title: Inverse planning optimization method for intensity modulated radiation therapy.
abstract: In order to facilitate the leaf sequencing process in intensity modulated radiation therapy (IMRT), and design of a practical leaf sequencing algorithm, it is an important issue to smooth the planned fluence maps. The objective is to achieve both high-efficiency and high-precision dose delivering by considering characteristics of leaf sequencing process. The key factor which affects total number of monitor units for the leaf sequencing optimization process is the max flow value of the digraph which formulated from the fluence maps. Therefore, we believe that one strategy for compromising dose conformity and total number of monitor units in dose delivery is to balance the dose distribution function and the max flow value mentioned above. However, there are too many paths in the digraph, and we don't know the flow value of which path is the maximum. The maximum flow value among the horizontal paths was selected and used in the objective function of the fluence map optimization to formulate the model. The model is a traditional linear constrained quadratic optimization model which can be solved by interior point method easily. We believe that the smoothed maps from this model are more suitable for leaf sequencing optimization process than other smoothing models. A clinical head-neck case and a prostate case were tested and compared using our proposed model and the smoothing model which is based on the minimization of total variance. The optimization results with the same level of total number of monitor units (TNMU) show that the fluence maps obtained from our model have much better dose performance for the target/non-target region than the maps from total variance based on the smoothing model. This indicates that our model achieves better dose distribution when the algorithm suppresses the TNMU at the same level. Although we have just used the max flow value of the horizontal paths in the diagraph in the objective function, a good balance has been achieved between the dose conformity and the total number of monitor units. This idea can be extended to other fluence map optimization model, and we believe it can also achieve good performance.
authors.affiliation: School of Computer and Information Technology, Nanyang Normal University, Nanyang, Henan 473061, P.R.China. Cchung2012@sina.com.
authors.forename: Yihua
authors.initials: Y
authors.lastname: Lan
authors.forename: Haozheng
authors.initials: H
authors.lastname: Ren
authors.forename: Cunhua
authors.initials: C
authors.lastname: Li
authors.forename: Zhifang
authors.initials: Z
authors.lastname: Min
authors.forename: Jinxin
authors.initials: J
authors.lastname: Wan
authors.forename: Jianxin
authors.initials: J
authors.lastname: Ma
authors.forename: Chih-Cheng
authors.initials: CC
authors.lastname: Hung
mesh_terms: D000465:Algorithms; D006258:Head and Neck Neoplasms / Q000532:radiotherapy*; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D011471:Prostatic Neoplasms / Q000532:radiotherapy*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated