AI-guided parameter optimization in inverse treatment planning.
An artificial intelligence (AI)-guided inverse planning system was developed to optimize the combination of parameters in the objective function for intensity-modulated radiation therapy (IMRT). In this system, the empirical knowledge of inverse planning was formulated with fuzzy if-then rules, which then guide the parameter modification based on the on-line calculated dose. Three kinds of parameters (weighting factor, dose specification, and dose prescription) were automatically modified using the fuzzy inference system (FIS). The performance of the AI-guided inverse planning system (AIGIPS) was examined using the simulated and clinical examples. Preliminary results indicate that the expected dose distribution was automatically achieved using the AI-guided inverse planning system, with the complicated compromising between different parameters accomplished by the fuzzy inference technique. The AIGIPS provides a highly promising method to replace the current trial-and-error approach.
Yan
Hui
H
Department of Radiation Oncology, Henry Ford Hospital, Detroit, Ml 48202, USA. hyan1@hfhs.org
Yin
Fang-Fang
FF
Guan
Huai-qun
HQ
Kim
Jae Ho
JH
D000465:Algorithms; D001185:Artificial Intelligence; D001822:Body Burden; D003198:Computer Simulation; D005240:Feasibility Studies; D017143:Fuzzy Logic; D006801:Humans; D008954:Models, Biological; D011786:Quality Control; D011835:Radiation Protection / Q000379:methods*; 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