SU‐E‐I‐07: Response Characteristics and Signal Conversion Modeling of KV Flat‐Panel Detector in Cone Beam CT System

Abstract

Purpose:The flat‐panel detector response characteristics are investigated to optimize the scanning parameter considering the image quality and less radiation dose. The signal conversion model is also established to predict the tumor shape and physical thickness changes.Methods:With the ELEKTA XVI system, the planar images of 10cm water phantom were obtained under different image acquisition conditions, including tube voltage, electric current, exposure time and frames. The averaged responses of square area in center were analyzed using Origin8.0. The response characteristics for each scanning parameter were depicted by different fitting types. The transmission measured for 10cm water was compared to Monte Carlo simulation. Using the quadratic calibration method, a series of variable‐thickness water phantoms images were acquired to derive the signal conversion model. A 20cm wedge water phantom with 2cm step thickness was used to verify the model. At last, the stability and reproducibility of the model were explored during a four week period.Results:The gray values of image center all decreased with the increase of different image acquisition parameter presets. The fitting types adopted were linear fitting, quadratic polynomial fitting, Gauss fitting and logarithmic fitting with the fitting R‐Square 0.992, 0.995, 0.997 and 0.996 respectively. For 10cm water phantom, the transmission measured showed better uniformity than Monte Carlo simulation. The wedge phantom experiment show that the radiological thickness changes prediction error was in the range of (‐4mm, 5mm). The signal conversion model remained consistent over a period of four weeks.Conclusion:The flat‐panel response decrease with the increase of different scanning parameters. The preferred scanning parameter combination was 100kV, 10mA, 10ms, 15frames. It is suggested that the signal conversion model could effectively be used for tumor shape change and radiological thickness prediction.Supported by National Natural Science Foundation of China (81101132, 11305203) and Natural Science Foundation of Anhui Province (11040606Q55, 1308085QH138)