Open tools

The source code of MIROpt is fully open and can be accessed and donwloaded from the OpenReggui platform.

Features

• Monte Carlo based treatment planning (using the fast MC dose engine MCsquare)
• Robust (worst-case) optimization (using libraries from the large-scale non linear optimizer IPOPT)
• Robustness against setup (systematic and random) and range errors
• 4D robust optimization
• Versatile design through MATLAB interface

Workflow integration with OpenReggui

The plugin developed in OpenReggui offers a graphical interface and a very user-friendly environment for MIROpt.

Publications

MIROpt has been used in different research projects that lead to several conference communication and journal articles:

• Accelerated robust optimization algorithm for proton therapy treatment planning, G Buti, K Souris, AM Barragán-Montero, M Cohilis, JA Lee, E Sterpin, Med Phys 2020, doi: 10.1002/mp.14132
• Towards fast and robust 4D optimization for moving tumors with scanned proton therapy, G Buti, K Souris, AM Barragán-Montero, JA Lee, E Sterpin, Med Phys 2019, doi: 10.1002/mp.13850
• Monte Carlo methods to comprehensively evaluate the robustness of 4D treatments in proton therapy, K Souris, AM Barragán-Montero, G Janssens, D Di Perri, E Sterpin, JA Lee, Med Phys 2019, doi: 10.1002/mp.13749
• E292-Linear Energy Transfer Incorporated Spot Scanning Proton Arc Therapy (SPArc) Optimization, X Li, X Ding, G Liu, G Janssens, K Souris, AM Barragan Montero, D Yan, C Stevens, P Kabolizadeh, AAPM 2019
• Performance of a hybrid Monte Carlo-Pencil Beam dose algorithm for proton therapy inverse planning, AM Barragán Montero, K Souris, D Sanchez‐Parcerisa, E Sterpin, JA Lee, Med Phys 2017, doi: 10.1002/mp.12688
• Patient-specific bolus for range shifter air gap reduction in intensity-modulated proton therapy of head-and-neck cancer studied with Monte Carlo based plan optimization, S Michiels, AM Barragán, K Souris, K Poels, W Crijns, JA Lee, E Sterpin, S Nuyts, K Haustermans, and T Depuydt, Radiother Oncol. 2017 doi: 10.1016/j.radonc.2017.09.006
• EP-1643: Simulate baseline shift uncertainties to improve robustness of proton therapy treatments, Souris K., Barragan A., Di Perri D., Geets X., Sterpin E., and Lee J. A., and Sterpin E., ESTRO 2017 doi: 10.1016/S0167-8140(17)32078-9
• SU-H3-GePD-T-2: Simulation of Motion Amplitude Variations to Verify the Robustness of Treatment Plans, Souris K., Barragan Montero A., Janssens G., Di Perri D., Sterpin E., and Lee J. A., and Sterpin E., AAPM 2017 SU-H3-GePD-T-2
• OC-0265: Efficient implementation of random errors in robust optimization for proton therapy with Monte Carlo, Barragan Montero A. M., Souris K., Sterpin E., and Lee J. A., ESTRO 2016 doi: 10.1016/S0167-8140(16)31514-6
• WE-AB-209-01: A Monte Carlo-Based Method to Include Random Errors in Robust Optimization, Barragan Montero A. M., Souris K., Lee J. A., and Sterpin E., AAPM 2016 doi: 10.1118/1.4957770