title: A novel fluence map optimization model incorporating leaf sequencing constraints.
abstract: A novel fluence map optimization model incorporating leaf sequencing constraints is proposed to overcome the drawbacks of the current objective inside smoothing models. Instead of adding a smoothing item to the objective function, we add the total number of monitor unit (TNMU) requirement directly to the constraints which serves as an important factor to balance the fluence map optimization and leaf sequencing optimization process at the same time. Consequently, we formulate the fluence map optimization models for the trailing (left) leaf synchronized, leading (right) leaf synchronized and the interleaf motion constrained non-synchronized leaf sweeping schemes, respectively. In those schemes, the leaves are all swept unidirectionally from left to right. Each of those models is turned into a linear constrained quadratic programming model which can be solved effectively by the interior point method. Those new models are evaluated with two publicly available clinical treatment datasets including a head-neck case and a prostate case. As shown by the empirical results, our models perform much better in comparison with two recently emerged smoothing models (the total variance smoothing model and the quadratic smoothing model). For all three leaf sweeping schemes, our objective dose deviation functions increase much slower than those in the above two smoothing models with respect to the decreasing of the TNMU. While keeping plans in the similar conformity level, our new models gain much better performance on reducing TNMU.
authors.affiliation: School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, People's Republic of China.
authors.forename: Renchao
authors.initials: R
authors.lastname: Jin
authors.forename: Zhifang
authors.initials: Z
authors.lastname: Min
authors.forename: Enmin
authors.initials: E
authors.lastname: Song
authors.forename: Hong
authors.initials: H
authors.lastname: Liu
authors.forename: Yinyu
authors.initials: Y
authors.lastname: Ye
mesh_terms: D000465:Algorithms; D019992:Databases as Topic; D006258:Head and Neck Neoplasms / Q000532:radiotherapy; D006801:Humans; D016014:Linear Models; D008297:Male; D008954:Models, Biological; D011471:Prostatic Neoplasms / Q000532:radiotherapy; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*