Yican Wu

Multi-objective optimization of inverse planning for accurate radiotherapy

The multi-objective optimization of inverse planning based on the Pareto solution set, according to the multi-objective character of inverse planning in accurate radiotherapy, was studied in this paper. Firstly, the clinical requirements of a treatment plan were transformed into a multi-objective optimization problem with multiple constraints. Then, the fast and elitist multi-objective Non-dominated Sorting Genetic Algorithm (NSGA- II) was introduced to optimize the problem. A clinical example was tested using this method. The results show that an obtained set of non-dominated solutions were uniformly distributed and the corresponding dose distribution of each solution not only approached the expected dose distribution, but also met the dose-volume constraints. It was indicated that the clinical requirements were better satisfied using the method and the planner could select the optimal treatment plan from the non-dominated solution set.

Improved Model on Minimizing Static Intensity Modulation Delivery Time

In Intensity Modulated Radiation Therapy static intensity modulation delivery, leaf sequencing is basically a process where the intensity map is broken down into subfields or segments that can be implemented by multileaf collimators. The purpose of this study was to investigate a new method to improve the Langer’s integer programming model. The method is to choose the horizontal or orthogonal leaf direction of intensity map according with Xia and Verhy’s alogrithm and invoke a new constraint to optimize multileaf collimator leaf travel distances. A comparative study of three different leaf sequencing methods, Xia and Verhey, Langer et al., and our improved model is presented. The numerical outputs reveal that model modifications can yield better results according with the three criteria: total number of monitor unit, number of segment and leaf travel distances. Keywordsmultileaf collimator; leaf sequencing; leaf travel distances

Human Organ Geometry Construction from Segmented Images

The human voxel phantom containing large number of voxels is laborious for three dimensional (3D) reconstructions in Monte Carlo simulation applications. A fast and accurate geometry construction method was put forward in present study. The voxels of the phantom was processed as follows: (1) Assigned voxels of one certain organ with 3D coordinates. (2)Based on the coordinate matrix, the voxels were 3D merged into large cuboids. (3)Extract the vertical plane of the surface of the organ in six directions. (4) Planes were used to enclose the organ for the geometry reconstruction. In the process, the voxels was indexed in accordance with the coordinates in order to establish and achieve traversals. With this method, the whole-body phantom of a Chinese radiation virtual human (Rad-HUMAN) was effectively constructed. The geometry described by planes could represent the accurate appearance and precise interior structure of the whole-body phantom. Without ambiguity and self-crossing, it can faithfully describe the voxel pattern of the phantom which can be effectively visualized.