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Schipaanboord BWK, Heijmen BJM, Breedveld S. TBS-BAO: fully automated beam angle optimization for IMRT guided by a total-beam-space reference plan. Phys Med Biol 2022; 67. [PMID: 35026742 DOI: 10.1088/1361-6560/ac4b37] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 01/13/2022] [Indexed: 11/11/2022]
Abstract
Properly selected beam angles contribute to the quality of radiotherapy treatment plans. However, the beam angle optimization (BAO) problem is difficult to solve to optimality due to its non-convex discrete nature with many local minima. In this study, we propose TBS-BAO, a novel approach for solving the BAO problem, and test it for non-coplanar robotic CyberKnife radiotherapy for prostate cancer. First, an ideal Pareto-optimal reference dose distribution is automatically generated usinga priorimulti-criterial fluence map optimization (FMO) to generate a plan that includes all candidate beams (total-beam-space, TBS). Then, this ideal dose distribution is reproduced as closely as possible in a subsequent segmentation/beam angle optimization step (SEG/BAO), while limiting the number of allowed beams to a user-selectable preset value. SEG/BAO aims at a close reproduction of the ideal dose distribution. For each of 33 prostate SBRT patients, 18 treatment plans with different pre-set numbers of allowed beams were automatically generated with the proposed TBS-BAO. For each patient, the TBS-BAO plans were then compared to a plan that was automatically generated with an alternative BAO method (Erasmus-iCycle) and to a high-quality manually generated plan. TBS-BAO was able to automatically generate plans with clinically feasible numbers of beams (∼25), with a quality highly similar to corresponding 91-beam ideal reference plans. Compared to the alternative Erasmus-iCycle BAO approach, similar plan quality was obtained for 25-beam segmented plans, while computation times were reduced from 10.7 hours to 4.8/1.5 hours, depending on the applied pencil-beam resolution in TBS-BAO. 25-beam TBS-BAO plans had similar quality as manually generated plans with on average 48 beams, while delivery times reduced from 22.3 to 18.4/18.1 min. TBS reference plans could effectively steer the discrete non-convex BAO.
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Affiliation(s)
- B W K Schipaanboord
- Department of Radiotherapy, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - B J M Heijmen
- Department of Radiotherapy, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - S Breedveld
- Department of Radiotherapy, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
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Kafaei P, Cappart Q, Renaud MA, Chapados N, Rousseau LM. Graph neural networks and deep reinforcement learning for simultaneous beam orientation and trajectory optimization of Cyberknife. Phys Med Biol 2021; 66. [PMID: 34592726 DOI: 10.1088/1361-6560/ac2bb5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 09/30/2021] [Indexed: 11/12/2022]
Abstract
Objective. Despite the high-quality treatment, the long treatment time of the Cyberknife system is believed to be a drawback. The high flexibility of its robotic arm requires meticulous path-finding algorithms to deliver the prescribed dose in the shortest time.Approach. We proposed a Deep Q-learning based on Graph Neural Networks to find the subset of the beams and the order to traverse them. A complex reward function is defined to minimize the distance covered by the robotic arm while avoiding the selection of close beams. Individual beam scores are also generated based on their effect on the beam intensity and are incorporated in the reward function. Main results. The performance of the presented method is evaluated on three clinical cases suffering from lung cancer. Applying this approach leads to an average of 35% reduction in the treatment time while delivering the prescribed dose provided by the physicians.Significance. Shorter treatment times result in a better treatment experience for individual patients, reduces discomfort and the sides effects of inadvertent movements for them. Additionally, it creates the opportunity to treat a higher number of patients in a given time period at the radiation therapy centers.
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Affiliation(s)
- Peyman Kafaei
- Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Canada.,CIRRELT-Interuniversity Research Center on Enterprise Networks, Logistics and Transportation, Montreal, Canada
| | - Quentin Cappart
- CIRRELT-Interuniversity Research Center on Enterprise Networks, Logistics and Transportation, Montreal, Canada.,Department of Computer Engineering and Software Engineering, Polytechnique Montreal, Montreal, Canada
| | - Marc-Andre Renaud
- Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Canada.,CIRRELT-Interuniversity Research Center on Enterprise Networks, Logistics and Transportation, Montreal, Canada
| | - Nicolas Chapados
- Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Canada
| | - Louis-Martin Rousseau
- Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Canada.,CIRRELT-Interuniversity Research Center on Enterprise Networks, Logistics and Transportation, Montreal, Canada
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Sharfo AWM, Rossi L, Dirkx MLP, Breedveld S, Aluwini S, Heijmen BJM. Complementing Prostate SBRT VMAT With a Two-Beam Non-Coplanar IMRT Class Solution to Enhance Rectum and Bladder Sparing With Minimum Increase in Treatment Time. Front Oncol 2021; 11:620978. [PMID: 33816253 PMCID: PMC8018286 DOI: 10.3389/fonc.2021.620978] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Accepted: 03/01/2021] [Indexed: 01/05/2023] Open
Abstract
Purpose Enhance rectum and bladder sparing in prostate SBRT with minimum increase in treatment time by complementing dual-arc coplanar VMAT with a two-beam non-coplanar IMRT class solution (CS). Methods For twenty patients, an optimizer for automated multi-criterial planning with integrated beam angle optimization (BAO) was used to generate dual-arc VMAT plans, supplemented with five non-coplanar IMRT beams with individually optimized orientations (VMAT+5). In all plan generations, reduction of high rectum dose had the highest priority after obtaining adequate PTV coverage. A CS with two most preferred directions in VMAT+5 and largest rectum dose reductions compared to dual-arc VMAT was then selected to define VMAT+CS. VMAT+CS was compared with automatically generated i) dual-arc coplanar VMAT plans (VMAT), ii) VMAT+5 plans, and iii) IMRT plans with 30 patient-specific non-coplanar beam orientations (30-NCP). Plans were generated for a 4 x 9.5 Gy fractionation scheme. Differences in PTV doses, healthy tissue sparing, and computation and treatment delivery times were quantified. Results For equal PTV coverage, VMAT+CS, consisting of dual-arc VMAT supplemented with two fixed, non-coplanar IMRT beams with fixed Gantry/Couch angles of 65°/30° and 295°/-30°, significantly reduced OAR doses and the dose bath, compared to dual-arc VMAT. Mean relative differences in rectum Dmean, D1cc, V40GyEq and V60GyEq were 19.4 ± 10.6%, 4.2 ± 2.7%, 34.9 ± 20.3%, and 39.7 ± 23.2%, respectively (all p<0.001). There was no difference in bladder D1cc, while bladder Dmean reduced by 17.9 ± 11.0% (p<0.001). Also, the clinically evaluated urethra D5%, D10%, and D50% showed small, but statistically significant improvements. All patient VX with X = 2, 5, 10, 20, and 30 Gy were reduced with VMAT+CS, with a maximum relative reduction for V10Gy of 19.0 ± 7.3% (p<0.001). Total delivery times with VMAT+CS only increased by 1.9 ± 0.7 min compared to VMAT (9.1 ± 0.7 min). The dosimetric quality of VMAT+CS plans was equivalent to VMAT+5, while optimization times were reduced by a factor of 25 due to avoidance of individualized BAO. Compared to VMAT+CS, the 30-NCP plans were only favorable in terms of dose bath, at the cost of much enhanced optimization and delivery times. Conclusions The proposed two-beam non-coplanar class solution to complement coplanar dual-arc VMAT resulted in substantial plan quality improvements for OARs (especially rectum) and reduced irradiated patient volumes with minor increases in treatment delivery times.
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Affiliation(s)
- Abdul Wahab M Sharfo
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, Netherlands
| | - Linda Rossi
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, Netherlands
| | - Maarten L P Dirkx
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, Netherlands
| | - Sebastiaan Breedveld
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, Netherlands
| | - Shafak Aluwini
- Department of Radiation Oncology, University Medical Center Groningen, Groningen, Netherlands
| | - Ben J M Heijmen
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, Netherlands
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Kearney V, Chan JW, Wang T, Perry A, Descovich M, Morin O, Yom SS, Solberg TD. DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation. Sci Rep 2020; 10:11073. [PMID: 32632116 PMCID: PMC7338467 DOI: 10.1038/s41598-020-68062-7] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 05/27/2020] [Indexed: 11/08/2022] Open
Abstract
Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-pixel loss to update network parameters. However, stereotactic body radiotherapy (SBRT) dose is often heterogeneous, making it difficult to model using pixel-level loss. Generative adversarial networks (GANs) utilize adversarial learning that incorporates image-level loss and is better suited to learn from heterogeneous labels. However, GANs are difficult to train and rely on compromised architectures to facilitate convergence. This study suggests an attention-gated generative adversarial network (DoseGAN) to improve learning, increase model complexity, and reduce network redundancy by focusing on relevant anatomy. DoseGAN was compared to alternative state-of-the-art dose prediction algorithms using heterogeneity index, conformity index, and various dosimetric parameters. All algorithms were trained, validated, and tested using 141 prostate SBRT patients. DoseGAN was able to predict more realistic volumetric dosimetry compared to all other algorithms and achieved statistically significant improvement compared to all alternative algorithms for the V100 and V120 of the PTV, V60 of the rectum, and heterogeneity index.
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Affiliation(s)
- Vasant Kearney
- Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.
| | - Jason W Chan
- Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA
| | - Tianqi Wang
- Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA
| | - Alan Perry
- Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA
| | - Martina Descovich
- Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA
| | - Olivier Morin
- Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA
| | - Sue S Yom
- Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA
| | - Timothy D Solberg
- Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA
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5
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Bedford JL, Nill S, Oelfke U. Dosimetric accuracy of delivering SBRT using dynamic arcs on Cyberknife. Med Phys 2020; 47:1533-1544. [PMID: 32048303 PMCID: PMC7216988 DOI: 10.1002/mp.14090] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 01/29/2020] [Accepted: 02/06/2020] [Indexed: 12/14/2022] Open
Abstract
PURPOSE Several studies have demonstrated potential improvements in treatment time through the use of dynamic arcs for delivery of stereotactic body radiation therapy (SBRT) on Cyberknife. However, the delivery system has a finite accuracy, so that potential exists for dosimetric uncertainties. This study estimates the expected dosimetric accuracy of dynamic delivery of SBRT, based on realistic estimates of the uncertainties in delivery parameters. METHODS Five SBRT patient cases (prostate A - conventional, prostate B - brachytherapy-type, lung, liver, partial left breast) were retrospectively studied. Treatment plans were produced for a fixed arc trajectory using fluence optimization, segmentation, and direct aperture optimization. Dose rate uncertainty was modeled as a smoothly varying random fluctuation of ± 1.0%, ±2.0% or ± 5.0% over a time period of 10, 30 or 60 s. Multileaf collimator uncertainty was modeled as a lag in position of each leaf up to 0.25 or 0.5 mm. Robot pointing error was modeled as a shift of the target location, with the direction of the shift chosen as a random angle with respect to the multileaf collimator and with a random magnitude in the range 0.0-1.0 mm at the delivery nodes and with an additional random magnitude of 0.5-1.0 mm in between the delivery nodes. The impact of the errors was investigated using dose-volume histograms. RESULTS Uncertainty in dose rate has the effect of varying the total monitor units delivered, which in turn produces a variation in mean dose to the planning target volume. The random sampling of dose rate error produces a distribution of mean doses with a standard deviation proportional to the magnitude of the dose rate uncertainty. A lag in multileaf collimator position of 0.25 or 0.5 mm produces a small impact on the delivered dose. In general, an increase in the PTV mean dose of around 1% is observed. An error in robot pointing of the order of 1 mm produces a small increase in dose inhomogeneity to the planning target volume, sometimes accompanied by an increase in mean dose by around 1%. CONCLUSIONS Based upon the limited data available on the dose rate stability and geometric accuracy of the Cyberknife system, this study estimates that dynamic arc delivery can be accomplished with sufficient accuracy for clinical application. Dose rate variation produces a change in dose to the planning target volume according to the perturbation of total monitor units delivered, while multileaf collimator lag and robot pointing error typically increase the mean dose to the planning target volume by up to 1%.
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Affiliation(s)
- James L. Bedford
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonSM2 5PTUK
| | - Simeon Nill
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonSM2 5PTUK
| | - Uwe Oelfke
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonSM2 5PTUK
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Kearney V, Ziemer BP, Perry A, Wang T, Chan JW, Ma L, Morin O, Yom SS, Solberg TD. Attention-Aware Discrimination for MR-to-CT Image Translation Using Cycle-Consistent Generative Adversarial Networks. Radiol Artif Intell 2020; 2:e190027. [PMID: 33937817 DOI: 10.1148/ryai.2020190027] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Revised: 11/18/2019] [Accepted: 11/25/2019] [Indexed: 11/11/2022]
Abstract
Purpose To suggest an attention-aware, cycle-consistent generative adversarial network (A-CycleGAN) enhanced with variational autoencoding (VAE) as a superior alternative to current state-of-the-art MR-to-CT image translation methods. Materials and Methods An attention-gating mechanism is incorporated into a discriminator network to encourage a more parsimonious use of network parameters, whereas VAE enhancement enables deeper discrimination architectures without inhibiting model convergence. Findings from 60 patients with head, neck, and brain cancer were used to train and validate A-CycleGAN, and findings from 30 patients were used for the holdout test set and were used to report final evaluation metric results using mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). Results A-CycleGAN achieved superior results compared with U-Net, a generative adversarial network (GAN), and a cycle-consistent GAN. The A-CycleGAN averages, 95% confidence intervals (CIs), and Wilcoxon signed-rank two-sided test statistics are shown for MAE (19.61 [95% CI: 18.83, 20.39], P = .0104), structure similarity index metric (0.778 [95% CI: 0.758, 0.798], P = .0495), and PSNR (62.35 [95% CI: 61.80, 62.90], P = .0571). Conclusion A-CycleGANs were a superior alternative to state-of-the-art MR-to-CT image translation methods.© RSNA, 2020.
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Affiliation(s)
- Vasant Kearney
- Department of Radiation Oncology, University of California, 1600 Divisidero St, San Francisco, CA 94115
| | - Benjamin P Ziemer
- Department of Radiation Oncology, University of California, 1600 Divisidero St, San Francisco, CA 94115
| | - Alan Perry
- Department of Radiation Oncology, University of California, 1600 Divisidero St, San Francisco, CA 94115
| | - Tianqi Wang
- Department of Radiation Oncology, University of California, 1600 Divisidero St, San Francisco, CA 94115
| | - Jason W Chan
- Department of Radiation Oncology, University of California, 1600 Divisidero St, San Francisco, CA 94115
| | - Lijun Ma
- Department of Radiation Oncology, University of California, 1600 Divisidero St, San Francisco, CA 94115
| | - Olivier Morin
- Department of Radiation Oncology, University of California, 1600 Divisidero St, San Francisco, CA 94115
| | - Sue S Yom
- Department of Radiation Oncology, University of California, 1600 Divisidero St, San Francisco, CA 94115
| | - Timothy D Solberg
- Department of Radiation Oncology, University of California, 1600 Divisidero St, San Francisco, CA 94115
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7
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Bedford JL, Tsang HS, Nill S, Oelfke U. Treatment planning optimization with beam motion modeling for dynamic arc delivery of SBRT using Cyberknife with multileaf collimation. Med Phys 2019; 46:5421-5433. [PMID: 31587322 PMCID: PMC6916282 DOI: 10.1002/mp.13848] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 09/09/2019] [Accepted: 09/23/2019] [Indexed: 12/29/2022] Open
Abstract
PURPOSE The use of dynamic arcs for delivery of stereotactic body radiation therapy (SBRT) on Cyberknife is investigated, with a view to improving treatment times. This study investigates the required modeling of robot and multileaf collimator (MLC) motion between control points in the trajectory and then uses this to develop an optimization method for treatment planning of a dynamic arc with Cyberknife. The resulting plans are compared in terms of dose-volume histograms and estimated treatment times with those produced by a conventional beam arrangement. METHODS Five SBRT patient cases (prostate A - conventional, prostate B - brachytherapy-type, lung, liver, and partial left breast) were retrospectively studied. A suitable arc trajectory with control points spaced at 5° was proposed and treatment plans were produced for typical clinical protocols. The optimization consisted of a fluence optimization, segmentation, and direct aperture optimization using a gradient descent method. Dose delivered by the moving MLC was either taken to be the dose delivered discretely at the control points or modeled using effective fluence delivered between control points. The accuracy of calculated dose was assessed by recalculating after optimization using five interpolated beams and 100 interpolated apertures between each optimization control point. The resulting plans were compared using dose-volume histograms and estimated treatment times with those for a conventional Cyberknife beam arrangement. RESULTS If optimization is performed based on discrete doses delivered at the arc control points, large differences of up to 40% of the prescribed dose are seen when recalculating with interpolation. When the effective fluence between control points is taken into account during optimization, dosimetric differences are <2% for most structures when the plans are recalculated using intermediate nodes, but there are differences of up to 15% peripherally. Treatment plan quality is comparable between the arc trajectory and conventional body path. All plans meet the relevant clinical goals, with the exception of specific structures which overlap with the planning target volume. Median estimated treatment time is 355 s (range 235-672 s) for arc delivery and 675 s (range 554-1025 s) for conventional delivery. CONCLUSIONS The method of using effective fluence to model MLC motion between control points is sufficiently accurate to provide for accurate inverse planning of dynamic arcs with Cyberknife. The proposed arcing method produces treatment plans with comparable quality to the body path, with reduced estimated treatment delivery time.
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Affiliation(s)
- James L. Bedford
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonSM2 5PTUK
| | - Henry S. Tsang
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonSM2 5PTUK
| | - Simeon Nill
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonSM2 5PTUK
| | - Uwe Oelfke
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonSM2 5PTUK
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Schlüter M, Fürweger C, Schlaefer A. Optimizing robot motion for robotic ultrasound-guided radiation therapy. ACTA ACUST UNITED AC 2019; 64:195012. [DOI: 10.1088/1361-6560/ab3bfb] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Kearney V, Chan JW, Wang T, Perry A, Yom SS, Solberg TD. Attention-enabled 3D boosted convolutional neural networks for semantic CT segmentation using deep supervision. ACTA ACUST UNITED AC 2019; 64:135001. [DOI: 10.1088/1361-6560/ab2818] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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10
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Smyth G, Evans PM, Bamber JC, Bedford JL. Recent developments in non-coplanar radiotherapy. Br J Radiol 2019; 92:20180908. [PMID: 30694086 PMCID: PMC6580906 DOI: 10.1259/bjr.20180908] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 01/15/2019] [Accepted: 01/17/2019] [Indexed: 11/05/2022] Open
Abstract
This paper gives an overview of recent developments in non-coplanar intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). Modern linear accelerators are capable of automating motion around multiple axes, allowing efficient delivery of highly non-coplanar radiotherapy techniques. Novel techniques developed for C-arm and non-standard linac geometries, methods of optimization, and clinical applications are reviewed. The additional degrees of freedom are shown to increase the therapeutic ratio, either through dose escalation to the target or dose reduction to functionally important organs at risk, by multiple research groups. Although significant work is still needed to translate these new non-coplanar radiotherapy techniques into the clinic, clinical implementation should be prioritized. Recent developments in non-coplanar radiotherapy demonstrate that it continues to have a place in modern cancer treatment.
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Affiliation(s)
- Gregory Smyth
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | | | - Jeffrey C Bamber
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - James L Bedford
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
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Bedford JL, Ziegenhein P, Nill S, Oelfke U. Beam selection for stereotactic ablative radiotherapy using Cyberknife with multileaf collimation. Med Eng Phys 2019; 64:28-36. [PMID: 30579786 PMCID: PMC6358634 DOI: 10.1016/j.medengphy.2018.12.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 08/14/2018] [Accepted: 12/12/2018] [Indexed: 11/16/2022]
Abstract
The Cyberknife system (Accuray Inc., Sunnyvale, CA) enables radiotherapy using stereotactic ablative body radiotherapy (SABR) with a large number of non-coplanar beam orientations. Recently, a multileaf collimator has also been available to allow flexibility in field shaping. This work aims to evaluate the quality of treatment plans obtainable with the multileaf collimator. Specifically, the aim is to find a subset of beam orientations from a predetermined set of candidate directions, such that the treatment quality is maintained but the treatment time is reduced. An evolutionary algorithm is used to successively refine a randomly selected starting set of beam orientations. By using an efficient computational framework, clinically useful solutions can be found in several hours. It is found that 15 beam orientations are able to provide treatment quality which approaches that of the candidate beam set of 110 beam orientations, but with approximately half of the estimated treatment time. Choice of an efficient subset of beam orientations offers the possibility to improve the patient experience and maximise the number of patients treated.
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Affiliation(s)
- James L Bedford
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5PT, UK.
| | - Peter Ziegenhein
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5PT, UK
| | - Simeon Nill
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5PT, UK
| | - Uwe Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5PT, UK
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Kearney V, Chan JW, Haaf S, Descovich M, Solberg TD. DoseNet: a volumetric dose prediction algorithm using 3D fully-convolutional neural networks. Phys Med Biol 2018; 63:235022. [PMID: 30511663 DOI: 10.1088/1361-6560/aaef74] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The goal of this study is to demonstrate the feasibility of a novel fully-convolutional volumetric dose prediction neural network (DoseNet) and test its performance on a cohort of prostate stereotactic body radiotherapy (SBRT) patients. DoseNet is suggested as a superior alternative to U-Net and fully connected distance map-based neural networks for non-coplanar SBRT prostate dose prediction. DoseNet utilizes 3D convolutional downsampling with corresponding 3D deconvolutional upsampling to preserve memory while simultaneously increasing the receptive field of the network. DoseNet was implemented on 2 Nvidia 1080 Ti graphics processing units and utilizes a 3 phase learning protocol to help achieve convergence and improve generalization. DoseNet was trained, validated, and tested with 151 patients following Kaggle completion rules. The dosimetric quality of DoseNet was evaluated by comparing the predicted dose distribution with the clinically approved delivered dose distribution in terms of conformity index, heterogeneity index, and various clinically relevant dosimetric parameters. The results indicate that the DoseNet algorithm is a superior alternative to U-Net and fully connected methods for prostate SBRT patients. DoseNet required ~50.1 h to train, and ~0.83 s to make a prediction on a 128 × 128 × 64 voxel image. In conclusion, DoseNet is capable of making accurate volumetric dose predictions for non-coplanar SBRT prostate patients, while simultaneously preserving computational efficiency.
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Kearney V, Haaf S, Sudhyadhom A, Valdes G, Solberg TD. An unsupervised convolutional neural network-based algorithm for deformable image registration. ACTA ACUST UNITED AC 2018; 63:185017. [DOI: 10.1088/1361-6560/aada66] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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14
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Kearney V, Descovich M, Sudhyadhom A, Cheung JP, McGuinness C, Solberg TD. A continuous arc delivery optimization algorithm for CyberKnife m6. Med Phys 2018; 45:3861-3870. [PMID: 29855038 DOI: 10.1002/mp.13022] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 05/01/2018] [Accepted: 05/01/2018] [Indexed: 11/10/2022] Open
Abstract
PURPOSE This study aims to reduce the delivery time of CyberKnife m6 treatments by allowing for noncoplanar continuous arc delivery. To achieve this, a novel noncoplanar continuous arc delivery optimization algorithm was developed for the CyberKnife m6 treatment system (CyberArc-m6). METHODS AND MATERIALS CyberArc-m6 uses a five-step overarching strategy, in which an initial set of beam geometries is determined, the robotic delivery path is calculated, direct aperture optimization is conducted, intermediate MLC configurations are extracted, and the final beam weights are computed for the continuous arc radiation source model. This algorithm was implemented on five prostate and three brain patients, previously planned using a conventional step-and-shoot CyberKnife m6 delivery technique. The dosimetric quality of the CyberArc-m6 plans was assessed using locally confined mutual information (LCMI), conformity index (CI), heterogeneity index (HI), and a variety of common clinical dosimetric objectives. RESULTS Using conservative optimization tuning parameters, CyberArc-m6 plans were able to achieve an average CI difference of 0.036 ± 0.025, an average HI difference of 0.046 ± 0.038, and an average LCMI of 0.920 ± 0.030 compared with the original CyberKnife m6 plans. Including a 5 s per minute image alignment time and a 5-min setup time, conservative CyberArc-m6 plans achieved an average treatment delivery speed up of 1.545x ± 0.305x compared with step-and-shoot plans. CONCLUSIONS The CyberArc-m6 algorithm was able to achieve dosimetrically similar plans compared to their step-and-shoot CyberKnife m6 counterparts, while simultaneously reducing treatment delivery times.
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Affiliation(s)
- Vasant Kearney
- Department of Radiation Oncology, University of California, San Francisco, CA, USA
| | - Martina Descovich
- Department of Radiation Oncology, University of California, San Francisco, CA, USA
| | - Atchar Sudhyadhom
- Department of Radiation Oncology, University of California, San Francisco, CA, USA
| | - Joey P Cheung
- Department of Radiation Oncology, University of California, San Francisco, CA, USA
| | | | - Timothy D Solberg
- Department of Radiation Oncology, University of California, San Francisco, CA, USA
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15
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Moustakis C, Chan MKH, Kim J, Nilsson J, Bergman A, Bichay TJ, Palazon Cano I, Cilla S, Deodato F, Doro R, Dunst J, Eich HT, Fau P, Fong M, Haverkamp U, Heinze S, Hildebrandt G, Imhoff D, de Klerck E, Köhn J, Lambrecht U, Loutfi-Krauss B, Ebrahimi F, Masi L, Mayville AH, Mestrovic A, Milder M, Morganti AG, Rades D, Ramm U, Rödel C, Siebert FA, den Toom W, Wang L, Wurster S, Schweikard A, Soltys SG, Ryu S, Blanck O. Treatment planning for spinal radiosurgery : A competitive multiplatform benchmark challenge. Strahlenther Onkol 2018; 194:843-854. [PMID: 29802435 DOI: 10.1007/s00066-018-1314-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 05/08/2018] [Indexed: 12/20/2022]
Abstract
PURPOSE To investigate the quality of treatment plans of spinal radiosurgery derived from different planning and delivery systems. The comparisons include robotic delivery and intensity modulated arc therapy (IMAT) approaches. Multiple centers with equal systems were used to reduce a bias based on individual's planning abilities. The study used a series of three complex spine lesions to maximize the difference in plan quality among the various approaches. METHODS Internationally recognized experts in the field of treatment planning and spinal radiosurgery from 12 centers with various treatment planning systems participated. For a complex spinal lesion, the results were compared against a previously published benchmark plan derived for CyberKnife radiosurgery (CKRS) using circular cones only. For two additional cases, one with multiple small lesions infiltrating three vertebrae and a single vertebra lesion treated with integrated boost, the results were compared against a benchmark plan generated using a best practice guideline for CKRS. All plans were rated based on a previously established ranking system. RESULTS All 12 centers could reach equality (n = 4) or outperform (n = 8) the benchmark plan. For the multiple lesions and the single vertebra lesion plan only 5 and 3 of the 12 centers, respectively, reached equality or outperformed the best practice benchmark plan. However, the absolute differences in target and critical structure dosimetry were small and strongly planner-dependent rather than system-dependent. Overall, gantry-based IMAT with simple planning techniques (two coplanar arcs) produced faster treatments and significantly outperformed static gantry intensity modulated radiation therapy (IMRT) and multileaf collimator (MLC) or non-MLC CKRS treatment plan quality regardless of the system (mean rank out of 4 was 1.2 vs. 3.1, p = 0.002). CONCLUSIONS High plan quality for complex spinal radiosurgery was achieved among all systems and all participating centers in this planning challenge. This study concludes that simple IMAT techniques can generate significantly better plan quality compared to previous established CKRS benchmarks.
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Affiliation(s)
- Christos Moustakis
- Department of Radiation Oncology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Münster, Germany.
| | - Mark K H Chan
- Department of Radiation Oncology, University Clinic Schleswig-Holstein, Kiel, Germany
| | - Jinkoo Kim
- Department of Radiation Oncology, Stony Brook University Hospital, Stony Brook, NY, USA
| | - Joakim Nilsson
- Department of Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Alanah Bergman
- Vancouver Cancer Centre, Department of Medical Physics, BC Cancer Agency, Vancouver, BC, Canada
| | - Tewfik J Bichay
- Lacks Cancer Center, Department of Radiation Oncology, Mercy Health Saint Mary's, Grand Rapids, MI, USA.,Wayne State University School of Medicine, Detroit, MI, USA
| | | | - Savino Cilla
- Fondazione di Ricerca e Cura "Giovanni Paolo II", Medical Physics Unit, Catholic University of Sacred Heart, Campobasso, Italy
| | - Francesco Deodato
- Fondazione di Ricerca e Cura "Giovanni Paolo II", Radiation Oncology Unit, Catholic University of Sacred Heart, Campobasso, Italy
| | - Raffaela Doro
- Department of Medical Physics and Radiation Oncology, IFCA, Firenze, Italy
| | - Jürgen Dunst
- Department of Radiation Oncology, University Clinic Schleswig-Holstein, Kiel, Germany.,Department of Radiation Oncology, University Clinic Copenhagen, Copenhagen, Denmark
| | - Hans Theodor Eich
- Department of Radiation Oncology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Münster, Germany
| | - Pierre Fau
- University of Aix Marseille, Marseille, France.,Physics Department, Institut Paoli Calmettes, Marseille, France
| | - Ming Fong
- Vancouver Cancer Centre, Department of Radiation Therapy, BC Cancer Agency, Vancouver, BC, Canada
| | - Uwe Haverkamp
- Department of Radiation Oncology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Münster, Germany
| | - Simon Heinze
- Department of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Guido Hildebrandt
- Department of Radiation Oncology, University Medicine Rostock, Rostock, Germany
| | - Detlef Imhoff
- Department of Radiation Oncology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Erik de Klerck
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Janett Köhn
- Department of Radiation Oncology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Ulrike Lambrecht
- Department of Radiation Oncology, University Clinic Erlangen, Erlangen, Germany
| | - Britta Loutfi-Krauss
- Department of Radiation Oncology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Fatemeh Ebrahimi
- Department of Radiation Oncology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Münster, Germany
| | - Laura Masi
- Department of Medical Physics and Radiation Oncology, IFCA, Firenze, Italy
| | - Alan H Mayville
- Lacks Cancer Center, Department of Radiation Oncology, Mercy Health Saint Mary's, Grand Rapids, MI, USA
| | - Ante Mestrovic
- Vancouver Cancer Centre, Department of Medical Physics, BC Cancer Agency, Vancouver, BC, Canada
| | - Maaike Milder
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Alessio G Morganti
- Radiation Oncology Department, DIMES University of Bologna-S. Orsola Malpighi Hospital, Bologna, Italy
| | - Dirk Rades
- Department of Radiation Oncology, University Clinic Schleswig-Holstein, Lübeck, Germany
| | - Ulla Ramm
- Department of Radiation Oncology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Claus Rödel
- Department of Radiation Oncology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Frank-Andre Siebert
- Department of Radiation Oncology, University Clinic Schleswig-Holstein, Kiel, Germany
| | - Wilhelm den Toom
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Lei Wang
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Stefan Wurster
- Saphir Radiosurgery Center, Northern Germany and Frankfurt, Güstrow, Germany.,Department of Radiation Oncology, University Medicine Greifswald, Greifswald, Germany
| | - Achim Schweikard
- Institute for Robotic and Cognitive Systems, University of Lübeck, Lübeck, Germany
| | - Scott G Soltys
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Samuel Ryu
- Department of Radiation Oncology, Stony Brook University Hospital, Stony Brook, NY, USA
| | - Oliver Blanck
- Department of Radiation Oncology, University Clinic Schleswig-Holstein, Kiel, Germany.,Saphir Radiosurgery Center, Northern Germany and Frankfurt, Güstrow, Germany
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