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A dose‐volume histogram based optimization algorithm for ultrasound guided prostate implants
Author(s) -
Chen Yan,
Boyer Arthur L.,
Xing Lei
Publication year - 2000
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1308087
Subject(s) - voxel , histogram , dose volume histogram , radiation treatment planning , algorithm , dosimetry , volume (thermodynamics) , computer science , mathematics , mathematical optimization , nuclear medicine , radiation therapy , artificial intelligence , medicine , radiology , physics , quantum mechanics , image (mathematics)
The task of treatment planning for prostate implants is to find an optimal seed configuration, comprising the target coverage and dosimetric consideration of critical structures such as the rectum and urethra. An efficient method to accomplish this is to use an inverse planning technique that derives the optimized solution from a prescribed treatment goal. The goal can be specified in the voxel domain as the desired doses to the voxels of the target and critical structures, or in the dose volume representation as the desired dose volume histograms (DVHs) of the target and critical structures. The DVH based optimization has been successfully used in plan optimization for intensity‐modulated radiation therapy (IMRT) but little attention has been paid to its application in prostate implants. Clinically, it has long been known that some normal structure tolerances are more accurately assessed by volumetric information. Dose‐volume histograms are also widely used for plan evaluation. When working in the DVH domain for optimization one has more control over the final DVHs. We have constructed an objective function sensitive to the DVHs of the target and critical structures. The objective function is minimized using an iterative algorithm, starting from a randomly selected initial seed configuration. At each iteration step, a trial position is given to a randomly selected source and the trial position is accepted if the objective function is decreased. To avoid being trapped in a less optimal local minimum, the optimization process is repeated. The final plan is selected from a pool of optimized plans obtained from a series of randomized initial seed configurations.