487 research outputs found
A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT
Purpose: Current inverse planning methods for IMRT are limited because they
are not designed to explore the trade-offs between the competing objectives
between the tumor and normal tissues. Our goal was to develop an efficient
multiobjective optimization algorithm that was flexible enough to handle any
form of objective function and that resulted in a set of Pareto optimal plans.
Methods: We developed a hierarchical evolutionary multiobjective algorithm
designed to quickly generate a diverse Pareto optimal set of IMRT plans that
meet all clinical constraints and reflect the trade-offs in the plans. The top
level of the hierarchical algorithm is a multiobjective evolutionary algorithm
(MOEA). The genes of the individuals generated in the MOEA are the parameters
that define the penalty function minimized during an accelerated deterministic
IMRT optimization that represents the bottom level of the hierarchy. The MOEA
incorporates clinical criteria to restrict the search space through protocol
objectives and then uses Pareto optimality among the fitness objectives to
select individuals.
Results: Acceleration techniques implemented on both levels of the
hierarchical algorithm resulted in short, practical runtimes for optimizations.
The MOEA improvements were evaluated for example prostate cases with one target
and two OARs. The modified MOEA dominated 11.3% of plans using a standard
genetic algorithm package. By implementing domination advantage and protocol
objectives, small diverse populations of clinically acceptable plans that were
only dominated 0.2% by the Pareto front could be generated in a fraction of an
hour.
Conclusions: Our MOEA produces a diverse Pareto optimal set of plans that
meet all dosimetric protocol criteria in a feasible amount of time. It
optimizes not only beamlet intensities but also objective function parameters
on a patient-specific basis
A GPU-based multi-criteria optimization algorithm for HDR brachytherapy
Currently in HDR brachytherapy planning, a manual fine-tuning of an objective
function is necessary to obtain case-specific valid plans. This study intends
to facilitate this process by proposing a patient-specific inverse planning
algorithm for HDR prostate brachytherapy: GPU-based multi-criteria optimization
(gMCO).
Two GPU-based optimization engines including simulated annealing (gSA) and a
quasi-Newton optimizer (gL-BFGS) were implemented to compute multiple plans in
parallel. After evaluating the equivalence and the computation performance of
these two optimization engines, one preferred optimization engine was selected
for the gMCO algorithm. Five hundred sixty-two previously treated prostate HDR
cases were divided into validation set (100) and test set (462). In the
validation set, the number of Pareto optimal plans to achieve the best plan
quality was determined for the gMCO algorithm. In the test set, gMCO plans were
compared with the physician-approved clinical plans.
Over 462 cases, the number of clinically valid plans was 428 (92.6%) for
clinical plans and 461 (99.8%) for gMCO plans. The number of valid plans with
target V100 coverage greater than 95% was 288 (62.3%) for clinical plans and
414 (89.6%) for gMCO plans. The mean planning time was 9.4 s for the gMCO
algorithm to generate 1000 Pareto optimal plans.
In conclusion, gL-BFGS is able to compute thousands of SA equivalent
treatment plans within a short time frame. Powered by gL-BFGS, an ultra-fast
and robust multi-criteria optimization algorithm was implemented for HDR
prostate brachytherapy. A large-scale comparison against physician approved
clinical plans showed that treatment plan quality could be improved and
planning time could be significantly reduced with the proposed gMCO algorithm.Comment: 18 pages, 7 figure
Feasibility of Multisolutions Optimization Technique for Real-Time HDR Brachytherapy of Prostate
The purpose of this study was to evaluate the efficacy of multisolutions optimization algorithm for High Dose Rate (HDR) brachytherapy of prostate. In this retrospective study, we included data from 20 prostate cancer patients who underwent ultrasound based real time HDR Brachytherapy at institution. The treatment plans of all 20 patients were optimized in Oncentra Prostate treatment planning system (TPS) using inverse dose volume histogram based optimization followed by graphical optimization (GRO) in real time. The data of all the patients were retrieved later, and the treatment plans were re-optimized using multisolutions dose volume histogram based optimization (MDVHO) and multisolutions variance based optimization (MVBO) algorithms with same set of dose constraints, same number of catheters, and same contour set as in GRO. Several Pareto optimal solutions were obtained by varying the weighting factors of composite objective function in finite steps of adequate resolutions. These solutions were then stored in the database of TPS and same decision criteria was employed to pick the final solution using a decision engine. The average values for planning target volume receiving 100% of prescribed dose (V100) for MDVHO, MVBO, and GRO were 95.03%, 86.72% and 97.56%, respectively. The average V100 due to MDVHO was statistically significant (P = 0.002) in comparison to MVBO, whereas the average V100 due to MDVHO and GRO was not statistically significant (P = 0.066). In conclusion, the MDVHO can provide comparable solutions to typical clinical optimizations using GRO within clinically reasonable amount of time. In most of the cases, the plans created by MVBO were not clinically acceptable without usersâ further manual intervention
Potential for doseâ escalation and reduction of risk in pancreatic cancer using IMRT optimization with lexicographic ordering and gEUDâ based cost functions
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135067/1/mp6403.pd
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