Journal Articles
2020
Antaki, Majd; Deufel, Christopher L; Enger, Shirin A.
Fast mixed integer optimization (FMIO) for high dose rate brachytherapy Journal Article
In: Physics in Medicine and Biology, vol. 65, no. 21, pp. 215005, 2020, ISSN: 1361-6560.
Abstract | Links | BibTeX | Tags: Algorithms, Brachytherapy, Computer-Assisted, Humans, Linear Models, Male, Monte Carlo Method, Prostatic Neoplasms, Radiation Dosage, Radiotherapy Dosage, Radiotherapy Planning, Software, Time Factors
@article{antaki_fast_2020,
title = {Fast mixed integer optimization (FMIO) for high dose rate brachytherapy},
author = {Majd Antaki and Christopher L Deufel and Shirin A. Enger},
doi = {10.1088/1361-6560/aba317},
issn = {1361-6560},
year = {2020},
date = {2020-12-01},
journal = {Physics in Medicine and Biology},
volume = {65},
number = {21},
pages = {215005},
abstract = {The purpose of this work was to develop an efficient quadratic mixed integer programming algorithm for high dose rate (HDR) brachytherapy treatment planning problems and integrate the algorithm into an open-source Monte Carlo based treatment planning software, RapidBrachyMCTPS. The mixed-integer algorithm yields a globally optimum solution to the dose volume histogram (DVH) based problem and, unlike other methods, is not susceptible to local minimum trapping. A hybrid linear-quadratic penalty model coupled to a mixed integer programming model was used to optimize treatment plans for 10 prostate cancer patients. Dose distributions for each dwell position were calculated with RapidBrachyMCTPS with type A uncertainties less than 0.2% in voxels within the planning target volume (PTV). The optimization process was divided into two parts. First, the data was preprocessed, in which the problem size was reduced by eliminating voxels that had negligible impact on the solution (e.g. far from the dwell position). Second, the best combination of dwell times to obtain a plan with the highest score was found. The dwell positions and dose volume constraints were used as input to a commercial mixed integer optimizer (Gurobi Optimization, Inc.). A penalty-based criterion was adopted for the scoring. The voxel-reduction technique successfully reduced the problem size by an average of 91%, without loss of quality. The preprocessing of the optimization process required on average 4 s and solving for the global maximum required on average 33 s. The total optimization time averaged 37 s, which is a substantial improvement over the ∼15 min optimization time reported in published literature. The plan quality was evaluated by evaluating dose volume metrics, including PTV D90, rectum and bladder D1cc and urethra D0.1cc. In conclusion, fast mixed integer optimization is an order of magnitude faster than current mixed-integer approaches for solving HDR brachytherapy treatment planning problems with DVH based metrics.},
keywords = {Algorithms, Brachytherapy, Computer-Assisted, Humans, Linear Models, Male, Monte Carlo Method, Prostatic Neoplasms, Radiation Dosage, Radiotherapy Dosage, Radiotherapy Planning, Software, Time Factors},
pubstate = {published},
tppubtype = {article}
}
The purpose of this work was to develop an efficient quadratic mixed integer programming algorithm for high dose rate (HDR) brachytherapy treatment planning problems and integrate the algorithm into an open-source Monte Carlo based treatment planning software, RapidBrachyMCTPS. The mixed-integer algorithm yields a globally optimum solution to the dose volume histogram (DVH) based problem and, unlike other methods, is not susceptible to local minimum trapping. A hybrid linear-quadratic penalty model coupled to a mixed integer programming model was used to optimize treatment plans for 10 prostate cancer patients. Dose distributions for each dwell position were calculated with RapidBrachyMCTPS with type A uncertainties less than 0.2% in voxels within the planning target volume (PTV). The optimization process was divided into two parts. First, the data was preprocessed, in which the problem size was reduced by eliminating voxels that had negligible impact on the solution (e.g. far from the dwell position). Second, the best combination of dwell times to obtain a plan with the highest score was found. The dwell positions and dose volume constraints were used as input to a commercial mixed integer optimizer (Gurobi Optimization, Inc.). A penalty-based criterion was adopted for the scoring. The voxel-reduction technique successfully reduced the problem size by an average of 91%, without loss of quality. The preprocessing of the optimization process required on average 4 s and solving for the global maximum required on average 33 s. The total optimization time averaged 37 s, which is a substantial improvement over the ∼15 min optimization time reported in published literature. The plan quality was evaluated by evaluating dose volume metrics, including PTV D90, rectum and bladder D1cc and urethra D0.1cc. In conclusion, fast mixed integer optimization is an order of magnitude faster than current mixed-integer approaches for solving HDR brachytherapy treatment planning problems with DVH based metrics.2018
Famulari, Gabriel; Renaud, Marc-André; Poole, Christopher M.; Evans, Michael D. C.; Seuntjens, Jan; Enger, Shirin A.
RapidBrachyMCTPS: a Monte Carlo-based treatment planning system for brachytherapy applications Journal Article
In: Physics in Medicine and Biology, vol. 63, no. 17, pp. 175007, 2018, ISSN: 1361-6560.
Abstract | Links | BibTeX | Tags: Brachytherapy, Computer-Assisted, Humans, Imaging, Monte Carlo Method, Phantoms, Radiotherapy Dosage, Radiotherapy Planning, Software
@article{famulari_rapidbrachymctps_2018,
title = {RapidBrachyMCTPS: a Monte Carlo-based treatment planning system for brachytherapy applications},
author = {Gabriel Famulari and Marc-André Renaud and Christopher M. Poole and Michael D. C. Evans and Jan Seuntjens and Shirin A. Enger},
doi = {10.1088/1361-6560/aad97a},
issn = {1361-6560},
year = {2018},
date = {2018-08-01},
journal = {Physics in Medicine and Biology},
volume = {63},
number = {17},
pages = {175007},
abstract = {Despite being considered the gold standard for brachytherapy dosimetry, Monte Carlo (MC) has yet to be implemented into a software for brachytherapy treatment planning. The purpose of this work is to present RapidBrachyMCTPS, a novel treatment planning system (TPS) for brachytherapy applications equipped with a graphical user interface (GUI), optimization tools and a Geant4-based MC dose calculation engine, RapidBrachyMC. Brachytherapy sources and applicators were implemented in RapidBrachyMC and made available to the user via a source and applicator library in the GUI. To benchmark RapidBrachyMC, TG-43 parameters were calculated for the microSelectron v2 (192Ir) and SelectSeed (125I) source models and were compared against previously validated MC brachytherapy codes. The performance of RapidBrachyMC was evaluated for a prostate high dose rate case. To assess the accuracy of RapidBrachyMC in a heterogeneous setup, dose distributions with a cylindrical shielded/unshielded applicator were validated against film measurements in a Solid WaterTM phantom. TG-43 parameters calculated using RapidBrachyMC generally agreed within 1%-2% of the results obtained in previously published work. For the prostate case, clinical dosimetric indices showed general agreement with Oncentra TPS within 1%. Simulation times were on the order of minutes on a single core to achieve uncertainties below 2% in voxels within the prostate. The calculation time was decreased further using the multithreading features of Geant4. In the comparison between MC-calculated and film-measured dose distributions, at least 95% of points passed the 3%/3 mm gamma index criteria in all but one case. RapidBrachyMCTPS can be used as a post-implant dosimetry toolkit, as well as for MC-based brachytherapy treatment planning. This software is especially well suited for the development of new source and applicator models.},
keywords = {Brachytherapy, Computer-Assisted, Humans, Imaging, Monte Carlo Method, Phantoms, Radiotherapy Dosage, Radiotherapy Planning, Software},
pubstate = {published},
tppubtype = {article}
}
Despite being considered the gold standard for brachytherapy dosimetry, Monte Carlo (MC) has yet to be implemented into a software for brachytherapy treatment planning. The purpose of this work is to present RapidBrachyMCTPS, a novel treatment planning system (TPS) for brachytherapy applications equipped with a graphical user interface (GUI), optimization tools and a Geant4-based MC dose calculation engine, RapidBrachyMC. Brachytherapy sources and applicators were implemented in RapidBrachyMC and made available to the user via a source and applicator library in the GUI. To benchmark RapidBrachyMC, TG-43 parameters were calculated for the microSelectron v2 (192Ir) and SelectSeed (125I) source models and were compared against previously validated MC brachytherapy codes. The performance of RapidBrachyMC was evaluated for a prostate high dose rate case. To assess the accuracy of RapidBrachyMC in a heterogeneous setup, dose distributions with a cylindrical shielded/unshielded applicator were validated against film measurements in a Solid WaterTM phantom. TG-43 parameters calculated using RapidBrachyMC generally agreed within 1%-2% of the results obtained in previously published work. For the prostate case, clinical dosimetric indices showed general agreement with Oncentra TPS within 1%. Simulation times were on the order of minutes on a single core to achieve uncertainties below 2% in voxels within the prostate. The calculation time was decreased further using the multithreading features of Geant4. In the comparison between MC-calculated and film-measured dose distributions, at least 95% of points passed the 3%/3 mm gamma index criteria in all but one case. RapidBrachyMCTPS can be used as a post-implant dosimetry toolkit, as well as for MC-based brachytherapy treatment planning. This software is especially well suited for the development of new source and applicator models.
Journal Articles
2020
Antaki, Majd; Deufel, Christopher L; Enger, Shirin A.
Fast mixed integer optimization (FMIO) for high dose rate brachytherapy Journal Article
In: Physics in Medicine and Biology, vol. 65, no. 21, pp. 215005, 2020, ISSN: 1361-6560.
Abstract | Links | BibTeX | Tags: Algorithms, Brachytherapy, Computer-Assisted, Humans, Linear Models, Male, Monte Carlo Method, Prostatic Neoplasms, Radiation Dosage, Radiotherapy Dosage, Radiotherapy Planning, Software, Time Factors
@article{antaki_fast_2020,
title = {Fast mixed integer optimization (FMIO) for high dose rate brachytherapy},
author = {Majd Antaki and Christopher L Deufel and Shirin A. Enger},
doi = {10.1088/1361-6560/aba317},
issn = {1361-6560},
year = {2020},
date = {2020-12-01},
journal = {Physics in Medicine and Biology},
volume = {65},
number = {21},
pages = {215005},
abstract = {The purpose of this work was to develop an efficient quadratic mixed integer programming algorithm for high dose rate (HDR) brachytherapy treatment planning problems and integrate the algorithm into an open-source Monte Carlo based treatment planning software, RapidBrachyMCTPS. The mixed-integer algorithm yields a globally optimum solution to the dose volume histogram (DVH) based problem and, unlike other methods, is not susceptible to local minimum trapping. A hybrid linear-quadratic penalty model coupled to a mixed integer programming model was used to optimize treatment plans for 10 prostate cancer patients. Dose distributions for each dwell position were calculated with RapidBrachyMCTPS with type A uncertainties less than 0.2% in voxels within the planning target volume (PTV). The optimization process was divided into two parts. First, the data was preprocessed, in which the problem size was reduced by eliminating voxels that had negligible impact on the solution (e.g. far from the dwell position). Second, the best combination of dwell times to obtain a plan with the highest score was found. The dwell positions and dose volume constraints were used as input to a commercial mixed integer optimizer (Gurobi Optimization, Inc.). A penalty-based criterion was adopted for the scoring. The voxel-reduction technique successfully reduced the problem size by an average of 91%, without loss of quality. The preprocessing of the optimization process required on average 4 s and solving for the global maximum required on average 33 s. The total optimization time averaged 37 s, which is a substantial improvement over the ∼15 min optimization time reported in published literature. The plan quality was evaluated by evaluating dose volume metrics, including PTV D90, rectum and bladder D1cc and urethra D0.1cc. In conclusion, fast mixed integer optimization is an order of magnitude faster than current mixed-integer approaches for solving HDR brachytherapy treatment planning problems with DVH based metrics.},
keywords = {Algorithms, Brachytherapy, Computer-Assisted, Humans, Linear Models, Male, Monte Carlo Method, Prostatic Neoplasms, Radiation Dosage, Radiotherapy Dosage, Radiotherapy Planning, Software, Time Factors},
pubstate = {published},
tppubtype = {article}
}
The purpose of this work was to develop an efficient quadratic mixed integer programming algorithm for high dose rate (HDR) brachytherapy treatment planning problems and integrate the algorithm into an open-source Monte Carlo based treatment planning software, RapidBrachyMCTPS. The mixed-integer algorithm yields a globally optimum solution to the dose volume histogram (DVH) based problem and, unlike other methods, is not susceptible to local minimum trapping. A hybrid linear-quadratic penalty model coupled to a mixed integer programming model was used to optimize treatment plans for 10 prostate cancer patients. Dose distributions for each dwell position were calculated with RapidBrachyMCTPS with type A uncertainties less than 0.2% in voxels within the planning target volume (PTV). The optimization process was divided into two parts. First, the data was preprocessed, in which the problem size was reduced by eliminating voxels that had negligible impact on the solution (e.g. far from the dwell position). Second, the best combination of dwell times to obtain a plan with the highest score was found. The dwell positions and dose volume constraints were used as input to a commercial mixed integer optimizer (Gurobi Optimization, Inc.). A penalty-based criterion was adopted for the scoring. The voxel-reduction technique successfully reduced the problem size by an average of 91%, without loss of quality. The preprocessing of the optimization process required on average 4 s and solving for the global maximum required on average 33 s. The total optimization time averaged 37 s, which is a substantial improvement over the ∼15 min optimization time reported in published literature. The plan quality was evaluated by evaluating dose volume metrics, including PTV D90, rectum and bladder D1cc and urethra D0.1cc. In conclusion, fast mixed integer optimization is an order of magnitude faster than current mixed-integer approaches for solving HDR brachytherapy treatment planning problems with DVH based metrics.
2018
Famulari, Gabriel; Renaud, Marc-André; Poole, Christopher M.; Evans, Michael D. C.; Seuntjens, Jan; Enger, Shirin A.
RapidBrachyMCTPS: a Monte Carlo-based treatment planning system for brachytherapy applications Journal Article
In: Physics in Medicine and Biology, vol. 63, no. 17, pp. 175007, 2018, ISSN: 1361-6560.
Abstract | Links | BibTeX | Tags: Brachytherapy, Computer-Assisted, Humans, Imaging, Monte Carlo Method, Phantoms, Radiotherapy Dosage, Radiotherapy Planning, Software
@article{famulari_rapidbrachymctps_2018,
title = {RapidBrachyMCTPS: a Monte Carlo-based treatment planning system for brachytherapy applications},
author = {Gabriel Famulari and Marc-André Renaud and Christopher M. Poole and Michael D. C. Evans and Jan Seuntjens and Shirin A. Enger},
doi = {10.1088/1361-6560/aad97a},
issn = {1361-6560},
year = {2018},
date = {2018-08-01},
journal = {Physics in Medicine and Biology},
volume = {63},
number = {17},
pages = {175007},
abstract = {Despite being considered the gold standard for brachytherapy dosimetry, Monte Carlo (MC) has yet to be implemented into a software for brachytherapy treatment planning. The purpose of this work is to present RapidBrachyMCTPS, a novel treatment planning system (TPS) for brachytherapy applications equipped with a graphical user interface (GUI), optimization tools and a Geant4-based MC dose calculation engine, RapidBrachyMC. Brachytherapy sources and applicators were implemented in RapidBrachyMC and made available to the user via a source and applicator library in the GUI. To benchmark RapidBrachyMC, TG-43 parameters were calculated for the microSelectron v2 (192Ir) and SelectSeed (125I) source models and were compared against previously validated MC brachytherapy codes. The performance of RapidBrachyMC was evaluated for a prostate high dose rate case. To assess the accuracy of RapidBrachyMC in a heterogeneous setup, dose distributions with a cylindrical shielded/unshielded applicator were validated against film measurements in a Solid WaterTM phantom. TG-43 parameters calculated using RapidBrachyMC generally agreed within 1%-2% of the results obtained in previously published work. For the prostate case, clinical dosimetric indices showed general agreement with Oncentra TPS within 1%. Simulation times were on the order of minutes on a single core to achieve uncertainties below 2% in voxels within the prostate. The calculation time was decreased further using the multithreading features of Geant4. In the comparison between MC-calculated and film-measured dose distributions, at least 95% of points passed the 3%/3 mm gamma index criteria in all but one case. RapidBrachyMCTPS can be used as a post-implant dosimetry toolkit, as well as for MC-based brachytherapy treatment planning. This software is especially well suited for the development of new source and applicator models.},
keywords = {Brachytherapy, Computer-Assisted, Humans, Imaging, Monte Carlo Method, Phantoms, Radiotherapy Dosage, Radiotherapy Planning, Software},
pubstate = {published},
tppubtype = {article}
}
Despite being considered the gold standard for brachytherapy dosimetry, Monte Carlo (MC) has yet to be implemented into a software for brachytherapy treatment planning. The purpose of this work is to present RapidBrachyMCTPS, a novel treatment planning system (TPS) for brachytherapy applications equipped with a graphical user interface (GUI), optimization tools and a Geant4-based MC dose calculation engine, RapidBrachyMC. Brachytherapy sources and applicators were implemented in RapidBrachyMC and made available to the user via a source and applicator library in the GUI. To benchmark RapidBrachyMC, TG-43 parameters were calculated for the microSelectron v2 (192Ir) and SelectSeed (125I) source models and were compared against previously validated MC brachytherapy codes. The performance of RapidBrachyMC was evaluated for a prostate high dose rate case. To assess the accuracy of RapidBrachyMC in a heterogeneous setup, dose distributions with a cylindrical shielded/unshielded applicator were validated against film measurements in a Solid WaterTM phantom. TG-43 parameters calculated using RapidBrachyMC generally agreed within 1%-2% of the results obtained in previously published work. For the prostate case, clinical dosimetric indices showed general agreement with Oncentra TPS within 1%. Simulation times were on the order of minutes on a single core to achieve uncertainties below 2% in voxels within the prostate. The calculation time was decreased further using the multithreading features of Geant4. In the comparison between MC-calculated and film-measured dose distributions, at least 95% of points passed the 3%/3 mm gamma index criteria in all but one case. RapidBrachyMCTPS can be used as a post-implant dosimetry toolkit, as well as for MC-based brachytherapy treatment planning. This software is especially well suited for the development of new source and applicator models.