High Quality DTS/CBCT Reconstruction Based On Flat Panel Detector

The detector is used as a core component of the data acquisition system whose stability and sensitivity are the keys to ensure the quality of tomography.At present,the flat panel detector commonly used for the cone beam CT(CBCT)and digital tomosynthesis(DTS)has been developed from the multi-row detector for multi-slice spiral CT which evolved through the original single detector for parallel X-ray and the multi-detector for fan beam X-ray.Depending on small thickness and high X-ray utilization rate,the flat panel detector achieves more efficient acquisition integrated with the reading system.The two-dimensional size of the flat panel detector keeps the projection away from distortion,achieving high spatial resolution image.As the most state-of-the-art detection technology,the flat panel detector CT data acquisition system has entered the clinical application and has a broad prospect for development.DTS(Digital Tomosynthesis)is a new tomographic technology based on traditional geometric imaging and flat panel detection system,using digital image algorithm to achieve image reconstruction in any slice paralleled to the plane of detector.DBT(Digital Breast Tomosynthesis)imaging which applys DTS to 3D imaging of breast has great clinical value for its low radiation dose and high detection rates.However,the quality of low-dose DBT is always limited by image noise.The classical weighted least squares algorithm can be used to optimize the projection data,so as to improve the quality of the reconstructed image.Currently,how to improve the accuracy of the noise model is the key point to improve the performance of noise reduction for the least squares algorithm.In this work,we propose to utilize the penalized weighted least square algorithm incorporating the accurate modeling of the variance of the projection data and the noise correlation in the flat panel detector for low-dose DBT projection recovery.Specially,we model the noise for the quantal noise as well as the electronic noise in the DBT system and then rebuild the penalized weighted least squares algorithm based on noise correlation for projection data recovery;After that,the processed projection data are reconstructed by filter back-projection algorithm.The reconstruction results show that the PLWS method has promising effect on noise suppression,and the quality of reconstructed DBT image is apparently improved.So far,the panel detection system has developed as the mainstream of the CBCT data acquisition system.Four-dimensional cone beam CT(4D-CBCT)imaging can provide location of accurate real-time breathing information for image guided radiotherapy.How to improve the accuracy of 4D-CBCT reconstruction image is the hot area of research.PICCS algorithm performs remarkably in all 4D-CBCT reconstruction algorithms based on CS theory.The improved PICCS algorithm proposed in this paper improves the prior image on the basis of the traditional PICCS algorithm.According to the location information of each phase,the corresponding prior image is constructed,which completely eliminates the motion blur of the reconstructed image caused by the mismatch of the projection data.Meanwhile,the data fidelity model of the proposed method is consistent with the traditional PICCS algorithm.The experimental results show that the reconstruction image of proposed method has a more clear organization boundary compared with that of traditional PICCS algorithm.In method,the motion artifact is reduced and the image resolution is improved.