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Helical TomoTherapy(HT) planning depends on optimize parameters including field width (FW), pitch factor (PF) and modulation factor (MF). These optimize parameters are effect to quality of plans and treatment time. The aim of this study was to find the optimized parameters which compromise between plan quality and treatment times. Six nasopharyngeal cancer patients were used. For each patient data set, 18 treatment plans consisted of different optimize parameters combination (FW=5.0, 2.5, 1.0 cm; PF=0.43, 0.287, 0.215; MF2.0, 3.0) were created. The identical optimization procedure followed ICRU83 recommendations. The average D50 of both parotid glands and treatment times per fraction were compared for all plans. The study show treatment plan with FW1.0 cm showed the lowest average D50 of both parotid glands. The treatment time increased inversely to FW. The FW1.0 cm the average treatment time was 4 times longer than FW5.0 cm. PF was very little influence on the average D50 of both parotid glands. Finally, MF increased from 2.0 to 3.0 the average D50 of both parotid glands was slightly decreased. However, the average treatment time was increased 22.28%. For routine nasopharyngeal cancer patients with HT, we suggest the planning optimization parameters consist of FW=5.0 cm, PF=0.43 and MF=2.0.

Several shape recognition systems based on pairwise shape matching technique have achieved high accuracy but they face a problem of time consumption when they are evaluated on a large database. So this drawback makes the system impractical for real-time applications. Motivated by this obstacle, we have investigated a novel and robust neural network solution to achieve high speed of shape recognition without sacrificing accuracy via the non-absolute 1-D triangle area representation (NATA). Our method has been evaluated over a number of affine distorted shapes. The experimental results demonstrate that a shape recognition system using the neural network can achieve high speed and accuracy comparable with the prior system.

Shape recognition is an important part of machine intelligence in both decision making and data processing. A good shape representation in shape recognition should describe the shape in the way that makes it distinguishable from other shapes and be invariant to transform of position, size, angle and skew. More importantly, developing and finding appropriate shape representation are still a challenging problem. In our previous works, the 1-D area representation at various triangle side lengths has been proposed as an affine invariant shape representation. Finding the optimum triangle side length, the best 1-D area representation, needs to conduct through recognition systems experimentally. In this work, Fisherpsilas discriminant analysis is applied to predict the optimum triangle side length instead of obtaining it from the experiment. This method has been evaluated over a number of affine distorted shapes. The predicted optimum triangle side length is compared with the optimum results obtained from simulation through the recognition systems (neural networks and normalized cross-correlation). The results demonstrate that the performance of recognition depends on the discriminant power of shape representation. The higher Fisherpsilas ratio gives the better recognition performance. The best performance is achieved by using shape representation that has the maximum Fisherpsilas ratio.