IMRT dose shaping with regionally variable penalty scheme.
A commonly known deficiency of currently available inverse planning systems is the difficulty in fine-tuning the final dose distribution. In practice, it is not uncommon that just a few unsatisfactory regions in the planning target volume or an organ at risk prevent an intensity modulated radiation therapy (IMRT) plan from being clinically acceptable. The purpose of this work is to introduce a mechanism for controlling the regional doses after a conventional IMRT plan is obtained and to demonstrate its clinical utility. Two types of importance factors are introduced in the objective function to model the tradeoffs of different clinical objectives. The first is the conventional structure-dependent importance factor, which quantifies the interstructure tradeoff. The second type is the voxel-dependent importance factor which "modulates" the importance of different voxels within a structure. The planning proceeds in two major steps. First a conventional inverse planning is performed, where the structure-dependent importance factors are determined in a trial-and-error fashion. The next level of planning involves fine-tuning the regional doses to meet specific clinical requirements. To achieve this, the voxels where doses need to be modified are identified either graphically on the isodose layouts, or on the corresponding dose-volume histogram (DVH) curves. The importance value of these voxels is then adjusted to increase/decrease the penalty at the corresponding regions. The technique is applied to two clinical cases. It was found that both tumor hot spots and critical structure maximal doses can be easily controlled by varying the regional penalty. One to three trials were sufficient for the conventionally optimized dose distributions to be adjusted to meet clinical expectation. Thus introducing the voxel-dependent penalty scheme provides an effective means for IMRT dose distributions painting and sculpting.
Cotrutz
Cristian
C
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California 94305-5304, USA.
Xing
Lei
L
D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008297:Male; D008954:Models, Biological; D009010:Monte Carlo Method; D009928:Organ Specificity; D011471:Prostatic Neoplasms / Q000532:radiotherapy*; D011874:Radiometry / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013125:Spinal Neoplasms / Q000532:radiotherapy*