A comprehensive formulation for volumetric modulated arc therapy planning

Nguyen, Dan; Lyu, Qihui; Ruan, Dan; O’Connor, Daniel; Low, Daniel A.; Sheng, Ke

doi:10.1118/1.4953832

Cited by 19 publications

(35 citation statements)

References 46 publications

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“…Much work over the years has been focused on reducing the treatment complexity by simplifying certain aspects in the planning workflow, such as feasibility seeking, 16 multicriteria optimization for tradeoff navigation on the Pareto surface, [17][18][19] and other algorithms for performance improvements. [20][21][22][23][24][25][26] While effective, these methods still require a large amount of intelligent input from the dosimetrist and physician, such as in weight tuning and deciding appropriate dose-volume constraints and tradeoffs.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Fortunately, the formulation is multiconvex—by updating one variable while holding the other two constant, we have a convex module. Similar to a previous study, the algorithm is broken down into three modules, which are evaluated in an alternating block fashion.…”

Section: Methodsmentioning

confidence: 99%

“…The Chambolle-Pock algorithm, a first-order primal dual algorithm, 25,26 has previously been successfully implemented for fluence map optimization problems. 9,33,36,45 The algorithm solves a primal problem of the canonical form…”

Section: Conflict Of Interestmentioning

confidence: 99%

Deterministic direct aperture optimization using multiphase piecewise constant segmentation

et al. 2017

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Purpose Direct Aperture Optimization (DAO) attempts to incorporate machine constraints in the inverse optimization to eliminate the post-processing steps in fluence map optimization (FMO) that degrade plan quality. Current commercial DAO methods utilize a stochastic or greedy approach to search a small aperture solution space. In this study, we propose a novel deterministic direct aperture optimization that integrates the segmentation of fluence map in the optimization problem using the multiphase piecewise constant Mumford-Shah formulation. Methods The Mumford-Shah based direct aperture optimization problem was formulated to include an L2-norm dose fidelity term to penalize differences between the projected dose and the prescribed dose, an anisotropic total variation term to promote piecewise continuity in the fluence maps, and the multiphase piecewise constant Mumford-Shah function to partition the fluence into pairwise discrete segments. A proximal-class, first-order primal-dual solver was implemented to solve the large scale optimization problem, and an alternating module strategy was implemented to update fluence and delivery segments. Three patients of varying complexity—one glioblastoma multiforme (GBM) patient, one lung (LNG) patient, and one bilateral head and neck (H&N) patient with 3 PTVs—were selected to test the new DAO method. For each patient, 20 non-coplanar beams were first selected using column generation, followed by the Mumford-Shah based DAO (DAOMS). For comparison, a popular and successful approach to DAO known as simulated annealing—a stochastic approach—was replicated. The simulated annealing DAO (DAOSA) plans were then created using the same beam angles and maximum number of segments per beam. PTV coverage, PTV homogeneity true(D95D5true), and OAR sparing were assessed for each plan. In addition, high dose spillage, defined as the 50% isodose volume divided by the tumor volume, as well as conformity, defined as the van’t Riet conformation number, were evaluated. Results DAOMS achieved essentially the same OAR doses compared with the DAOSA plans for the GBM case. The average difference of OAR Dmax and Dmean between the two plans were within 0.05% of the plan prescription dose. The lung case showed slightly improved critical structure sparing using the DAOMS approach, where the average OAR Dmax and Dmean were reduced by 3.67% and 1.08%, respectively, of the prescription dose. The DAOMS plan substantially improved OAR dose sparing for the H&N patient, where the average OAR Dmax and Dmean were reduced by over 10% of the prescription dose. The DAOMS and DAOSA plans were comparable for the GBM and LNG PTV coverage, while the DAOMS plan substantially improved the H&N PTV coverage, increasing D99 by 6.98% of the prescription dose. For the GBM and LNG patients, the DAOMS and DAOSA plans had comparable high dose spillage but slightly worse conformity with the DAOMS approach. For the H&N plan, DAOMS was considerably superior in high dose spillage and conformity to the DAOSA. The deterministic...

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“…We generated many pseudo-random plans by assigning random weights, as described below, to the OARs using an in-house GPU-based proximal-class first-order primal-dual algorithm, Chambolle-Pock. 59 While there are some uses of the Chambolle-Pock algorithm used in radiation therapy, [60][61][62][63][64] any convex solver will theoretically arrive at the same solution as Chambolle-Pock for solving the optimization problem. The weight generation structure fell into one of three categories, as shown in Table I.…”

Section: A Pareto Optimal Plansmentioning

confidence: 99%

Using deep learning to predict beam‐tunable Pareto optimal dose distribution for intensity‐modulated radiation therapy

et al. 2020

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Purpose: Many researchers have developed deep learning models for predicting clinical dose distributions and Pareto optimal dose distributions. Models for predicting Pareto optimal dose distributions have generated optimal plans in real time using anatomical structures and static beam orientations. However, Pareto optimal dose prediction for intensity-modulated radiation therapy (IMRT) prostate planning with variable beam numbers and orientations has not yet been investigated. We propose to develop a deep learning model that can predict Pareto optimal dose distributions by using any given set of beam angles, along with patient anatomy, as input to train the deep neural networks. We implement and compare two deep learning networks that predict with two different beam configuration modalities. Methods: We generated Pareto optimal plans for 70 patients with prostate cancer. We used fluence map optimization to generate 500 IMRT plans that sampled the Pareto surface for each patient, for a total of 35 000 plans. We studied and compared two different models, Models I and II. Although they both used the same anatomical structuresincluding the planning target volume (PTV), organs at risk (OARs), and bodythese models were designed with two different methods for representing beam angles. Model I directly uses beam angles as a second input to the network as a binary vector. Model II converts the beam angles into beam doses that are conformal to the PTV. We divided the 70 patients into 54 training, 6 validation, and 10 testing patients, thus yielding 27 000 training, 3000 validation, and 5000 testing plans. Mean square loss (MSE) was taken as the loss function. We used the Adam optimizer with a default learning rate of 0.01 to optimize the network's performance. We evaluated the models' performance by comparing their predicted dose distributions with the ground truth (Pareto optimal) dose distribution, in terms of dose volume histogram (DVH) plots and evaluation metrics such as PTV D 98 , D 95 , D 50 , D 2 , D max , D mean , Paddick Conformation Number, R50, and Homogeneity index. Results: Our deep learning models predicted voxel-level dose distributions that precisely matched the ground truth dose distributions. The DVHs generated also precisely matched the ground truth. Evaluation metrics such as PTV statistics, dose conformity, dose spillage (R50), and homogeneity index also confirmed the accuracy of PTV curves on the DVH. Quantitatively, Model I's prediction error of 0.043 (confirmation), 0.043 (homogeneity), 0.327 (R50), 2.80% (D95), 3.90% (D98), 0.6% (D50), and 1.10% (D2) was lower than that of Model II, which obtained 0.076 (confirmation), 0.058 (homogeneity), 0.626 (R50), 7.10% (D95), 6.50% (D98), 8.40% (D50), and 6.30% (D2). Model I also outperformed Model II in terms of the mean dose error and the max dose error on the PTV, bladder, rectum, left femoral head, and right femoral head. Conclusions: Treatment planners who use our models will be able to use deep learning to control the trade-offs between the PTV and OAR...

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A comprehensive formulation for volumetric modulated arc therapy planning

Cited by 19 publications

References 46 publications

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

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

Deterministic direct aperture optimization using multiphase piecewise constant segmentation

Using deep learning to predict beam‐tunable Pareto optimal dose distribution for intensity‐modulated radiation therapy

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