A generalized inverse planning tool for volumetric-modulated arc therapy

Particle Swarm Optimisation Applied to the Direct Aperture Optimisation Problem in Radiation Therapy

Intensity modulated radiation therapy (IMRT) is one of the most used techniques for cancer treatment. Using a linear accelerator, it delivers radiation directly at the cancerogenic cells in the tumour, reducing the impact of the radiation on the organs surrounding the tumour. The complexity of the IMRT problem forces researchers to subdivide it into three sub-problems that are addressed sequentially. Using this sequential approach, we first need to find a beam angle configuration that will be the set of irradiation points (beam angles) over which the tumour radiation is delivered. This first problem is called the Beam Angle Optimisation (BAO) problem. Then, we must optimise the radiation intensity delivered from each angle to the tumour. This second problem is called the Fluence Map Optimisation (FMO) problem. Finally, we need to generate a set of apertures for each beam angle, making the intensities computed in the previous step deliverable. This third problem is called the Sequencing problem. Solving these three sub-problems sequentially allows clinicians to obtain a treatment plan that can be delivered from a physical point of view. However, the obtained treatment plans generally have too many apertures, resulting in long delivery times. One strategy to avoid this problem is the Direct Aperture Optimisation (DAO) problem. In the DAO problem, the idea is to merge the FMO and the Sequencing problem. Hence, optimising the radiation's intensities considers the physical constraints of the delivery process. The DAO problem is usually modelled as a Mixed-Integer optimisation problem and aims to determine the aperture shapes and their corresponding radiation intensities, considering the physical constraints imposed by the Multi-Leaf Collimator device. In solving the DAO problem, generating clinically acceptable treatments without additional sequencing steps to deliver to the patients is possible. In this work, we propose to solve the DAO problem using the well-known Particle Swarm Optimisation (PSO) algorithm. Our approach integrates the use of mathematical programming to optimise the intensities and utilizes PSO to optimise the aperture shapes. Additionally, we introduce a reparation heuristic to enhance aperture shapes with minimal impact on the treatment plan. We apply our proposed algorithm to prostate cancer cases and compare our results with those obtained in the sequential approach. Results show that the PSO obtains competitive results compared to the sequential approach, receiving less radiation time (beam on time) and using the available apertures with major efficiency.

Advances in Automated Treatment Planning

Treatment planning in radiation therapy has progressed enormously over the past several decades. Such advancements came in the form of innovative hardware and algorithms, giving rise to modalities such as intensity-modulated radiation therapy and volume modulated arc therapy, greatly improving patient outcome and quality of life. While these developments have improved the overall plan quality, they have also given rise to higher treatment planning complexity. This has resulted in increased treatment planning time and higher variability in the final approved plan quality. Radiation oncology, as an already technologically advanced field, has much research and implementation involving the use of AI. The field has begun to show the efficacy of using such technologies in many of its sub-areas, such as in diagnosis, imaging, segmentation, treatment planning, quality assurance, treatment delivery, and follow-up. Some AI technologies have already been clinically implemented by commercial systems. In this article, we will provide an overview to methods involved with treatment planning in radiation therapy. In particular, we will review the recent research and literature related to automation of the treatment planning process, leading to potentially higher efficiency and higher quality plans. We will then present the current and future challenges, as well as some future perspectives.

Convolutional neural network and transfer learning for dose volume histogram prediction for prostate cancer radiotherapy

To adopt a transfer learning approach and establish a convolutional neural network (CNN) model for the prediction of rectum and bladder dose-volume histograms (DVH) in prostate patients treated with a VMAT technique. One hundred forty-four VMAT patients with intermediate or high-risk prostate cancer were included in this study. Data were split into two sets: 120 and 24 patients, respectively. The second set was used for final validation. To ensure the accuracy of the training data, we developed a ground-truth analysis for detecting and correcting for all potential outliers. We used transfer learning in combination with a pre-trained VGG-16 network. We dropped the fully connected layers from the VGG-16 and added a new fully connected neural network. The inputs for the CNN were a 2D image of the volumes contoured in the CT, but we only retained the geometrical information of every CT-slice. The outputs were the corresponding rectum and bladder DVH for every slice. We used a confusion matrix to analyze the performance of our model. Our model achieved 100% and 81% of true positive and true negative predictions, respectively. We have an overall accuracy of 87.5%, a misclassification rate of 12.5%, and a precision of 100%. We have successfully developed a model for reliable prediction of rectum and bladder DVH in prostate patients by applying a previously pre-trained CNN. To our knowledge, this is the first attempt to apply transfer learning to the prediction of DVHs that accounts for the ground truth problem.

Solving the volumetric modulated arc therapy (VMAT) problem using a sequential convex programming method

The volumetric modulated arc therapy (VMAT) problem is highly non-convex and much more difficult than the fixed-field intensity modulated radiotherapy optimization problem. To solve it efficiently, we propose a sequential convex programming algorithm that solves a sequence of convex optimization problems. Beginning by optimizing the aperture weights of many (72) evenly distributed beams using the beam's eye view of the target from each direction as the initial aperture shape, the search space is constrained to allowing the leaves to move within a pre-defined step-size. A convex approximation problem is introduced and solved to optimize the leaf positions and the aperture weights within the search space. The algorithm is equipped with both local and global search strategies, whereby a global search is followed by a local search: a large step-size results in a global search with a less accurate convex approximation, followed by a small step-size local search with an accurate convex approximation. The performance of the proposed algorithm is tested on three patients with three different disease sites (paraspinal, prostate and oligometastasis). The algorithm generates VMAT plans comparable to the ideal 72-beam fluence map optimized plans (i.e. IMRT plans before leaf sequencing) in 14 iterations and 36 mins on average. The algorithm is also tested on a small down-sampled prostate case for which we could computationally afford to obtain the ground-truth by solving the non-convex mixed-integer optimization problem exactly. This general algorithm is able to produce results essentially equivalent to the ground-truth but 12 times faster. The algorithm is also scalable and can handle real clinical cases, whereas the ground-truth solution using mixed-integer optimization can only be obtained for highly down-sampled cases.

A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks

Recently, artificial intelligence technologies and algorithms have become a major focus for advancements in treatment planning for radiation therapy. As these are starting to become incorporated into the clinical workflow, a major concern from clinicians is not whether the model is accurate, but whether the model can express to a human operator when it does not know if its answer is correct. We propose to use Monte Carlo Dropout (MCDO) and the bootstrap aggregation (bagging) technique on deep learning (DL) models to produce uncertainty estimations for radiation therapy dose prediction. We show that both models are capable of generating a reasonable uncertainty map, and, with our proposed scaling technique, creating interpretable uncertainties and bounds on the prediction and any relevant metrics. Performance-wise, bagging provides statistically significant reduced loss value and errors in most of the metrics investigated in this study. The addition of bagging was able to further reduce errors by another 0.34% for [Formula: see text] and 0.19% for [Formula: see text] on average, when compared to the baseline model. Overall, the bagging framework provided significantly lower mean absolute error (MAE) of 2.62, as opposed to the baseline model's MAE of 2.87. The usefulness of bagging, from solely a performance standpoint, does highly depend on the problem and the acceptable predictive error, and its high upfront computational cost during training should be factored in to deciding whether it is advantageous to use it. In terms of deployment with uncertainty estimations turned on, both methods offer the same performance time of about 12 s. As an ensemble-based metaheuristic, bagging can be used with existing machine learning architectures to improve stability and performance, and MCDO can be applied to any DL models that have dropout as part of their architecture.

Photon beam energy dependent single-arc volumetric modulated arc optimization

PURPOSE

The purpose of this work was to present a new single-arc mixed photon (6&18MV) VMAT (SAMP) optimization framework that concurrently optimizes for two photon energies with corresponding partial arc lengths.

METHODS AND MATERIALS

Owing to simultaneous optimization of energy dependent intensity maps and corresponding arc locations, the proposed model poses nonlinearity. Unique relaxation constraints based on McCormick approximations were introduced for linearization. Energy dependent intensity maps were then decomposed to generate apertures. Feasibility of the proposed framework was tested on a sample of ten prostate cancer cases with lateral separation ranging from 34 cm (case no.1) to 52 cm (case no.6). The SAMP plans were compared against single energy (6MV) VMAT (SE) plans through dose volume histograms (DVHs) and radiobiological parameters including normal tissue complication probability (NTCP) and equivalent uniform dose (EUD).

RESULTS

The contribution of higher energy photon beam optimized by the algorithm demonstrated an increase for cases with a lateral separation >40 cm. SAMP-VMAT notably improved bladder and rectum sparing in large size cases. Compared to single energy, SAMP-VMAT plans reduced bladder and rectum NTCP in cases with large lateral separation. With the exception of one case, SAMP-VMAT either improved or maintained femoral heads compared to SE-VMAT. SAMP-VMAT reduced the nontarget tissue integral dose in all ten cases.

CONCLUSIONS

A single-arc VMAT optimization framework comprising mixed photon energy partial arcs was presented. Overall results underline the feasibility and potential of the proposed approach for improving OAR sparing in large size patients without compromising the target homogeneity and coverage.

Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose-volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy

PURPOSE

We propose a novel domain-specific loss, which is a differentiable loss function based on the dose-volume histogram (DVH), and combine it with an adversarial loss for the training of deep neural networks. In this study, we trained a neural network for generating Pareto optimal dose distributions, and evaluate the effects of the domain-specific loss on the model performance.

METHODS

In this study, three loss functions - mean squared error (MSE) loss, DVH loss, and adversarial (ADV) loss - were used to train and compare four instances of the neural network model: (a) MSE, (b) MSE + ADV, (c) MSE + DVH, and (d) MSE + DVH+ADV. The data for 70 prostate patients, including the planning target volume (PTV), and the organs at risk (OAR) were acquired as 96 × 96 × 24 dimension arrays at 5 mm3 voxel size. The dose influence arrays were calculated for 70 prostate patients, using a 7 equidistant coplanar beam setup. Using a scalarized multicriteria optimization for intensity-modulated radiation therapy, 1200 Pareto surface plans per patient were generated by pseudo-randomizing the PTV and OAR tradeoff weights. With 70 patients, the total number of plans generated was 84 000 plans. We divided the data into 54 training, 6 validation, and 10 testing patients. Each model was trained for a total of 100,000 iterations, with a batch size of 2. All models used the Adam optimizer, with a learning rate of 1 × 10-3 .

RESULTS

Training for 100 000 iterations took 1.5 days (MSE), 3.5 days (MSE+ADV), 2.3 days (MSE+DVH), and 3.8 days (MSE+DVH+ADV). After training, the prediction time of each model is 0.052 s. Quantitatively, the MSE+DVH+ADV model had the lowest prediction error of 0.038 (conformation), 0.026 (homogeneity), 0.298 (R50), 1.65% (D95), 2.14% (D98), and 2.43% (D99). The MSE model had the worst prediction error of 0.134 (conformation), 0.041 (homogeneity), 0.520 (R50), 3.91% (D95), 4.33% (D98), and 4.60% (D99). For both the mean dose PTV error and the max dose PTV, Body, Bladder and rectum error, the MSE+DVH+ADV outperformed all other models. Regardless of model, all predictions have an average mean and max dose error <2.8% and 4.2%, respectively.

CONCLUSION

The MSE+DVH+ADV model performed the best in these categories, illustrating the importance of both human and learned domain knowledge. Expert human domain-specific knowledge can be the largest driver in the performance improvement, and adversarial learning can be used to further capture nuanced attributes in the data. The real-time prediction capabilities allow for a physician to quickly navigate the tradeoff space for a patient, and produce a dose distribution as a tangible endpoint for the dosimetrist to use for planning. This is expected to considerably reduce the treatment planning time, allowing for clinicians to focus their efforts on the difficult and demanding cases.

Simultaneous optimization of mixed photon energy beams in volumetric modulated arc therapy

PURPOSE

Despite the availability of multiple energy photon beams on clinical linear accelerators, volumetric modulated arc therapy (VMAT) optimization is currently limited to a single photon beam. The purpose of this work was to present a proof-of-principle study on an algorithm for simultaneous optimization of mixed photon beams for VMAT (MP - VMAT), utilizing an additional photon energy as an additional degree of freedom.

METHODS

The MP - VMAT optimization algorithm is presented as a two-step heuristic approach. First, a convex linear programming problem is solved for simultaneous optimization of nonuniform dual energy intensity maps (DEIMs) for an angular resolution of 36 equi-spaced beam segments. Subsequently, for a given gantry speed schedule, the second step aims to best replicate each DEIM by dispersing MP - VMAT apertures along with their corresponding intensities over their respective beam segment. This constitutes a nonlinear problem, which is linearized using McCormick relaxation. The final large-scale mixed integer linear programming (MILP) dispersion model ensures a contiguous and smooth transition of multileaf collimators (MLCs) from one beam segment to the next. To demonstrate the proof-of-principle, we first compared the quality of dose volume histograms (DVHs) of MP - VMAT to the ones calculated from 36 DEIMs following the step 1 of MP - VMAT model. Additionally, the MLCs motion violations were evaluated for the complete 360° gantry rotation for gantry speeds ranging from 1 to 6° per second. The quality of MP - VMAT plans were also compared to conventional single energy VMAT plans via DVH, homogeneity index (HI), and conformity number (CN) for two prostate cases.

RESULTS

The MP - VMAT model resulted in a successful convergence of DVHs relative to the ones from DEIMs with HI and CN of 0.05 and 0.9, respectively, for 1 and 2° per second gantry speed schedules. In replicating the DEIMs, the MILP dispersion model was able to achieve optimality for almost all segments at 1° per second and for majority of segments at 2° per second. Although, DVHs quality was slightly inferior for 3° per second gantry speed, the target conformity of 0.9 and heterogeneity of 0.08 were achievable even for the suboptimal solutions. No violations of the MLC constraints were observed throughout the complete 360 degree arc rotation for any gantry speed schedule, thereby confirming MILP dispersion model. For the two prostate cases, the results showed MP - VMAT's ability to achieve substantial dose reduction in rectum and bladder while yielding similar target coverage compared to single energy VMAT. Bladder volume was mostly spared in low-to-intermediate dose region. Rectal volume sparing (3 % to 12 %) was observed in the intermediate (from 25 to 50 Gy) dose region.

CONCLUSION

We demonstrate the first formalism of a large-scale simultaneous optimization of mixed photon energy beams for VMAT. Dosimetric comparison of MP - VMAT to single energy VMAT demonstrated potential advantages of using mixed photon energy beams for prostate plans, thus providing an impetus for further testing on a large clinical cohort.

Integrating DVH criteria into a column generation algorithm for VMAT treatment planning

Volumetric-modulated arc therapy (VMAT) treatment planning is an efficient treatment technique with a high degree of flexibility in terms of dose rate, gantry speed, and aperture shapes during rotation around the patient. However, the dynamic nature of VMAT results in a large-scale nonconvex optimization problem. Determining the priority of the tissues and voxels to obtain clinically acceptable treatment plans poses additional challenges for VMAT optimization. The main purpose of this paper is to develop an automatic planning approach integrating dose-volume histogram (DVH) criteria in direct aperture optimization for VMAT, by adjusting the model parameters during the algorithm. The proposed algorithm is based on column generation, an optimization technique that sequentially generates the apertures and optimizes the corresponding intensities. We take the advantage of iterative procedure in this method to modify the weight vector of the penalty function based on the DVH criteria and decrease the use of trial-and-error in the search for clinically acceptable plans. We evaluate the efficiency of the algorithm and treatment quality using a clinical prostate case and a challenging head-and-neck case. In both cases, we generate 15 random initial weight vectors to assess the robustness of the algorithm. In the prostate case, our methodology obtained clinically acceptable plans in all instances with only a 10% increase in the computational time, while simple VMAT optimization found just three acceptable plans. To have an idea with respect to the existing software, we compared the obtained DVH to a commercial software. The quality of the diagrams of the proposed method, especially for the healthy tissues, is significantly better while the computational time is less. In the head-and-neck case, 93.3% of the clinically acceptable plans are obtained while no plan was acceptable in simple VMAT. In sum, the results demonstrate the ability of the proposed optimization algorithm to obtain clinically acceptable plans without human intervention and also its robustness to weight parameters. Moreover, our proposed weight adjustment procedure proves to reduce the symmetry in the solution space and the time required for the post-optimization phase.

A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning

With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able to accurately predict the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer patients, where the average Dice similarity coefficient is 0.91 when comparing the predicted vs. true isodose volumes between 0% and 100% of the prescription dose. The average value of the absolute differences in [max, mean] dose is found to be under 5% of the prescription dose, specifically for each structure is [1.80%, 1.03%](PTV), [1.94%, 4.22%](Bladder), [1.80%, 0.48%](Body), [3.87%, 1.79%](L Femoral Head), [5.07%, 2.55%](R Femoral Head), and [1.26%, 1.62%](Rectum) of the prescription dose. We thus managed to map a desired radiation dose distribution from a patient's PTV and OAR contours. As an additional advantage, relatively little data was used in the techniques and models described in this paper.

Multidimensional correlation among plan complexity, quality and deliverability parameters for volumetric-modulated arc therapy using canonical correlation analysis

A multidimensional exploratory statistical method, canonical correlation analysis (CCA), was applied to evaluate the impact of complexity parameters on the plan quality and deliverability of volumetric-modulated arc therapy (VMAT) and to determine parameters in the generation of an ideal VMAT plan. Canonical correlations among complexity, quality and deliverability parameters of VMAT, as well as the contribution weights of different parameters were investigated with 71 two-arc VMAT nasopharyngeal cancer (NPC) patients, and further verified with 28 one-arc VMAT prostate cancer patients. The average MU and MU per control point (MU/CP) for two-arc VMAT plans were 702.6 ± 55.7 and 3.9 ± 0.3 versus 504.6 ± 99.2 and 5.6 ± 1.1 for one-arc VMAT plans, respectively. The individual volume-based 3D gamma passing rates of clinical target volume (γCTV) and planning target volume (γPTV) for NPC and prostate cancer patients were 85.7% ± 9.0% vs 92.6% ± 7.8%, and 88.0% ± 7.6% vs 91.2% ± 7.7%, respectively. Plan complexity parameters of NPC patients were correlated with plan quality (P = 0.047) and individual volume-based 3D gamma indices γ(IV) (P = 0.01), in which, MU/CP and segment area (SA) per control point (SA/CP) were weighted highly in correlation with γ(IV) , and SA/CP, percentage of CPs with SA < 5 × 5 cm2 (%SA < 5 × 5 cm2) and PTV volume were weighted highly in correlation with plan quality with coefficients of 0.98, 0.68 and -0.99, respectively. Further verification with one-arc VMAT plans demonstrated similar results. In conclusion, MU, SA-related parameters and PTV volume were found to have strong effects on the plan quality and deliverability.

A comprehensive formulation for volumetric modulated arc therapy planning

PURPOSE

Volumetric modulated arc therapy (VMAT) is a widely employed radiation therapy technique, showing comparable dosimetry to static beam intensity modulated radiation therapy (IMRT) with reduced monitor units and treatment time. However, the current VMAT optimization has various greedy heuristics employed for an empirical solution, which jeopardizes plan consistency and quality. The authors introduce a novel direct aperture optimization method for VMAT to overcome these limitations.

METHODS

The comprehensive VMAT (comVMAT) planning was formulated as an optimization problem with an L2-norm fidelity term to penalize the difference between the optimized dose and the prescribed dose, as well as an anisotropic total variation term to promote piecewise continuity in the fluence maps, preparing it for direct aperture optimization. A level set function was used to describe the aperture shapes and the difference between aperture shapes at adjacent angles was penalized to control MLC motion range. A proximal-class optimization solver was adopted to solve the large scale optimization problem, and an alternating optimization strategy was implemented to solve the fluence intensity and aperture shapes simultaneously. Single arc comVMAT plans, utilizing 180 beams with 2° angular resolution, were generated for a glioblastoma multiforme case, a lung (LNG) case, and two head and neck cases-one with three PTVs (H&N3PTV) and one with foue PTVs (H&N4PTV)-to test the efficacy. The plans were optimized using an alternating optimization strategy. The plans were compared against the clinical VMAT (clnVMAT) plans utilizing two overlapping coplanar arcs for treatment.

RESULTS

The optimization of the comVMAT plans had converged within 600 iterations of the block minimization algorithm. comVMAT plans were able to consistently reduce the dose to all organs-at-risk (OARs) as compared to the clnVMAT plans. On average, comVMAT plans reduced the max and mean OAR dose by 6.59% and 7.45%, respectively, of the prescription dose. Reductions in max dose and mean dose were as high as 14.5 Gy in the LNG case and 15.3 Gy in the H&N3PTV case. PTV coverages measured by D95, D98, and D99 were within 0.25% of the prescription dose. By comprehensively optimizing all beams, the comVMAT optimizer gained the freedom to allow some selected beams to deliver higher intensities, yielding a dose distribution that resembles a static beam IMRT plan with beam orientation optimization.

CONCLUSIONS

The novel nongreedy VMAT approach simultaneously optimizes all beams in an arc and then directly generates deliverable apertures. The single arc VMAT approach thus fully utilizes the digital Linac's capability in dose rate and gantry rotation speed modulation. In practice, the new single VMAT algorithm generates plans superior to existing VMAT algorithms utilizing two arcs.

Comparison of dosimetric variation between prostate IMRT and VMAT due to patient's weight loss: Patient and phantom study

AIM

This study compared the dosimetric impact between prostate IMRT and VMAT due to patient's weight loss.

BACKGROUND

Dosimetric variation due to change of patient's body contour is difficult to predict in prostate IMRT and VMAT, since a large number of small and irregular segmental fields is used in the delivery.

MATERIALS AND METHODS

Five patients with prostate volumes ranging from 32.0 to 86.5 cm(3) and a heterogeneous pelvis phantom were used for prostate IMRT and VMAT plans using the same set of dose-volume constraints. Doses in IMRT and VMAT plans were recalculated with the patient's and phantom's body contour reduced by 0.5-2 cm to mimic size reduction. Dose coverage/criteria of the PTV and CTV and critical organs (rectum, bladder and femoral heads) were compared between IMRT and VMAT.

RESULTS

In IMRT plans, increases of the D99% for the PTV and CTV were equal to 4.0 ± 0.1% per cm of reduced depth, which were higher than those in VMAT plans (2.7 ± 0.24% per cm). Moreover, increases of the D30% of the rectum and bladder per reduced depth in IMRT plans (4.0 ± 0.2% per cm and 3.5 ± 0.5% per cm) were higher than those of VMAT (2.2 ± 0.2% per cm and 2.0 ± 0.6% per cm). This was also true for the increase of the D5% for the right femoral head in a patient or phantom with size reduction due to weight loss.

CONCLUSIONS

VMAT would be preferred to IMRT in prostate radiotherapy, when a patient has potential to suffer from weight loss during the treatment.

Prostate volumetric-modulated arc therapy: dosimetry and radiobiological model variation between the single-arc and double-arc technique

This study investigates the dosimetry and radiobiological model variation when a second photon arc was added to prostate volumetric‐modulated arc therapy (VMAT) using the single‐arc technique. Dosimetry and radiobiological model comparison between the single‐arc and double‐arc prostate VMAT plans were performed on five patients with prostate volumes ranging from 29−68.1 cm3. The prescription dose was 78 Gy/39 fractions and the photon beam energy was 6 MV. Dose‐volume histogram, mean and maximum dose of targets (planning and clinical target volume) and normal tissues (rectum, bladder and femoral heads), dose‐volume criteria in the treatment plan (D99% of PTV; D30%,D50%,V17Gy and V35Gy of rectum and bladder; D5% of femoral heads), and dose profiles along the vertical and horizontal axis crossing the isocenter were determined using the single‐arc and double‐arc VMAT technique. For comparison, the monitor unit based on the RapidArc delivery method, prostate tumor control probability (TCP), and rectal normal tissue complication probability (NTCP) based on the Lyman‐Burman‐Kutcher algorithm were calculated. It was found that though the double‐arc technique required almost double the treatment time than the single‐arc, the double‐arc plan provided a better rectal and bladder dose‐volume criteria by shifting the delivered dose in the patient from the anterior–posterior direction to the lateral. As the femoral head was less radiosensitive than the rectum and bladder, the double‐arc technique resulted in a prostate VMAT plan with better prostate coverage and rectal dose‐volume criteria compared to the single‐arc. The prostate TCP of the double‐arc plan was found slightly increased (0.16%) compared to the single‐arc. Therefore, when the rectal dose‐volume criteria are very difficult to achieve in a single‐arc prostate VMAT plan, it is worthwhile to consider the double‐arc technique.

PACS number: 87.55.D‐, 87.55.dk, 87.55.K‐, 87.55.Qr

Tomotherapy-like versus VMAT-like treatments: a multicriteria comparison for a prostate geometry

PURPOSE

To perform a methodological comparison of volumetric modulated arc therapy (VMAT)-like and tomotherapy-like techniques for a prostate geometry, exploring the dependence on machine, delivery, and optimization parameters of cost function values optimized for each technique.

METHODS

A gradient-descent algorithm is used to optimize tomotherapy-like treatments, while VMAT-like optimization is carried out using a direct-aperture simulated annealing algorithm with 180 control points equispaced at 2° angles. Dose distributions are linked to fluences via a three-dimensional double-gaussian pencil beam model. Plans are optimized for a prostate geometry, outlined according to the CHHiP protocol. The cost function used for optimization contains ten simple functions, each of which describes a single planning objective. These functions are split into three structure groups according to whether they are used to control PTV, rectal or bladder dose levels. Different optimizations have been performed by varying the relative weights of each of these structure groups, exploring in this way a three-dimensional Pareto front. Plan quality is studied according to the value of the optimized cost function and the relative Euclidean distance between the components of the cost function and those of the nearest plan lying on a reference Pareto front obtained for tomotherapy-like plans generated using a 1 cm fan-beam width and 1/3 pitch.

RESULTS

The quality of tomotherapy-like optimization depends on the fan-beam width, s, and rotation pitch, p, used to deliver the treatment. These values together define the effective longitudinal resolution with which fluence can be modulated, and lower cost function values are obtained for treatments optimized with tighter pitches and narrower fan-beam widths (higher modulation resolution). On the other hand, the cost function values of VMAT-like optimizations depends on the optimization running time, leaf displacement constraints, and number of arcs employed, as well as on the size of the beamlets used in the optimization (a change in leaf width from 5 to 10 mm clearly worsens the value of the objective function, but only a marginal improvement is observed when the leaf movement discretization step is reduced from 5 to 5/3 mm). However, for no combination of these parameter values did VMAT-like optimizations match the cost function values of optimized tomo-like plans obtained for s = 1 cm and p = 1∕3 (or 1/2). This is the case all across the Pareto front. On the other hand, cost function values of VMAT-like plans are generally lower than those of optimized tomotherapy-like plans obtained for s = 2.5 cm.

CONCLUSIONS

Tomotherapy-like plans created for the prostate geometry using a 1 cm fan-beam width and pitches of 1/3 or 1/2 have lower cost function values than VMAT-like plans, although the associated dosimetric improvements are quite small, both techniques generating very good dose distributions. When a 2.5 cm wide fan-beam is used for tomotherapy-like treatments the pattern is reversed, the tomotherapy-like plans having higher cost functions than the VMAT-like ones.

Delivery parameter variations and early clinical outcomes of volumetric modulated arc therapy for 31 prostate cancer patients: an intercomparison of three treatment planning systems

We created volumetric modulated arc therapy (VMAT) plans for 31 prostate cancer patients using one of three treatment planning systems (TPSs)—ERGO++, Monaco, or Pinnacle—and then treated those patients. A dose of 74 Gy was prescribed to the planning target volume (PTV). The rectum, bladder, and femur were chosen as organs at risk (OARs) with specified dose-volume constraints. Dose volume histograms (DVHs), the mean dose rate, the beam-on time, and early treatment outcomes were evaluated and compared. The DVHs calculated for the three TPSs were comparable. The mean dose rates and beam-on times for Ergo++, Monaco, and SmartArc were, respectively, 174.3 ± 17.7, 149.7 ± 8.4, and 185.8 ± 15.6 MU/min and 132.7 ± 8.4, 217.6 ± 13.1, and 127.5 ± 27.1 sec. During a follow-up period of 486.2 ± 289.9 days, local recurrence was not observed, but distant metastasis was observed in a single patient. Adverse events of grade 3 to grade 4 were not observed. The mean dose rate for Monaco was significantly lower than that for ERGO++ and SmartArc (P < 0.0001), and the beam-on time for Monaco was significantly longer than that for ERGO++ and SmartArc (P < 0.0001). Each TPS was successfully used for prostate VMAT planning without significant differences in early clinical outcomes despite significant TPS-specific delivery parameter variations.

Dosimetry estimation on variations of patient size in prostate volumetric-modulated arc therapy

This study investigated the dosimetric variations of the target and critical organs of patients who had weight loss associated with prostate volumetric-modulated arc therapy (VMAT). Five patients with prostate volumes ranging from 32-86.5 cm³ were selected from a group of 30 patients. Prostate VMAT plans were carried out on each patient using the 6-MV photon beam with a single 360° arc. Decrease of patient size as a result of weight loss was mimicked by contracting the patient's external contour in the anterior, left, and right directions with depths from 0.5-2 cm. Soft tissue excluded by the contracted external contour was replaced by air and the dose distribution was recalculated using the same beam geometry and dose prescription. Dose-volume histograms and dose-volume points such as D99% and D5% for the planning target volume (PTV), clinical target volume (CTV), rectum, bladder, and femoral heads were calculated with variations of reduced depth. In addition, the minimum, maximum, and mean doses for the target and critical organs were determined. PTV and CTV D99% were found to have increased 2.86 ± 0.30% per cm and 2.75 ± 0.38% per cm of reduced depth ranging from 0.5-2 cm. Moreover, the rectal and bladder D30% increased 2.20 ± 0.20% per cm and 2.31 ± 0.83% per cm, and the femoral head D5% increased 3.30 ± 0.11% per cm of reduced depth. Results from variations of the minimum, maximum, and mean doses of the PTV, CTV, rectum, bladder, and femoral heads showed that there was a >5% increase of dose when the reduced depth reached 2 cm. This study provided dosimetry estimation for radiation oncology staff to justify dose variations of the target and critical organs when patients' weight loss occurred in prostate VMAT. Dose variations >5% were seen when the patients' reduced depth was equal to 2 cm.

A new column-generation-based algorithm for VMAT treatment plan optimization

We study the treatment plan optimization problem for volumetric modulated arc therapy (VMAT). We propose a new column-generation-based algorithm that takes into account bounds on the gantry speed and dose rate, as well as an upper bound on the rate of change of the gantry speed, in addition to MLC constraints. The algorithm iteratively adds one aperture at each control point along the treatment arc. In each iteration, a restricted problem optimizing intensities at previously selected apertures is solved, and its solution is used to formulate a pricing problem, which selects an aperture at another control point that is compatible with previously selected apertures and leads to the largest rate of improvement in the objective function value of the restricted problem. Once a complete set of apertures is obtained, their intensities are optimized and the gantry speeds and dose rates are adjusted to minimize treatment time while satisfying all machine restrictions. Comparisons of treatment plans obtained by our algorithm to idealized IMRT plans of 177 beams on five clinical prostate cancer cases demonstrate high quality with respect to clinical dose-volume criteria. For all cases, our algorithm yields treatment plans that can be delivered in around 2 min. Implementation on a graphic processing unit enables us to finish the optimization of a VMAT plan in 25-55 s.

Implementing RapidArc into clinical routine: a comprehensive program from machine QA to TPS validation and patient QA

PURPOSE

With the increased commercial availability of intensity modulated arc therapy (IMAT) comes the need for comprehensive QA programs, covering the different aspects of this newly available technology. This manuscript proposes such a program for the RapidArc (RA) (Varian Medical Systems, Palo Alto) IMAT solution.

METHODS

The program was developed and tested out for a Millennium120 MLC on iX Clinacs and a HighDefinition MLC on a Novalis TX, using a variety of measurement equipment including Gafchromic film, 2D ion chamber arrays (Seven29 and StarCheck, PTW, Freiburg, Germany) with inclinometer and Octavius phantom, the Delta4 systam (ScandiDos, Uppsala, Sweden) and the portal imager (EPID). First, a number of complementary machine QA tests were developed to monitor the correct interplay between the accelerating/decelerating gantry, the variable dose rate and the MLC position, straining the delivery to the maximum allowed limits. Second, a systematic approach to the validation of the dose calculation for RA was adopted, starting with static gantry and RA specific static MLC shapes and gradually moving to dynamic gantry, dynamic MLC shapes. RA plans were then optimized on a series of artificial structures created within the homogeneous Octavius phantom and within a heterogeneous lung phantom. These served the double purpose of testing the behavior of the optimization algorithm (PRO) as well as the precision of the forward dose calculation. Finally, patient QA on a series of clinical cases was performed with different methods. In addition to the well established in-phantom QA, we evaluated the portal dosimetry solution within the Varian approach.

RESULTS

For routine machine QA, the "Snooker Cue" test on the EPID proved to be the most sensitive to overall problem detection. It is also the most practical one. The "Twinkle" and "Sunrise" tests were useful to obtain well differentiated information on the individual treatment delivery components. The AAA8.9 dose calculations showed excellent agreement with all corresponding measurements, except in areas where the 2.5 mm fixed fluence resolution was insufficient to accurately model the tongue and groove effect or the dose through nearly closed opposing leafs. Such cases benefited from the increased fluence resolution in AAA10.0. In the clinical RA fields, these effects were smeared out spatially and the impact of the fluence resolution was considerably less pronounced. The RA plans on the artificial structure sets demonstrated some interesting characteristics of the PRO8.9 optimizer, such as a sometimes unexpected dependence on the collimator rotation and a suboptimal coverage of targets within lung tissue. Although the portal dosimetry was successfully validated, we are reluctant to use it as a sole means of patient QA as long as no gantry angle information is embedded.

CONCLUSIONS

The all-in validation program allows a systematic approach in monitoring the different levels of RA treatments. With the systematic approach comes a better understanding of both the capabilities and the limits of the used solution. The program can be useful for implementation, but also for the validation of major upgrades.

Choreographing couch and collimator in volumetric modulated arc therapy

PURPOSE

To design and optimize trajectory-based, noncoplanar subarcs for volumetric modulated arc therapy (VMAT) deliverable on both Varian TrueBEAM system and traditional accelerators; and to investigate their potential advantages for treating central nervous system (CNS) tumors.

METHODS AND MATERIALS

To guide the computerized selection of beam trajectories consisting of simultaneous couch, gantry, and collimator motion, a score function was implemented to estimate the geometric overlap between targets and organs at risk for each couch/gantry angle combination. An initial set of beam orientations is obtained as a function of couch and gantry angle, according to a minimum search of the score function excluding zones of collision. This set is grouped into multiple continuous and extended subarcs subject to mechanical limitations using a hierarchical clustering algorithm. After determination of couch/gantry trajectories, a principal component analysis finds the collimator angle at each beam orientation that minimizes residual target-organ at risk overlaps. An in-house VMAT optimization algorithm determines the optimal multileaf collimator position and monitor units for control points within each subarc. A retrospective study of 10 CNS patients compares the proposed method of VMAT trajectory with dynamic gantry, leaves, couch, and collimator motion (Tra-VMAT); a standard noncoplanar VMAT with no couch/collimator motion within subarcs (Std-VMAT); and noncoplanar intensity-modulated radiotherapy (IMRT) plans that were clinically used.

RESULTS

Tra-VMAT provided improved target dose conformality and lowered maximum dose to brainstem, optic nerves, and chiasm by 7.7%, 1.1%, 2.3%, and 1.7%, respectively, compared with Std-VMAT. Tra-VMAT provided higher planning target volume minimum dose and reduced maximum dose to chiasm, optic nerves, and cochlea by 6.2%, 1.3%, 6.3%, and 8.4%, respectively, and reduced cochlea mean dose by 8.7%, compared with IMRT. Tra-VMAT averaged beam-on time was comparable to Std-VMAT but significantly (45%) less than IMRT.

CONCLUSION

Optimized couch, gantry, and collimator trajectories may be integrated into VMAT with improved mechanical flexibility and may provide better dosimetric properties and improved efficiency in the treatment of CNS tumors.

Comparison of Elekta VMAT with helical tomotherapy and fixed field IMRT: plan quality, delivery efficiency and accuracy

PURPOSE

Helical tomotherapy (HT) and volumetric modulated arc therapy (VMAT) are arc-based approaches to IMRT delivery. The objective of this study is to compare VMAT to both HT and fixed field IMRT in terms of plan quality, delivery efficiency, and accuracy.

METHODS

Eighteen cases including six prostate, six head-and-neck, and six lung cases were selected for this study. IMRT plans were developed using direct machine parameter optimization in the Pinnacle3 treatment planning system. HT plans were developed using a Hi-Art II planning station. VMAT plans were generated using both the Pinnacle3 SmartArc IMRT module and a home-grown arc sequencing algorithm. VMAT and HT plans were delivered using Elekta's PreciseBeam VMAT linac control system (Elekta AB, Stockholm, Sweden) and a TomoTherapy Hi-Art II system (TomoTherapy Inc., Madison, WI), respectively. Treatment plan quality assurance (QA) for VMAT was performed using the IBA MatriXX system while an ion chamber and films were used for HT plan QA.

RESULTS

The results demonstrate that both VMAT and HT are capable of providing more uniform target doses and improved normal tissue sparing as compared with fixed field IMRT. In terms of delivery efficiency, VMAT plan deliveries on average took 2.2 min for prostate and lung cases and 4.6 min for head-and-neck cases. These values increased to 4.7 and 7.0 min for HT plans.

CONCLUSIONS

Both VMAT and HT plans can be delivered accurately based on their own QA standards. Overall, VMAT was able to provide approximately a 40% reduction in treatment time while maintaining comparable plan quality to that of HT.