An Improved Total Variation Minimization Method Using Prior Images and Split-Bregman Method in CT Reconstruction.
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.
Key Laboratory of Optoelectronics Technology & System, Chongqing University, Ministry of Education, Chongqing 400044, China.
Luzhen
0000-0002-6378-8398
L
Deng
Key Laboratory of Optoelectronics Technology & System, Chongqing University, Ministry of Education, Chongqing 400044, China.
Peng
0000-0002-7420-2161
P
Feng
Key Laboratory of Optoelectronics Technology & System, Chongqing University, Ministry of Education, Chongqing 400044, China.
Mianyi
0000-0002-3093-2920
M
Chen
Key Laboratory of Optoelectronics Technology & System, Chongqing University, Ministry of Education, Chongqing 400044, China.
Peng
0000-0002-6196-9590
P
He
Key Laboratory of Optoelectronics Technology & System, Chongqing University, Ministry of Education, Chongqing 400044, China.
Biao
0000-0002-3957-5405
B
Wei
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