An Improved Total Variation Minimization Method Using Prior Images and Split-Bregman Method in CT Reconstruction.

Luzhen Deng,Peng Feng,Biao Wei,Peng He,Mianyi Chen

doi:10.1155/2016/3094698

Abstract

Compressive Sensing (CS) theory has great potential for reconstructing Computed Tomography (CT) images from sparse-views projection data and Total Variation- (TV-) based CT reconstruction method is very popular. However, it does not directly incorporate prior images into the reconstruction. To improve the quality of reconstructed images, this paper proposed an improved TV minimization method using prior images and Split-Bregman method in CT reconstruction, which uses prior images to obtain valuable previous information and promote the subsequent imaging process. The images obtained asynchronously were registered via Locally Linear Embedding (LLE). To validate the method, two studies were performed. Numerical simulation using an abdomen phantom has been used to demonstrate that the proposed method enables accurate reconstruction of image objects under sparse projection data. A real dataset was used to further validate the method.

Highlights

Research on how to reduce Computed Tomography (CT) scanning dose of patients while the image quality is not deteriorated has very important significance in theory and in practical applications [1]
In practice we need to register these reconstructed images before further processing, and X-ray CT Geometrical Calibration via Locally Linear Embedding (LLE) which was provided by Chen et al [18] can be used
We find f⃗ PISPTV contains less artifacts, and the inner distribution near edge is more uniform than f⃗ Algebraic Reconstruction Technique (ART)-Total Variation (TV)

Summary

Introduction

Research on how to reduce Computed Tomography (CT) scanning dose of patients while the image quality is not deteriorated has very important significance in theory and in practical applications [1]. Comparing with traditional CT reconstruction approaches [2,3,4], algorithms based on Compressive Sensing (CS) [5,6,7,8,9,10] are more popular with the conditions of incomplete projections. They still can be improved by bringing in prior images. The information embedded in previous scanning is called prior knowledge which is valuable for reconstructing better images with low-dose in the following CT scanning [11,12,13,14,15]. We introduce the proposed algorithm show the results in the third section, and conclude the paper in the last section

Theory and Method

Simulation and Experiment

Methods

Conclusion