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Browse through our publications to learn more about our products, our research, and our company.

White Paper: Machine learning automated treatment planning
White Paper: Evaluation of Robustness in RayStation
White Paper: Machine learning automated treatment planning
White Paper: Deep-learning segmentation
White Paper: Micro-RayStation in Pre-Clinical Research
White Paper: MCO for helical TomoTherapy
White Paper: Deformable registration in RayStation
White Paper: Multi-criteria optimization in RayStation
White Paper: Constant Dose Rate VMAT in RayStation
White Paper: Robust optimization in RayStation
White Paper: Biological optimization in RayStation
White Paper: Scripting in RayStation
White Paper: VMAT optimization in RayStation
White Paper: Reduce organ at risk dose in RayStation
White Paper: MCO for helical TomoTherapy
White Paper: The effect of planning speed on VMAT plan quality
White Paper: Proton Monte Carlo dose calculation in RayStation

Deformable registration

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 Research, 58(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

Dose calculation on CBCT

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

Dose tracking

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


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

MR planning

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


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

VMAT optimization

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

Machine learning


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

Multi criteria optimization

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

Robust optimization

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., (2916). 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

Photon dose computation

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

Proton dose computation

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

Carbon dose computation

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.

Electron dose computation

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

Micro RayStation

Chiavassa, S., et al., (2108) µ-RayStation: an adaptation of RayStation 5 for small animal radiotherapy, Poster at ESTRO37 Barcelona. Link to poster