CERR: A computational environment for radiotherapy research. CERR (pronounced ’sir’) is a software platform for developing and sharing research results in radiation therapy treatment planning. CERR is written in the widely-used Matlab language (version 7.0 or later), allowing for low-cost development of visualization and analysis tools. CERR will import and display treatment plans from a wide variety of commercial or academic treatment planning systems (including both the RTOG format and DICOM-RT format). CERR provides a common filetype for the creation of multi-institutional treatment plan databases for various types of research studies, including dose-volume-outcomes analyses and IMRT treatment planning comparisons.

References in zbMATH (referenced in 15 articles )

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  1. Bonacker, Esther; Gibali, Aviv; Küfer, Karl-Heinz; Süss, Philipp: Speedup of lexicographic optimization by superiorization and its applications to cancer radiotherapy treatment (2017)
  2. Alfonso, J.C.L.; Buttazzo, G.; García-Archilla, B.; Herrero, M.A.; Núñez, L.: Selecting radiotherapy dose distributions by means of constrained optimization problems (2014)
  3. Chan, Timothy C.Y.; Mahmoudzadeh, Houra; Purdie, Thomas G.: A robust-CVaR optimization approach with application to breast cancer therapy (2014)
  4. Dias, Joana; Rocha, Humberto; Ferreira, Brígida: A genetic algorithm with neural network fitness function evaluation for IMRT beam angle optimization (2014)
  5. Bertsimas, D.; Cacchiani, V.; Craft, D.; Nohadani, O.: A hybrid approach to beam angle optimization in intensity-modulated radiation therapy (2013)
  6. Rocha, H.; Dias, J.M.; Ferreira, B.C.; Lopes, M.C.: Selection of intensity modulated radiation therapy treatment beam directions using radial basis functions within a pattern search methods framework (2013)
  7. Rocha, H.; Dias, J.M.; Ferreira, B.C.; Lopes, M.C.: Pattern search methods framework for beam angle optimization in radiotherapy design (2013)
  8. Hardcastle, Nicholas; Tome, Wolfgang A.: Risk-adaptive volumetric modulated arc therapy using biological objective functions for subvolume boosting in radiotherapy (2012)
  9. Rocha, H.; Dias, J.M.; Ferreira, B.C.; Lopes, M.C.: Discretization of optimal beamlet intensities in IMRT: a binary integer programming approach (2012) ioport
  10. Bertsimas, Dimitris; Nohadani, Omid; Teo, Kwong Meng: Nonconvex robust optimization for problems with constraints (2010)
  11. Cromvik, C.; Patriksson, M.: On the robustness of global optima and stationary solutions to stochastic mathematical programs with equilibrium constraints. II: Applications (2010)
  12. El Naqa, I.; Grigsby, P.W.; Apte, A.; Kidd, E.; Donnelly, E.; Khullar, D.; Chaudhari, S.; Yang, D.; Schmitt, M.; Laforest, Richard; Thorstad, W.L.; Deasy, J.O.: Exploring feature-based approaches in PET images for predicting cancer treatment outcomes (2009) ioport
  13. Clark, V.H.; Chen, Y.; Wilkens, J.; Alaly, J.R.; Zakaryan, K.; Deasy, J.O.: IMRT treatment planning for prostate cancer using prioritized prescription optimization and mean-tail-dose functions (2008)
  14. Zhang, Yin; Merritt, Michael: Dose-volume-based IMRT fluence optimization: a fast least-squares approach with differentiability (2008)
  15. Deasy, J.; Lee, E.K.; Bortfeld, T.; Langer, M.; Zakarian, K.; Alaly, J.; Zhang, Y.; Liu, H.; Mohan, R.; Ahuja, R.; Pollack, A.; Purdy, J.; Rardin, R.: A collaboratory for radiation therapy treatment planning optimization research (2006)