We use cookies to help provide you with the best possible online experience. Learn more


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

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

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

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

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

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

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

Takayama, Y., Kadoya, N., Yamamoto, T., Ito, K., Chiba, M., Fujiwara, K., … Jingu, K. (2017). 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. Journal of Radiation Research58(4), 567–571. https://doi.org/10.1093/jrr/rrw123

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

Chen, S., Le, Q., Mutaf, Y., Lu, W., Nichols, E. M., Yi, B. Y., … D’Souza, W. D. (2017). Feasibility of CBCT-based dose with a patient-specific stepwise HU-to-density curve to determine time of replanning. 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


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

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

Kadoya, N., Miyasaka, Y., Yamamoto, T., Kuroda, Y., Ito, K., Chiba, M., … Jingu, K. (2017). Evaluation of rectum and bladder dose accumulation from external beam radiotherapy and brachytherapy for cervical cancer using two different deformable image registration techniques. 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

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

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

McCallum H., Richmond N., Walker C., Andersson S., Svensson S. (2019). Feasibility of MR-only planning in a commercial treatment planning system, ESTRO 2019.
Link to poster

Erik Engwall, Cecilia Battinelli, Viktor Wase, Otte Marthin, Lars Glimelius, Rasmus Bokrantz, Björn Andersson and Albin Fredriksson (2022). Fast robust optimization of proton PBS arc therapy plans using early energy layer selection and spot assignment. Physics in Medicine & Biology, vol 67, no 6. 

Physics in Medicine & Biology


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

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

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

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

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

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


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). Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer. Nature Medicine 27, pages 999–1005.

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

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

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

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

Kamran, S.C., et al., (2016). 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. Radiotherapy  Oncolology 118 (3) 515-520. https://doi.org/10.1016/j.radonc.2015.12.028

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

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

Bokrantz, R., (2013). Multicriteria optimization for managing tradeoffs in radiation therapy treatment planning, PhD thesis, 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

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

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

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

Stuschke, M., et al., (2013). 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, Radiation Oncology 18(8) 145. https://doi.org/10.1186/1748-717X-8-145

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Kanai, T., Furuichi, W., Mori, S., (2019). 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, Physica Medica 57, 160-168, https://doi.org/10.1016/j.ejmp.2018.12.013.

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

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

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

Pittomvils G., et al (2022). Measurement-based validation of a commercial Monte Carlo dose calculation algorithm for electron beams, 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

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

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

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

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

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). Dose summation and image registration strategies for radiobiologically and anatomically corrected dose accumulation in pelvic re-irradiation. Acta Oncol. 2021 Sep 29;1-9. doi: 10.1080/0284186X.2021.1982145. https://pubmed.ncbi.nlm.nih.gov/34586938/

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

Rajesh Regmi, Dominic Maes, Alexander Nevitt, Allison Toltz, Erick Leuro, Jonathan Chen, Lia Halasz, Ramesh Rengan, Charles Bloch & Jatinder Saini. (2022). 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.

Treatment of ocular tumors through a novel applicator on a conventional proton pencil beam scanning beamline

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