1% were 0.2 ± 0.5%, -1.0 ± 4.6%, 1.7 ± 3.5%, -2.5 ± 6.7%, -0.2 ± 4.9%, -1.2 ± 3.6%, -2.8 ± 7.3%, and -2.1 ± 5.7%, respectively.

CONCLUSIONS

Our optimization workflow obtained using DoE for various field configurations provided the same or slightly superior plan quality compared with that created by experts. This process is feasible for clinical practice and will efficiently improve treatment quality while removing the influence of the planner's experience.

Integrated beam orientation and scanning-spot optimization in intensity-modulated proton therapy for brain and unilateral head and neck tumors

PURPOSE

Intensity-Modulated Proton Therapy (IMPT) is the state-of-the-art method of delivering proton radiotherapy. Previous research has been mainly focused on optimization of scanning spots with manually selected beam angles. Due to the computational complexity, the potential benefit of simultaneously optimizing beam orientations and spot pattern could not be realized. In this study, we developed a novel integrated beam orientation optimization (BOO) and scanning-spot optimization algorithm for intensity-modulated proton therapy (IMPT).

METHODS

A brain chordoma and three unilateral head-and-neck patients with a maximal target size of 112.49 cm³ were included in this study. A total number of 1162 noncoplanar candidate beams evenly distributed across 4π steradians were included in the optimization. For each candidate beam, the pencil-beam doses of all scanning spots covering the PTV and a margin were calculated. The beam angle selection and spot intensity optimization problem was formulated to include three terms: a dose fidelity term to penalize the deviation of PTV and OAR doses from ideal dose distribution; an L1-norm sparsity term to reduce the number of active spots and improve delivery efficiency; a group sparsity term to control the number of active beams between 2 and 4. For the group sparsity term, convex L2,1-norm and nonconvex L2,1/2-norm were tested. For the dose fidelity term, both quadratic function and linearized equivalent uniform dose (LEUD) cost function were implemented. The optimization problem was solved using the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). The IMPT BOO method was tested on three head-and-neck patients and one skull base chordoma patient. The results were compared with IMPT plans created using column generation selected beams or manually selected beams.

RESULTS

The L2,1-norm plan selected spatially aggregated beams, indicating potential degeneracy using this norm. L2,1/2-norm was able to select spatially separated beams and achieve smaller deviation from the ideal dose. In the L2,1/2-norm plans, the [mean dose, maximum dose] of OAR were reduced by an average of [2.38%, 4.24%] and[2.32%, 3.76%] of the prescription dose for the quadratic and LEUD cost function, respectively, compared with the IMPT plan using manual beam selection while maintaining the same PTV coverage. The L2,1/2 group sparsity plans were dosimetrically superior to the column generation plans as well. Besides beam orientation selection, spot sparsification was observed. Generally, with the quadratic cost function, 30%~60% spots in the selected beams remained active. With the LEUD cost function, the percentages of active spots were in the range of 35%~85%.The BOO-IMPT run time was approximately 20 min.

CONCLUSION

This work shows the first IMPT approach integrating noncoplanar BOO and scanning-spot optimization in a single mathematical framework. This method is computationally efficient, dosimetrically superior and produces delivery-friendly IMPT plans.

Investigating multi-objective fluence and beam orientation IMRT optimization

Radiation Oncology treatment planning requires compromises to be made between clinical objectives that are invariably in conflict. It would be beneficial to have a 'bird's-eye-view' perspective of the full spectrum of treatment plans that represent the possible trade-offs between delivering the intended dose to the planning target volume (PTV) while optimally sparing the organs-at-risk (OARs). In this work, the authors demonstrate Pareto-aware radiotherapy evolutionary treatment optimization (PARETO), a multi-objective tool featuring such bird's-eye-view functionality, which optimizes fluence patterns and beam angles for intensity-modulated radiation therapy (IMRT) treatment planning. The problem of IMRT treatment plan optimization is managed as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. To achieve this, PARETO is built around a powerful multi-objective evolutionary algorithm, called Ferret, which simultaneously optimizes multiple fitness functions that encode the attributes of the desired dose distribution for the PTV and OARs. The graphical interfaces within PARETO provide useful information such as: the convergence behavior during optimization, trade-off plots between the competing objectives, and a graphical representation of the optimal solution database allowing for the rapid exploration of treatment plan quality through the evaluation of dose-volume histograms and isodose distributions. PARETO was evaluated for two relatively complex clinical cases, a paranasal sinus and a pancreas case. The end result of each PARETO run was a database of optimal (non-dominated) treatment plans that demonstrated trade-offs between the OAR and PTV fitness functions, which were all equally good in the Pareto-optimal sense (where no one objective can be improved without worsening at least one other). Ferret was able to produce high quality solutions even though a large number of parameters, such as beam fluence and beam angles, were included in the optimization.

Pulmonary injury associated with radiation therapy - Assessment, complications and therapeutic targets

Pulmonary injury is more common in patients undergoing radiation therapy for lungs and other thoracic malignancies. Recently with the use of most-advanced technologies powerful doses of radiation can be delivered directly to tumor site with exquisite precision. The awareness of technical and clinical parameters that influence the chance of radiation induced lung injury is important to guide patient selection and toxicity minimization strategies. At the cellular level, radiation activates free radical production, leading to DNA damage, apoptosis, cell cycle changes, and reduced cell survival. Preclinical research shows the potential for therapies targeting transforming growth factor-β (TGF-B), Toll like receptor (TLRs), Tumour necrosis factor-alpha (TNF-alpha), Interferon gamma (IFN-γ) and so on that may restore lung function. At present Amifostine (WR-2721) is the only approved broad spectrum radioprotector in use for patients undergoing radiation therapy. Newer techniques also offer the opportunity to identify new biomarkers and new targets for interventions to prevent or ameliorate these late effects of lung damage.

Intelligence-guided beam angle optimization in treatment planning of intensity-modulated radiation therapy

An intelligence guided approach based on fuzzy inference system (FIS) was proposed to automate beam angle optimization in treatment planning of intensity-modulated radiation therapy (IMRT). The model of FIS is built on inference rules in describing the relationship between dose quality of IMRT plan and irradiated region of anatomical structure. Dose quality of IMRT plan is quantified by the difference between calculated and constraint doses of the anatomical structures in an IMRT plan. Irradiated region of anatomical structure is characterized by the metric, covered region of interest, which is the region of an anatomical structure under radiation field while beam's eye-view is conform to target volume. Initially, an IMRT plan is created with a single beam. The dose difference is calculated for the input of FIS and the output of FIS is obtained with processing of fuzzy inference. Later, a set of candidate beams is generated for replacing the current beam. This process continues until no candidate beams is found. Then the next beam is added to the IMRT plan and optimized in the same way as the previous beam. The new beam keeps adding to the IMRT plan until the allowed beam number is reached. Two spinal cases were investigated in this study. The preliminary results show that dose quality of IMRT plans achieved by this approach is better than those achieved by the default approach with equally spaced beam setting. It is effective to find the optimal beam combination of IMRT plan with the intelligence-guided approach.

A hybrid IMRT/VMAT technique for the treatment of nasopharyngeal cancer

Hybrid IMRT/VMAT technique which combined intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) was developed for the treatment of nasopharyngeal cancer (NPC). Two-full-arc VMAT (2ARC-VMAT), 9-field IMRT (9F-IMRT), and Hybrid IMRT/VMAT plans for NPC were compared in terms of the dosimetric quality, sparing of organs at risk (OARs), and delivery efficiency. The Hybrid IMRT/VMAT technique can improve the target dose homogeneity and conformity compared with 9F-IMRT and 2ARC-VMAT. It can reduce the dose delivered to the TMJ, mandible, temporal lobe, and unspecified tissue with fewer MUs compared with 9F-IMRT and dose delivered to parotids, brainstem, and spinal cord compared with 2ARC-VMAT technique. The mean delivery time of Hybrid plans was shorter than that of 9F-IMRT plans (408 s versus 812 s; P = 0.00) and longer than that of 2ARC-VMAT plans (408 s versus 179 s; P = 0.00). Hybrid IMRT/VMAT technique could be a viable radiotherapy technique with better plan quality.

Shared data for intensity modulated radiation therapy (IMRT) optimization research: the CORT dataset

BACKGROUND

We provide common datasets (which we call the CORT dataset: common optimization for radiation therapy) that researchers can use when developing and contrasting radiation treatment planning optimization algorithms. The datasets allow researchers to make one-to-one comparisons of algorithms in order to solve various instances of the radiation therapy treatment planning problem in intensity modulated radiation therapy (IMRT), including beam angle optimization, volumetric modulated arc therapy and direct aperture optimization.

RESULTS

We provide datasets for a prostate case, a liver case, a head and neck case, and a standard IMRT phantom. We provide the dose-influence matrix from a variety of beam/couch angle pairs for each dataset. The dose-influence matrix is the main entity needed to perform optimizations: it contains the dose to each patient voxel from each pencil beam. In addition, the original Digital Imaging and Communications in Medicine (DICOM) computed tomography (CT) scan, as well as the DICOM structure file, are provided for each case.

CONCLUSIONS

Here we present an open dataset - the first of its kind - to the radiation oncology community, which will allow researchers to compare methods for optimizing radiation dose delivery.

A surrogate-based metaheuristic global search method for beam angle selection in radiation treatment planning

An important element of radiation treatment planning for cancer therapy is the selection of beam angles (out of all possible coplanar and non-coplanar angles in relation to the patient) in order to maximize the delivery of radiation to the tumor site and minimize radiation damage to nearby organs-at-risk. This category of combinatorial optimization problem is particularly difficult because direct evaluation of the quality of treatment corresponding to any proposed selection of beams requires the solution of a large-scale dose optimization problem involving many thousands of variables that represent doses delivered to volume elements (voxels) in the patient. However, if the quality of angle sets can be accurately estimated without expensive computation, a large number of angle sets can be considered, increasing the likelihood of identifying a very high quality set. Using a computationally efficient surrogate beam set evaluation procedure based on single-beam data extracted from plans employing equallyspaced beams (eplans), we have developed a global search metaheuristic process based on the nested partitions framework for this combinatorial optimization problem. The surrogate scoring mechanism allows us to assess thousands of beam set samples within a clinically acceptable time frame. Tests on difficult clinical cases demonstrate that the beam sets obtained via our method are of superior quality.

Volumetric-modulated arc therapy for the treatment of a large planning target volume in thoracic esophageal cancer

Recently, volumetric‐modulated arc therapy (VMAT) has demonstrated the ability to deliver radiation dose precisely and accurately with a shorter delivery time compared to conventional intensity‐modulated fixed‐field treatment (IMRT). We applied the hypothesis of VMAT technique for the treatment of thoracic esophageal carcinoma to determine superior or equivalent conformal dose coverage for a large thoracic esophageal planning target volume (PTV) with superior or equivalent sparing of organs‐at‐risk (OARs) doses, and reduce delivery time and monitor units (MUs), in comparison with conventional fixed‐field IMRT plans. We also analyzed and compared some other important metrics of treatment planning and treatment delivery for both IMRT and VMAT techniques. These metrics include: 1) the integral dose and the volume receiving intermediate dose levels between IMRT and VMATI plans; 2) the use of 4D CT to determine the internal motion margin; and 3) evaluating the dosimetry of every plan through patient‐specific QA. These factors may impact the overall treatment plan quality and outcomes from the individual planning technique used. In this study, we also examined the significance of using two arcs vs. a single‐arc VMAT technique for PTV coverage, OARs doses, monitor units and delivery time. Thirteen patients, stage T2‐T3 N0‐N1 (TNM AJCC 7th edn.), PTV volume median 395 cc (range 281–601 cc), median age 69 years (range 53 to 85), were treated from July 2010 to June 2011 with a four‐field (n=4) or five‐field (n=9) step‐and‐shoot IMRT technique using a 6 MV beam to a prescribed dose of 50 Gy in 20 to 25 F. These patients were retrospectively replanned using single arc (VMATI, 91 control points) and two arcs (VMATII, 182 control points). All treatment plans of the 13 study cases were evaluated using various dose‐volume metrics. These included PTV D99, PTV D95, PTV V9547.5Gy(95%), PTV mean dose, Dmax, PTV dose conformity (Van't Riet conformation number (CN)), mean lung dose, lung V20 and V5, liver V30, and Dmax to the spinal canal prv3mm. Also examined were the total plan monitor units (MUs) and the beam delivery time. Equivalent target coverage was observed with both VMAT single and two‐arc plans. The comparison of VMATI with fixed‐field IMRT demonstrated equivalent target coverage; statistically no significant difference were found in PTV D99 (p=0.47), PTV mean (p=0.12), PTV D95 and PTV V9547.5Gy (95%) (p=0.38). However, Dmax in VMATI plans was significantly lower compared to IMRT (p=0.02). The Van't Riet dose conformation number (CN) was also statistically in favor of VMATI plans (p=0.04). VMATI achieved lower lung V20 (p=0.05), whereas lung V5 (p=0.35) and mean lung dose (p=0.62) were not significantly different. The other OARs, including spinal canal, liver, heart, and kidneys showed no statistically significant differences between the two techniques. Treatment time delivery for VMATI plans was reduced by up to 55% (p=5.8E−10) and MUs reduced by up to 16% (p=0.001). Integral dose was not statistically different between the two planning techniques (p=0.99). There were no statistically significant differences found in dose distribution of the two VMAT techniques (VMATI vs. VMATII) Dose statistics for both VMAT techniques were: PTV D99 (p=0.76), PTV D95 (p=0.95), mean PTV dose (p=0.78), conformation number (CN) (p=0.26), and MUs (p=0.1). However, the treatment delivery time for VMATII increased significantly by two‐fold (p=3.0E−11) compared to VMATI. VMAT‐based treatment planning is safe and deliverable for patients with thoracic esophageal cancer with similar planning goals, when compared to standard IMRT. The key benefit for VMATI was the reduction in treatment delivery time and MUs, and improvement in dose conformality. In our study, we found no significant difference in VMATII over single‐arc VMATI for PTV coverage or OARs doses. However, we observed significant increase in delivery time for VMATII compared to VMATI.

PACS number: 87.53.Kn, 87.55.‐x

4π non-coplanar liver SBRT: a novel delivery technique

PURPOSE

To improve the quality of liver stereotactic body radiation therapy (SBRT) treatments, a novel 4π framework was developed with accompanying algorithms to optimize non-coplanar beam orientations and fluences. The dose optimization is performed on a patient-specific deliverable beam geometry solution space, parameterized with patient and linear accelerator gantry orientations.

METHODS AND MATERIALS

Beams causing collision between the gantry and the couch or patient were eliminated by simulating all beam orientations using a precise computer assisted design model of the linear accelerator and a human subject. Integrated beam orientation and fluence map optimizations were performed on remaining beams using a greedy column generation method. Testing of the new method was performed on 10 liver SBRT cases previously treated with 50 to 60 Gy in 5 fractions using volumetric modulated arc therapy (VMAT). For each patient, both 14 and 22 non-coplanar fields were selected and optimized to meet the objective of ≥95% of the planning target volume (PTV) covered by 100% of the prescription dose. Doses to organs at risk, normal liver volumes receiving <15 Gy, integral dose, and 50% dose spillage volumes were compared against the delivered clinical VMAT plans.

RESULTS

Compared with the VMAT plans, the 4π plans yielded reduced 50% dose spillage volume and integral dose by 22% (range 10%-40%) and 19% (range 13%-26%), respectively. The mean normal liver volume receiving <15 Gy was increased by 51 cc (range 21-107 cc) with a 31% reduction of the mean normal liver dose. Mean doses to the left kidney and right kidney and maximum doses to the stomach and spinal cord were on average reduced by 70%, 51%, 67%, and 64% (P≤.05).

CONCLUSIONS

This novel 4π non-coplanar radiation delivery technique significantly improved dose gradient, reduced high dose spillage, and improved organ at risk sparing compared with state of the art VMAT plans.

Uncertainty incorporated beam angle optimization for IMPT treatment planning

PURPOSE

Beam angle optimization (BAO) by far remains an important and challenging problem in external beam radiation therapy treatment planning. Conventional BAO algorithms discussed in previous studies all focused on photon-based therapies. Impact of BAO on proton therapy is important while proton therapy increasingly receives great interests. This study focuses on potential benefits of BAO on intensity-modulated proton therapy (IMPT) that recently began available to clinical cancer treatment.

METHODS

The authors have developed a novel uncertainty incorporated BAO algorithm for IMPT treatment planning in that IMPT plan quality is highly sensitive to uncertainties such as proton range and setup errors. A linear programming was used to optimize robust intensity maps to scenario-based uncertainties for an incident beam angle configuration. Unlike conventional intensity-modulated radiation therapy with photons (IMXT), the search space for IMPT treatment beam angles may be relatively small but optimizing an IMPT plan may require higher computational costs due to larger data size. Therefore, a deterministic local neighborhood search algorithm that only needs a very limited number of plan objective evaluations was used to optimize beam angles in IMPT treatment planning.

RESULTS

Three prostate cancer cases and two skull base chordoma cases were studied to demonstrate the dosimetric advantages and robustness of optimized beam angles from the proposed BAO algorithm. Two- to four-beam plans were optimized for prostate cases, and two- and three-beam plans were optimized for skull base cases. By comparing plans with conventional two parallel-opposed angles, all plans with optimized angles consistently improved sparing at organs at risks, i.e., rectum and femoral heads for prostate, brainstem for skull base, in either nominal dose distribution or uncertainty-based dose distributions. The efficiency of the BAO algorithm was demonstrated by comparing it with alternative methods including simulated annealing and genetic algorithm. The numbers of IMPT plan objective evaluations required were reduced by up to a factor of 5 while the same optimal angle plans were converged in selected comparisons.

CONCLUSIONS

Uncertainty incorporated BAO may introduce pronounced improvement of IMPT plan quality including dosimetric benefits and robustness over uncertainties, based on the five clinical studies in this paper. In addition, local search algorithms may be more efficient in finding optimal beam angles than global optimization approaches for IMPT BAO.

Unsupervised clustering of multivariate circular data

In this paper, we study an unsupervised clustering problem. The originality of this problem lies in the data, which consist of the positions of five separate X-ray beams on a circle. Radiation therapists positioned the five X-ray beam 'projectors' around each patient on a predefined circle. However, similarities exist in positioning for certain groups of patients, and we aim to describe these similarities with the goal of creating pre-adjustment settings that could help save time during X-ray positioning. We therefore performed unsupervised clustering of observed X-ray positions. Because the data for each patient consist of five angle measurements, Euclidean distances are not appropriated. Furthermore, we cannot perform k-means algorithm, usually used for minimizing corresponding distortion because we cannot calculate centers of clusters. We present here solutions to these problems. First, we define a suitable distance on the circle. Then, we adapt an algorithm based on simulated annealing to minimize distortion. This algorithm is shown to be theoretically convergent. Finally, we present simulations on simulated and real data.

A set cover approach to fast beam orientation optimization in intensity modulated radiation therapy for total marrow irradiation

The beam orientation optimization (BOO) problem in intensity modulated radiation therapy (IMRT) treatment planning is a nonlinear problem, and existing methods to obtain solutions to the BOO problem are time consuming due to the complex nature of the objective function and size of the solution space. These issues become even more difficult in total marrow irradiation (TMI), where many more beams must be used to cover a vastly larger treatment area than typical site-specific treatments (e.g., head-and-neck, prostate, etc). These complications result in excessively long computation times to develop IMRT treatment plans for TMI, so we attempt to develop methods that drastically reduce treatment planning time. We transform the BOO problem into the classical set cover problem (SCP) and use existing methods to solve SCP to obtain beam solutions. Although SCP is NP-Hard, our methods obtain beam solutions that result in quality treatments in minutes. We compare our approach to an integer programming solver for the SCP to illustrate the speed advantage of our approach.

A methodology for automatic intensity-modulated radiation treatment planning for lung cancer

In intensity-modulated radiotherapy (IMRT), the quality of the treatment plan, which is highly dependent upon the treatment planner's level of experience, greatly affects the potential benefits of the radiotherapy (RT). Furthermore, the planning process is complicated and requires a great deal of iteration, and is often the most time-consuming aspect of the RT process. In this paper, we describe a methodology to automate the IMRT planning process in lung cancer cases, the goal being to improve the quality and consistency of treatment planning. This methodology (1) automatically sets beam angles based on a beam angle automation algorithm, (2) judiciously designs the planning structures, which were shown to be effective for all the lung cancer cases we studied, and (3) automatically adjusts the objectives of the objective function based on a parameter automation algorithm. We compared treatment plans created in this system (mdaccAutoPlan) based on the overall methodology with plans from a clinical trial of IMRT for lung cancer run at our institution. The 'autoplans' were consistently better, or no worse, than the plans produced by experienced medical dosimetrists in terms of tumor coverage and normal tissue sparing. We conclude that the mdaccAutoPlan system can potentially improve the quality and consistency of treatment planning for lung cancer.

New developments in arc radiation therapy: a review

Arc therapies have gained widespread clinical interest in radiation oncology over the past decade. Arc therapies have several potential advantages over standard techniques such as intensity-modulated radiation therapy, with implications for patients, administrators, and oncologists. This review focuses on the rationale for arc therapy, descriptions of the modern arc techniques that are currently clinically available, and highlights some distinguishing features of arc therapies, such as dose distributions, treatment times, and imaging capabilities. Arc therapies are exciting examples of progress in radiotherapy through technological innovation, aimed at ultimately improving the therapeutic ratio for patients receiving radiation.

A methodology for selecting the beam arrangement to reduce the intensity-modulated radiation therapy (IMRT) dose to the SPECT-defined functioning lung

Macroaggregated albumin single-photon emission computed tomography (MAA-SPECT) provides a map of the spatial distribution of lung perfusion. Our previous work developed a methodology to use SPECT guidance to reduce the dose to the functional lung in IMRT planning. This study aims to investigate the role of beam arrangement on both low and high doses in the functional lung. In our previous work, nine-beam IMRT plans were generated with and without SPECT guidance and compared for five patients. For the current study, the dose-function histogram (DFH) contribution for each of the nine beams for each patient was calculated. Four beams were chosen based on orientation and DFH contributions to create a SPECT-guided plan that spared the functional lung and maintained target coverage. Four-beam SPECT-guided IMRT plans reduced the F(20) and F(30) values by (16.5 +/- 6.8)% and (6.1 +/- 9.2)%, respectively, when compared to nine-beam conventional IMRT plans. Moreover, the SPECT-4F Plan reduces F(5) and F(13) for all patients by (11.0 +/- 8.2)% and (6.1 +/- 3.6)%, respectively, compared to the SPECT Plan. Using fewer beams in IMRT planning may reduce the amount of functional lung that receives 5 and 13 Gy, a factor that has recently been associated with radiation pneumonitis.

An approaching genetic algorithm for automatic beam angle selection in IMRT planning

A method named approaching genetic algorithm (AGA) is introduced to automatically select the beam angles for intensity-modulated radiotherapy (IMRT) planning. In AGA, the best individual of the current population is found at first, and the rest of the normal individuals approach the current best one according to some specially designed rules. In the course of approaching, some better individuals may be obtained. Then, the current best individual is updated to try to approach the real best one. The approaching and updating operations of AGA replace the selection, crossover and mutation operations of the genetic algorithm (GA) completely. Using the specially designed updating strategies, AGA can recover the varieties of the population to a certain extent and retain the powerful ability of evolution, compared to GA. The beam angles are selected using AGA, followed by a beam intensity map optimization using conjugate gradient (CG). A simulated case and a clinical case with nasopharynx cancer are employed to demonstrate the feasibility of AGA. For the case investigated, AGA was feasible for the beam angle optimization (BAO) problem in IMRT planning and converged faster than GA.

Optimal starting gantry angles using equiangular-spaced beams with intensity modulated radiation therapy for prostate cancer on RTOG 0126: A clinical study of 5 and 7 fields

BACKGROUND AND PURPOSE

To investigate the effects of starting gantry angle and number of equiangular-spaced beams for prostate cancer radiotherapy on the Radiation Therapy Oncology Group (RTOG) 0126 protocol using intensity-modulated radiation therapy (IMRT).

MATERIALS AND METHODS

Ten localized prostate cancer patients were prescribed to 79.2Gy in 44 fractions. Static IMRT plans using five and seven equiangular-spaced beams were generated. The starting gantry angles were incremented by 5 degrees resulting in 15 (5 beams) and 11 (7 beams) plans per patient. Constant target coverage was ensured for all plans in order to isolate the variation in the rectal and bladder metrics as a function of starting gantry angle.

RESULTS

The variation with starting gantry angle in rectal metrics using 5 beams was statistically significant (p<0.001) with dosimetric importance. The 5-beam rectal V 75Gy and V 70Gy demonstrated a class solution with a characteristic 'W' pattern and two optimal starting gantry angles near 20 degrees and 50 degrees . Statistically insignificant differences were observed for the bladder metrics using 5 beams. There was little dosimetric variation in the rectal and bladder metrics with 7 beams. Nearly equivalent rectal V 75Gy was achieved between 5 optimal equiangular-spaced beams starting at 20 degrees (class solution) and 7 equiangular-spaced beams starting at 0 degrees for most patients.

CONCLUSIONS

The use of an optimal starting gantry angle for 5 equiangular-spaced beams, as indicated by a class solution in this study, will facilitate rectal sparing and can produce plans that are equivalent to those employing 7 equiangular-spaced beams.

Local beam angle optimization with linear programming and gradient search

The optimization of beam angles in IMRT planning is still an open problem, with literature focusing on heuristic strategies and exhaustive searches on discrete angle grids. We show how a beam angle set can be locally refined in a continuous manner using gradient-based optimization in the beam angle space. The gradient is derived using linear programming duality theory. Applying this local search to 100 random initial angle sets of a phantom pancreatic case demonstrates the method, and highlights the many-local-minima aspect of the BAO problem. Due to this function structure, we recommend a search strategy of a thorough global search followed by local refinement at promising beam angle sets. Extensions to nonlinear IMRT formulations are discussed.

Measured peripheral dose in pediatric radiation therapy: A comparison of intensity-modulated and conformal techniques

BACKGROUND AND PURPOSE

The interest in IMRT for the treatment of pediatric malignancies has raised concern about possible increased total body dose. This study examines the pediatric peripheral dose resulting from IMRT compared to 3D conformal therapy.

METHODS AND MATERIALS

Five brain or base of skull pediatric cases were planned with both IMRT and 3D conformal techniques. A pediatric-sized anthropomorphic phantom was created and ion chambers were placed at interest points approximating the position of the thyroid, breast, ovary and testes. Measured peripheral doses at the interest points were compared for both IMRT and 3D conformal techniques for the 5 cases.

RESULTS

While tumor coverage was similar for both techniques, the IMRT delivery resulted in lower peripheral doses at points near the target (thyroid) presumably due to reduced internal scatter from a smaller effective field size for sliding window dynamic multi-leaf collimation. The IMRT delivery resulted in higher doses to the more distant points, presumably due to the higher monitor units and resulting increased head leakage. Since the magnitude of dose at the distant points was much smaller than that of the thyroid point, the overall absolute peripheral dose was similar for both techniques.

CONCLUSIONS

Peripheral dose is difficult to predict by monitor units alone. In this study, interest points closer to the beam received less dose with IMRT. This difference may result from the competing factors of reduced internal scatter from dynamic multileaf collimation IMRT and reduced head leakage for 3D conformal therapy.

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.

Resampling: An optimization method for inverse planning in robotic radiosurgery

By design, the range of beam directions in conventional radiosurgery are constrained to an isocentric array. However, the recent introduction of robotic radiosurgery dramatically increases the flexibility of targeting, and as a consequence, beams need be neither coplanar nor isocentric. Such a nonisocentric design permits a large number of distinct beam directions to be used in one single treatment. These major technical differences provide an opportunity to improve upon the well-established principles for treatment planning used with GammaKnife or LINAC radiosurgery. With this objective in mind, our group has developed over the past decade an inverse planning tool for robotic radiosurgery. This system first computes a set of beam directions, and then during an optimization step, weights each individual beam. Optimization begins with a feasibility query, the answer to which is derived through linear programming. This approach offers the advantage of completeness and avoids local optima. Final beam selection is based on heuristics. In this report we present and evaluate a new strategy for utilizing the advantages of linear programming to improve beam selection. Starting from an initial solution, a heuristically determined set of beams is added to the optimization problem, while beams with zero weight are removed. This process is repeated to sample a set of beams much larger compared with typical optimization. Experimental results indicate that the planning approach efficiently finds acceptable plans and that resampling can further improve its efficiency.

A sensitivity-guided algorithm for automated determination of IMRT objective function parameters

Optimizing intensity-modulated radiotherapy (IMRT) plans involves tradeoffs that balance normal-tissue objectives against each other and against tumor objectives. Adjusting the parameters that determine the appropriate contributions of individual anatomic structures to the objective functions through trial and error is time consuming and may not produce the best achievable plans. We have developed a sensitivity-guided parameter optimization (SGPO) method to assist in the automatic determination of parameters to drive the IMRT optimization to better achieve, or even exceed, specified planning goals. The method is based on the trade-off relationships among multiple objectives: In a globally optimal plan (or within a convex subspace of the plan objectives), any attempt to improve the achievement of goals for a structure will result in sacrificing the goals for at least one other structure. However, different objectives may have different sensitivities to the overall goal of an IMRT plan. For instance, changes in dose distribution, hence the subscore corresponding to an objective for a given normal structure, may minimally impact the target dose distribution. Stated differently, the target coverage is insensitive to the changes in dose distribution of the specific normal structure. A lung cancer treatment plan designed with the SGPO method was used to demonstrate that IMRT plans could be designed to favor a structure with the highest target sensitivity and spare the structures with the least target sensitivity without compromising the target coverage. Using one case each of prostate and paranasal sinus cancers, we also demonstrated that several alternative optimal solutions could be designed with the SGPO algorithm favoring different structures. Finally, we applied the method to eight oropharyngeal cancer cases to obtain objective function parameters that satisfied the Radiation Therapy Oncology Group RTOG-H-0022 protocol. The eight plans optimized using the computer-generated objective function parameters met the protocol's scoring criteria with no or only minor protocol violations. Our preliminary study indicates that the SGPO method may be an effective and practical way to improve IMRT planning.

A theoretical approach to the problem of dose-volume constraint estimation and their impact on the dose-volume histogram selection

This paper outlines a theoretical approach to the problem of estimating and choosing dose-volume constraints. Following this approach, a method of choosing dose-volume constraints based on biological criteria is proposed. This method is called "reverse normal tissue complication probability (NTCP) mapping into dose-volume space" and may be used as a general guidance to the problem of dose-volume constraint estimation. Dose-volume histograms (DVHs) are randomly simulated, and those resulting in clinically acceptable levels of complication, such as NTCP of 5 +/- 0.5%, are selected and averaged producing a mean DVH that is proven to result in the same level of NTCP. The points from the averaged DVH are proposed to serve as physical dose-volume constraints. The population-based critical volume and Lyman NTCP models with parameter sets taken from literature sources were used for the NTCP estimation. The impact of the prescribed value of the maximum dose to the organ, D(max), on the averaged DVH and the dose-volume constraint points is investigated. Constraint points for 16 organs are calculated. The impact of the number of constraints to be fulfilled based on the likelihood that a DVH satisfying them will result in an acceptable NTCP is also investigated. It is theoretically proven that the radiation treatment optimization based on physical objective functions can sufficiently well restrict the dose to the organs at risk, resulting in sufficiently low NTCP values through the employment of several appropriate dose-volume constraints. At the same time, the pure physical approach to optimization is self-restrictive due to the preassignment of acceptable NTCP levels thus excluding possible better solutions to the problem.

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.

Beam angle optimization and reduction for intensity-modulated radiation therapy of non–small-cell lung cancers

PURPOSE

To optimize beam angles and reduce the number of beams used for intensity-modulated radiation therapy (IMRT) of non-small-cell lung cancer (NSCLC).

METHODS AND MATERIALS

An exhaustive search scheme was used to perform beam angle optimization (BAO) for IMRT of NSCLC. This approach involved intercomparison of all possible beam angle combinations and selection of the best angles based on the scores or costs of the objective functions used in the treatment plan optimization. Ten Stage III NSCLC cases were selected to evaluate the BAO algorithm and dosimetry benefits of IMRT-BAO. IMRT plans using five or seven coplanar beams were optimized and compared with those using nine equal-spaced beams. Results of BAO were also compared between plans using different numbers of beams with or without fluence modulation.

RESULTS

Each anatomic structure, e.g., tumor or lung, had its own preferred beam angles. Thus, BAO required appropriate balance of competing objective functions. Plans using fewer angles (five or seven beams) could achieve plan quality similar to those using nine equal-spaced beams, however with reduced monitor units and field segments. The number of beams used for the treatment (five vs. seven) and the fluence modulation (open or IMRT beams) did not have a significant impact on the results of the BAO.

CONCLUSIONS

Use of fewer beams (e.g., five) for lung IMRT could result in acceptable plan quality but improved treatment efficiency. A multiresolution search scheme could be developed for BAO using fewer and nonmodulated beams to reduce the computation cost of BAO.

Effectiveness of noncoplanar IMRT planning using a parallelized multiresolution beam angle optimization method for paranasal sinus carcinoma

PURPOSE

To determine the effectiveness of noncoplanar beam configurations and the benefit of plans using fewer but optimally placed beams designed by a parallelized multiple-resolution beam angle optimization (PMBAO) approach.

METHODS AND MATERIALS

The PMBAO approach uses a combination of coplanar and noncoplanar beam configurations for intensity-modulated radiation therapy (IMRT) treatment planning of paranasal sinus cancers. A smaller number of beams (e.g. 3) are first used to explore the solution space to determine the best and worst beam directions. The results of this exploration are then used as a starting point for determining an optimum beam orientation configuration with more beams (e.g. 5). This process is parallelized using a message passing interface, which greatly reduces the overall computation time for routine clinical practice. To test this approach, treatment for 10 patients with paranasal sinus cancer was planned using a total of 5 beams from a pool of 46 possible beam angles. The PMBAO treatment plans were also compared with IMRT plans designed using 9 equally spaced coplanar beams, which is the standard approach in our clinic. Plans with these two different beam configurations were compared with respect to dose conformity, dose heterogeneity, dose-volume histograms, and doses to organs at risk (i.e., eyes, optic nerve, optic chiasm, and brain).

RESULTS

The noncoplanar beam configuration was superior in most paranasal sinus carcinoma cases. The target dose homogeneity was better using a PMBAO 5-beam configuration. However, the dose conformity using PMBAO was not improved and was case dependent. Compared with the 9-beam configuration, the PMBAO configuration significantly reduced the mean dose to the eyes and optic nerves and the maximum dose to the contralateral optical path (e.g. the contralateral eye and optic nerve). The maximum dose to the ipsilateral eye and optic nerve was also lower using the PMBAO configuration than using the 9-beam configuration, although this difference was not significant. The mean doses to the optic chiasm and brain are marginally lower using the PMBAO configuration than using 9-beam configuration. The maximum doses to the optic chiasm and brain are the same with the PMBAO configuration and the 9-beam configuration.

CONCLUSION

Parallelized multiple-resolution beam angle optimization with an optimized noncoplanar beam configuration is an effective and practical approach for IMRT treatment planning. Five-beam treatment plans optimized using the PMBAO are at least equivalent to, and overall better than, the plans using 9 equally spaced coplanar beams.

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.