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.
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.