SCIENTIFIC PUBLICATIONS

Commissioning of Helium Ion Therapy and the First Patient Treatment with Active Beam Delivery

Thomas Tessonnier, PhD, Swantje Ecker, MSc, Judith Besuglow, MSc, Jürgen Debus, MD, PhD, Oliver Jäkel, PhD , Andrea Mairani, et. al

Published: January 18, 2023 DOI:https://doi.org/10.1016/j.ijrobp.2023.01.015

 

In silico benchmarking of the linear energy transfer-based functionalities for carbon ion beams in a commercial treatment planning system

Mansure Schafasand, Andreas Franz Resch, Erik Traneus, Lars Glimelius, Piero Fossati, Markus Stock, Joanna Gora, Dietmar Georg, Antonio Carlino, et. al

First published: December 19, 2022 https://doi.org/10.1002/mp.16174

Modelling tissue specific RBE for different radiation qualities based on a multiscale characterization of energy deposition

Erik Almhagen, Fernanda Villegas, Nina Tilly, Lars Glimelius, Erik Traneus, Anders Ahnesjö, et. al

Published: February 16, 2023 DOI:https://doi.org/10.1016/j.radonc.2023.109539

ANACONDA algorithm.

The ANACONDA algorithm for deformable image registration in radiotherapy.
Weistrand, O., & Svensson, S. (2014).
Medical Physics, 42(1), 40–53. https://doi.org/10.1118/1.4894702

Morfeus algorithm.

Accuracy of finite element model-based multi-organ deformable image registration.
Brock, K. K., Sharpe, M. B., Dawson, L. a, Kim, S. M., & Jaffray, D. a. (2005). 
Medical Physics, 32(6), 1647–1659. https://doi.org/10.1118/1.1915012

Validation of biomechanical deformable image registration in the abdomen, thorax, and pelvis in a commercial radiotherapy treatment planning system.
Velec, M., Moseley, J. L., Svensson, S., Hårdemark, B., Jaffray, D. A., & Brock, K. K. (2017).
Medical Physics, 44(7), 3407–3417. https://doi.org/10.1002/mp.12307

Optimization of mesh generation for geometric accuracy, robustness, and efficiency of biomechanical-model-based deformable image registration.
He, Y., Anderson, B.M., Cazoulat, G., Rigaud, B., Almodovar-Abreu, L., Pollard-Larkin, J., Balter, P., Liao, Z., Mohan, R., Odisio, B., Svensson, S., Brock, K.K.
Medical Physics, 50(1):323-329 (2023), https://doi.org/10.1002/mp.15939

Independent validation of ANACONDA

Validation of a deformable image registration produced by a commercial treatment planning system in head and neck.
García-Mollá, R., Marco-Blancas, N. De, Bonaque, J., Vidueira, L., López-Tarjuelo, J., & Perez-Calatayud, J. (2015).
Physica Medica, 31(3), 219–223. http://doi.org/10.1016/j.ejmp.2015.01.007

Multi-institutional Validation Study of Commercially Available Deformable Image Registration Software for Thoracic Images.
Kadoya, N., Nakajima, Y., Saito, M., Miyabe, Y., Kurooka, M., Kito, S., … Jingu, K. (2016).
International Journal of Radiation Oncology*Biology*Physics, 96(2), 422–431. http://doi.org/10.1016/j.ijrobp.2016.05.012

Performance of commercially available deformable image registration platforms for contour propagation using patient-based computational phantoms: A multi-institutional study.
Loi, G., Fusella, M., Lanzi, E., Cagni, E., Garibaldi, C., Iacoviello, G., … Fiandra, C. (2018). 
Medical Physics, 45(2), 748–757. https://doi.org/10.1002/mp.12737

Usefulness of hybrid deformable image registration algorithms in prostate radiation therapy.
Motegi, K., Tachibana, H., Motegi, A., Hotta, K., Baba, H., & Akimoto, T. (2019). Journal of Applied Clinical Medical Physics, 20(1), 229–236. https://doi.org/10.1002/acm2.12515

Evaluation of the performance of deformable image registration between planning CT and CBCT images for the pelvic region: comparison between hybrid and intensity-based DIR.
Takayama, Y., Kadoya, N., Yamamoto, T., Ito, K., Chiba, M., Fujiwara, K., … Jingu, K. (2017). 
Journal of Radiation Research, 58(4), 567–571. https://doi.org/10.1093/jrr/rrw123

Independent validation of ANACONDA and RS-Morfeus.

The impact of robustness of deformable image registration on contour propagation and dose accumulation for head and neck adaptive radiotherapy.
Zhang, L., Wang, Z., Shi, C., Long, T., & Xu, X. G. (2018).
Journal of Applied Clinical Medical Physics, 19(4), 185–194. https://doi.org/10.1002/acm2.12361

Feasibility of CBCT-based dose with a patient-specific stepwise HU-to-density curve to determine time of replanning.
Chen, S., Le, Q., Mutaf, Y., Lu, W., Nichols, E. M., Yi, B. Y., … D’Souza, W. D. (2017). 
Journal of Applied Clinical Medical Physics, (May), 1–6. https://doi.org/10.1002/acm2.12127

"CBCT-based dose calculation in RayStation has helped us streamline our adaptive workflows and ensure that treatment plans are delivered as intended.”

This work examines the dosimetric performance of two algorithms creating a corrected CBCT (corrCBCT) and a virtual CT (vCT) implemented in a commercial treatment planning system.

Evaluation of CBCT based dose calculation in the thorax and pelvis using two generic algorithms

 

Comparing Delivered and Planned Radiation Therapy Doses Using Deformable Image Registration and Dose Accumulation for Locally Advanced Non–small Cell Lung Cancer.
Marshall, A., Kong, V. C., Chan, B., Moseley, J. L., Sun, A., Lindsay, P. E., & Bissonnette, J. P. (2017). 
International Journal of Radiation Oncology*Biology*Physics, 99(2), E696–E697. https://doi.org/10.1016/j.ijrobp.2017.06.2280

Dose tracking assessment for image-guided radiotherapy of the prostate bed and the impact on clinical workflow.
Orlandini, L. C., Coppola, M., Fulcheri, C., Cernusco, L., Wang, P., & Cionini, L. (2017).
Radiation Oncology, 12(1), 78. https://doi.org/10.1186/s13014-017-0815-y

Evaluation of rectum and bladder dose accumulation from external beam radiotherapy and brachytherapy for cervical cancer using two different deformable image registration techniques.
Kadoya, N., Miyasaka, Y., Yamamoto, T., Kuroda, Y., Ito, K., Chiba, M., … Jingu, K. (2017).
Journal of Radiation Research, 58(5), 720–728. https://doi.org/10.1093/jrr/rrx028

When your MR linac is down: Can an automated pipeline bail you out of trouble?
L. Placidi, D. Cusumano, A. Alparone, l. Boldrini, M. Nardini, G. Meffe, G. Chiloiro, A. Romano, V. Valentini, L. Indovina, Physica Medica 91 (2021) 80-86 https://www.sciencedirect.com/science/article/pii/S112017972100329X?dgcid=coauthor

Assessment of fully-automated atlas-based segmentation of novel oral mucosal surface organ-at-risk.
Dean, J. A., Welsh, L. C., McQuaid, D., Wong, K. H., Aleksic, A., Dunne, E., … Nutting, C. M. (2016).
Radiotherapy and Oncology, 119(1), 166–171. https://doi.org/10.1016/j.radonc.2016.02.022

Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region.
Kieselmann, J. P., Kamerling, C. P., Burgos, N., Menten, M. J., Fuller, C. D., Nill, S., … Oelfke, U. (2018).
Physics in Medicine & Biology, 63(14), 145007. https://doi.org/10.1088/1361-6560/aacb65

Training, validation, and clinical implementation of a deep-learning segmentation model for radiotherapy of loco-regional breast cancer
Almberg, S.S., Lervåg, C., Frengen, J., Eidem, M., Abramova, T.M., Nordstrand, C.S., Alsaker, M.D., Tøndel, H., Raj, S.X., Wanderås, A.D.
Radiotherapy and Oncology, 173:62-68 (2022), https://doi.org/10.1016/j.radonc.2022.05.018

Technical Note: Efficient and accurate MRI-only based treatment planning of the prostate using bulk density assignment through atlas-based segmentation
McCallum, H.M., Andersson, S., Wyatt, J. J., Richmond, N., Walker, C. WP., Svensson, S.
Medical Physics, 47(10):4758-4762 (2020), https://doi.org/10.1002/mp.14406

Fast robust optimization of proton PBS arc therapy plans using early energy layer selection and spot assignment.
Erik Engwall, Cecilia Battinelli, Viktor Wase, Otte Marthin, Lars Glimelius, Rasmus Bokrantz, Björn Andersson and Albin Fredriksson (2022).
Physics in Medicine & Biology, vol 67, no 6. https://iopscience.iop.org/article/10.1088/1361-6560/ac55a6

________________________

PTCOG 2022
Robustness in proton arc treatments for head and neck cancer patients: impact of gantry angle spacing and number of revolutions

Authors:
Marthin, Otte; Wase, Viktor; Glimelius, Lars; Bokrantz, Rasmus; Andersson, Björn; Fredriksson, Albin; de Jong, Bas; Korevaar, Erik W.; Both, Stefan; Engwall, Erik 

Link to poster

Automated planning of tangential breast intensity-modulated radiotherapy using heuristic optimization.
Purdie, T. G., Dinniwell, R. E., Letourneau, D., Hill, C., & Sharpe, M. B. (2011). 
International Journal of Radiation Oncology Biology Physics, 81(2), 575–583. https://doi.org/10.1016/j.ijrobp.2010.11.016

Automation and Intensity Modulated Radiation Therapy for Individualized High-Quality Tangent Breast Treatment Plans.
Purdie TG, Dinniwell RE, Fyles A., & Sharpe, M. B. (2014). 
International Journal of Radiation Oncology Biology Physics, 90, 688–695. https://doi.org/10.1016/j.ijrobp.2014.06.056

Reduction of heart and lung normal tissue complication probability using automatic beam angle optimization and more generic optimization objectives for breast radiotherapy.
Nienke Bakx, Hanneke Bluemink, Els Hagelaar, Jorien van der Leer, Maurice van der Sangen, Jacqueline Theuws, Coen Hurkmans (2021).
Physics and Imaging in Radiation Oncology 18 (2021) 48–50. https://phiro.science/article/S2405-6316(21)00022-1/fulltext

Development and evaluation of an efficient approach to volumetric arc therapy planning
Bzdusek, K., et al., (2009).
Medical Physics 36 (6) 2328-39. https://doi.org/10.1118/1.3132234

Planning

Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer.
Chris McIntosh, Leigh Conroy, Michael C. Tjong, Tim Craig, Andrew Bayley, Charles Catton, Mary Gospodarowicz, Joelle Helou, Naghmeh Isfahanian, Vickie Kong, Tony Lam, Srinivas Raman, Padraig Warde, Peter Chung, Alejandro Berlin, Thomas G. Purdie (2021).
Nature Medicine 27, pages 999–1005. https://www.nature.com/articles/s41591-021-01359-w

Development and evaluation of radiotherapy deep learning dose prediction models for breast cancer.
Nienke Bakx, Hanneke Bluemink, Els Hagelaar, Maurice van der Sangen, Jacqueline Theuws, Coen Hurkmans (2020). Physics and Imaging in Radiation Oncology 17, 65-70. https://phiro.science/article/S2405-6316(21)00006-3/fulltext

Contextual Atlas Regression Forests: Multiple-Atlas-Based Automated Dose Prediction in Radiation Therapy
McIntosh, C., Purdie, T. G. (2016).
IEEE Transactions on Medical Imaging, 35(4), 1000-1012. https://doi.org/10.1109/TMI.2015.2505188

Voxel-based dose prediction with multi-patient atlas selection for automated radiotherapy treatment planning.
McIntosh, C., Purdie, T. G. (2017).
Physics in Medicine & Biology, 62(2), 415-431. https://doi.org/10.1088/1361-6560/62/2/415

Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method.
McIntosh, C., Welch, M., McNiven, A., Jaffray, D.A., Purdie, T.G. (2017).
Physics in Medicine & Biology, 62(15), 5926-5944. https://doi.org/10.1088/1361-6560/aa71f8

Multi-criteria optimization achieves superior normal tissue sparing in a planning study of intensity-modulated radiation therapy for RTOG 1308-eligible non-small cell lung cancer patients.
Kamran, S.C., et al., (2016).
Radiotherapy  Oncolology 118 (3) 515-520. https://doi.org/10.1016/j.radonc.2015.12.028

Improved planning time and plan quality through multicriteria optimization for intensity-modulated radiotherapy
Craft, D. L., et al., (2012). 
International Journal of Radiation Oncology Biology Physics 82(1), e83-e90. https://doi.org/10.1016/j.ijrobp.2010.12.007

Multicriteria optimization enables less experienced planner to efficiently produce high quality treatment plans in head and neck cancer radiotherapy
Kierkels, R.G., et al., (2015). 
Radiation Oncoloy 10:87. https://doi.org/10.1186/s13014-015-0385-9

Multicriteria optimization for managing tradeoffs in radiation therapy treatment planning, PhD thesis
Bokrantz, R., (2013).
KTH Royal Institute of Technology, Stockholm. https://www.raysearchlabs.com/globalassets/about-overview/media-center/wp-re-ev-n-pdfs/publications/doctoral-thesis-multicriteria-optimization_rasmus_bokrantz_2013.pdf

Robust radiation therapy optimization using simulated treatment courses for handling deformable organ motion
Fredriksson A, Engwall E, Andersson B, (2021).
Physics in Medicine & Biology 66, 055010 https://iopscience.iop.org/article/10.1088/1361-6560/abd591/pdf

Composite minimax robust optimization of VMAT improves target coverage and reduces non-target dose in head and neck cancer patients
Wagenaar, D., et al., (2019).
Radiotherapy and Oncology 136, 71-77. https://doi.org/10.1016/j.radonc.2019.03.019

Robust Intensity Modulated Proton Therapy (IMPT) Increases Estimated Clinical Benefit in Head and Neck Cancer Patients
van Dijk, L.V., et al., (2016).
PLoS One 11(3) e0152477. https://doi.org/10.1371/journal.pone.0152477

Multi-scenario based robust intensity-modulated proton therapy (IMPT) plans can account for set-up errors more effectively in terms of normal tissue sparing than planning target volume (PTV) based intensity-modulated photon plans in the head and neck region
Stuschke, M., et al., (2013).
Radiation Oncology 18(8) 145. https://doi.org/10.1186/1748-717X-8-145

Robust optimization of radiation therapy accounting for geometric uncertainty, PhD thesis
Fredriksson, R., (2013).
KTH Royal Insitute of Technology.

Robust radiotherapy planning.
Unkelbach, J., et.al., (2018).
Physics in Medicine & Biology, 63(22), 22TR02. https://doi.org/10.1088/1361-6560/aae659

4D strategies for lung tumors treated with hypofractionated scanning proton beam therapy: Dosimetric impact and robustness to interplay effects
Mastella, E., et al., (2020 )
Radiotherapy and Oncology 146, 213-220. https://doi.org/10.1016/j.radonc.2020.02.025

Improving the modelling of a multi-leaf collimator with tilted lead sides used in radiotherapy
Hussein M, Angerud A, Saez J, Bogaert E, Lemire M, Barry M, Patallo I S, Shipley D, Clark C H, Hernandez V, Physics and Imaging in Radiation Oncology, 29 (2024) 100543
https://doi.org/10.1016/j.phro.2024.100543

Comparison of the RayStation photon Monte Carlo dose calculation algorithm against data under homogenous and heterogeneous irradiation geometries
Richmond N, Angerud A, Tamm F, Allen V
Physica Medica 82 (2021) 87-99. https://doi.org/10.1016/j.ejmp.2021.02.002 
https://www.sciencedirect.com/science/article/pii/S1120179721000909?dgcid=coauthor

Evaluation of dose distribution differences from five algorithms implemented in three commercial treatment planning systems for lung SBRT
Sarkar V, Paxton A, Rassaih P, Kokeny KE, Hitchcock YJ, Salter BJ
J Radiosurg SBRT (2020); 7(1): 57-66. https://pubmed.ncbi.nlm.nih.gov/32802579/

Unlocking a closed system: dosimetric commissioning of a ring gantry linear accelerator in a multivendor environment
Saini A, et al.
Journal of Applied Clinical Medical Phyics (2020); 1-4. https://doi.org/10.1002/acm2.13116

Dosimetric leaf gap and leaf trailing effect in a double-stacked multileaf collimator
Hernandez V, Saez J, Angerud A, Cayez R, Khamphan C, Nguyen D, Vieillevigne L, Feygelman V,
Med. Phys. 48 (2021) 3413-3424. doi: 10.1002/mp.14914. https://pubmed.ncbi.nlm.nih.gov/33932237/

The coupled photon/electron Monte Carlo transport code in the RayStation treatment planning system
Tamm, F., et al., (2019).
Poster at MCMA Montreal. Link to poster

Optimizing beam models for dosimetric accuracy over a wide range of treatments
Chen, J., et al., (2019)
Physica Medica 58, 47-53. https://doi.org/10.1016/j.ejmp.2019.01.011

Modeling and dosimetric performance evaluation of the RayStation treatment planning system
Mzenda, B., et al., (2014).
Journal of Applied Clinical Medical Physics 15(5) 4787. https://doi.org/10.1120/jacmp.v15i5.4787

Modeling of flattening filter free photon beams with analytical and Monte Carlo TPS
Valdenaire, S., Mailleux, H., Fau, P., (2016).
Biomedical Physics & Engineering Express 2(3) 035010. https://doi.org/10.1088/2057-1976/2/3/035010

Initial characterization, dosimetric benchmark and performance validation of Dynamic Wave Arcs
Burghela, M., et al., (2016).
Radiation Oncology 11:63. https://doi.org/10.1186/s13014-016-0633-7

A novel procedure for determining the optimal MLC configuration parameters in treatment planning systems based on measurements with a Farmer chamber
Saez, J. et al., (2020).
Accepted for publication in Physics in Medicine and Biology. https://doi.org/10.1088/1361-6560/ab8cd5

Maintaining dosimetric quality when switching to a Monte Carlo dose engine for head and neck volumetric-modulated arc therapy planning.
Feygelman V, Latifi K, Bowers M, Greco K, Moros EG, Isacson M, Angerud A, Caudell J (2022). https://pubmed.ncbi.nlm.nih.gov/35213089/

Clinical validation of a GPU-based Monte Carlo dose engine of a commercial treatment planning system for pencil beam scanning proton therapy
Francesco Fracchiolla, Erik Engwall, Martin Janson, Fredrik Tamm, Stefano Lorentini, Francesco Fellin, Mattia Bertolini, Carlo Algranati, Roberto Righetto, Paolo Farace, Maurizio Amichetti, Marco Schwarz.
Physica Medica, 23 July (2021). https://doi.org/10.1016/j.ejmp.2021.07.012

Pencil Beam Algorithms Are Unsuitable for Proton Dose Calculations in Lung
Taylor, P.A., Kry, S.F., Followill, D.S., (2017).
International Journal of Radiation Oncology Biology Physics 99(3), 750–756. https://doi.org/10.1016/j.ijrobp.2017.06.003

Improvements in pencil beam scanning proton therapy dose calculation in brain tumor cases with a commercial Monte Carlo algorithm
Widesott, L., et al., (2018).
Physics in Medicine & Biology, 63(14), 145016. https://doi.org/10.1088/1361-6560/aac279

Small field aperture validation of the RayStation proton pencil beam scanning Monte Carlo algorithm.
Blakey, M., et al., (2018).
Poster at PTCOG57 Prague. Link to poster

Dosimetric validation of the Monte Carlo dose engine in the treatment planning system RayStation for scanned proton field including apertures
Janson, M.S., et al., (2018).
Poster at PTCOG58 Cincinnati. Link to poster

Consideration of the Bragg peak detector size in the modeling of proton PBS machines in the treatment planning system RayStation
Janson, M.S., et al., (2019).
Poster at PTCOG59 Manchester. Link to poster

Validation of the RayStation Monte Carlo dose calculation algorithm using realistic animal tissue phantoms
Schreuder et. al, (2019).
J. Appl. Clin. Med. Phys. (2019) Sep 21, https://doi.org/10.1002/acm2.12733

Validation of the RayStation Monte Carlo dose calculation algorithm using a realistic lung phantom
Schreuder et. al, (2019).
J. Appl. Clin. Med. Phys. (2019) Nov 25, https://doi.org/10.1002/acm2.12777

Dosimetric evaluation of a commercial proton spot scanning Monte-Carlo dose algorithm: Comparisons against measurements and simulations
Saini et. al., (2017)
Phys. Med. Biol. 62(19), https://doi.org/10.1088/1361-6560/aa82a5

Evaluation of patient positional reproducibility on the treatment couch and its impact on dose distribution using rotating gantry system in scanned carbon-ion beam therapy
Kanai, T., Furuichi, W., Mori, S., (2019).
Physica Medica 57, 160-168, https://doi.org/10.1016/j.ejmp.2018.12.013.

RayStation Monte Carlo application: evaluation of electron calculations with entry obliquity
Archibald-Heeren, B., Liu, G., (2016).
Australasian Physical & Engineering Sciences in Medicine 39 (2):441-452. https://doi.org/10.1007/s13246-016-0437-y

An assessment on the use of RadCalc to verify RayStation Electron Monte Carlo plans
Hu, Y., et al., (2016).
Australasian Physical & Engineering Sciences in Medicine 39 (3): 735-745. https://doi.org/10.1007/s13246-016-0470-x

Evaluation of a commercial Monte Carlo calculation algorithm for electron treatment planning
Huang, J.Y., et al., (2019).
Journal of Applied Clinical Medical Physics 20:6, 184-193. https://doi.org/10.1002/acm2.12622

Measurement-based validation of a commercial Monte Carlo dose calculation algorithm for electron beams
Pittomvils G., et al (2022).
The International Journal of Medical Physics Research and Practice. https://doi.org/10.1002/mp.15685

See all scientific publiations on the product page here. 

Validation and practical implementation of seated position radiotherapy in a commercial TPS for proton therapy
Dominic Maes, Martin Janson, Rajesh Regmi, Alexander Egan, Anatoly Rosenfeld, Charles Bloch, Tony Wong, Jatinder Saini (2020).
Physica Medica 80, 175–185. https://doi.org/10.1016/j.ejmp.2020.10.027

Automatic planning for nasopharyngeal carcinoma based on progressive optimization in RayStation treatment planning system.
Yiweii Yang, Kainan Shao, Jie Zhang, Ming Chen, Yuanyuan Chen and Guoping Shan (2020).
Technology in Cancer Research & Treatment Volume 19: 1-8. https://journals.sagepub.com/doi/pdf/10.1177/1533033820915710

Effectiveness of different rescanning techniques for scanned proton radiotherapy in lung cancer patients.
Erik Engwall, Lars Glimelius and Elin Hynning (2018). 
Physics in Medicine & Biology, Volume 63, Number 9 https://iopscience.iop.org/article/10.1088/1361-6560/aabb7b

4D robust optimization including uncertainties in time structures can reduce the interplay effect in proton pencil beam scanning radiation therapy.
Erik Engwall, Albin Fredriksson, Lars Glimelius (2018).
Medical Physics, Volume 45, issue 9.
https://doi.org/10.1002/mp.13094

Dose summation and image registration strategies for radiobiologically and anatomically corrected dose accumulation in pelvic re-irradiation.
Mike Nix, Stephen Gregory, Michael Aldred, Lynn Aspin, John Lilley, Bashar Al-Qaiseh, Julien Uzan, Stina Svensson, Peter Dickinson, Ane L. Appelt, Louise Murray (2021).
Acta Oncol. 2021 Sep 29;1-9. doi: 10.1080/0284186X.2021.1982145. https://pubmed.ncbi.nlm.nih.gov/34586938/

Impact of beam properties for uveal melanoma proton therapy—An in silico planning study.
Jörg Wulff, Benjamin Koska, Martin Janson, Christian Bäumer, Andrea Denker, Dirk Geismar, Johannes Gollrad, Beate Timmermann, Jens Heufelder. (2022).
Medical Physics: https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.15573

Treatment of ocular tumors through a novel applicator on a conventional proton pencil beam scanning beamline. Nature: Treatment of ocular tumors through a novel applicator on a conventional proton pencil beam scanning beamline.
Rajesh Regmi, Dominic Maes, Alexander Nevitt, Allison Toltz, Erick Leuro, Jonathan Chen, Lia Halasz, Ramesh Rengan, Charles Bloch & Jatinder Saini. (2022). 

Until today, the majority of ocular proton treatments worldwide were planned with the EYEPLAN treatment planning system (TPS). Recently, the commercial, computed tomography (CT)-based TPS for ocular proton therapy RayOcular was released, which follows the general concepts of modelbased treatment planning approach in conjunction with a pencil-beam-type dose algorithm (PBA).

Commissioning and validation of a novel commercial TPS for ocular proton therapy