Abstract
The results of optimization of inverse treatment plans depend on a choice of the objective function. Even when the optimal solution for a given cost function can be obtained, a better solution may exist for a given clinical scenario and it could be obtained with a revised objective function. In the approach presented in this work mixed integer programming was used to introduce a new volume-based objective function, which allowed for minimization of the number of under- or overdosed voxels in selected structures. By selecting and prioritizing components of this function the user could drive the computations towards the desired solution. This optimization approach was tested using cases of patients treated for prostate and oropharyngeal cancer. Initial solutions were obtained based on minimization/maximization of the dose to critical structures and targets. Subsequently, the volume-based objective functions were used to locate solutions, which satisfied better clinical objectives particular to each of the cases. For prostate cases, these additional solutions offered further improvements in sparing of the rectum or the bladder. For oropharyngeal cases, families of solutions were obtained satisfying an intensity modulated radiation therapy protocol for this disease site, while offering significant improvement in the sparing of selected critical structures, e.g., parotid glands. An additional advantage of the present approach was in providing a convenient mechanism to test the feasibility of the dose-volume histogram constraints.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2503-2514 |
| Number of pages | 12 |
| Journal | Physics in Medicine and Biology |
| Volume | 49 |
| Issue number | 12 |
| DOIs | |
| State | Published - Jun 21 2004 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Biomedical Engineering
- Physics and Astronomy (miscellaneous)
- Radiology Nuclear Medicine and imaging
- Radiological and Ultrasound Technology
Cite this
Inverse treatment planning using volume-based objective functions. / Bednarz, Greg; Michalski, Darek; Anne, Pramila R.; Valicenti, Richard K.
In: Physics in Medicine and Biology, Vol. 49, No. 12, 21.06.2004, p. 2503-2514.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Inverse treatment planning using volume-based objective functions
AU - Bednarz, Greg
AU - Michalski, Darek
AU - Anne, Pramila R.
AU - Valicenti, Richard K
PY - 2004/6/21
Y1 - 2004/6/21
N2 - The results of optimization of inverse treatment plans depend on a choice of the objective function. Even when the optimal solution for a given cost function can be obtained, a better solution may exist for a given clinical scenario and it could be obtained with a revised objective function. In the approach presented in this work mixed integer programming was used to introduce a new volume-based objective function, which allowed for minimization of the number of under- or overdosed voxels in selected structures. By selecting and prioritizing components of this function the user could drive the computations towards the desired solution. This optimization approach was tested using cases of patients treated for prostate and oropharyngeal cancer. Initial solutions were obtained based on minimization/maximization of the dose to critical structures and targets. Subsequently, the volume-based objective functions were used to locate solutions, which satisfied better clinical objectives particular to each of the cases. For prostate cases, these additional solutions offered further improvements in sparing of the rectum or the bladder. For oropharyngeal cases, families of solutions were obtained satisfying an intensity modulated radiation therapy protocol for this disease site, while offering significant improvement in the sparing of selected critical structures, e.g., parotid glands. An additional advantage of the present approach was in providing a convenient mechanism to test the feasibility of the dose-volume histogram constraints.
AB - The results of optimization of inverse treatment plans depend on a choice of the objective function. Even when the optimal solution for a given cost function can be obtained, a better solution may exist for a given clinical scenario and it could be obtained with a revised objective function. In the approach presented in this work mixed integer programming was used to introduce a new volume-based objective function, which allowed for minimization of the number of under- or overdosed voxels in selected structures. By selecting and prioritizing components of this function the user could drive the computations towards the desired solution. This optimization approach was tested using cases of patients treated for prostate and oropharyngeal cancer. Initial solutions were obtained based on minimization/maximization of the dose to critical structures and targets. Subsequently, the volume-based objective functions were used to locate solutions, which satisfied better clinical objectives particular to each of the cases. For prostate cases, these additional solutions offered further improvements in sparing of the rectum or the bladder. For oropharyngeal cases, families of solutions were obtained satisfying an intensity modulated radiation therapy protocol for this disease site, while offering significant improvement in the sparing of selected critical structures, e.g., parotid glands. An additional advantage of the present approach was in providing a convenient mechanism to test the feasibility of the dose-volume histogram constraints.
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U2 - 10.1088/0031-9155/49/12/003
DO - 10.1088/0031-9155/49/12/003
M3 - Article
C2 - 15272670
AN - SCOPUS:3242809858
VL - 49
SP - 2503
EP - 2514
JO - Physics in Medicine and Biology
JF - Physics in Medicine and Biology
SN - 0031-9155
IS - 12
ER -