title: Median prior constrained TV algorithm for sparse view low-dose CT reconstruction.
abstract: It is known that lowering the X-ray tube current (mAs) or tube voltage (kVp) and simultaneously reducing the total number of X-ray views (sparse view) is an effective means to achieve low-dose in computed tomography (CT) scan. However, the associated image quality by the conventional filtered back-projection (FBP) usually degrades due to the excessive quantum noise. Although sparse-view CT reconstruction algorithm via total variation (TV), in the scanning protocol of reducing X-ray tube current, has been demonstrated to be able to result in significant radiation dose reduction while maintain image quality, noticeable patchy artifacts still exist in reconstructed images. In this study, to address the problem of patchy artifacts, we proposed a median prior constrained TV regularization to retain the image quality by introducing an auxiliary vector m in register with the object. Specifically, the approximate action of m is to draw, in each iteration, an object voxel toward its own local median, aiming to improve low-dose image quality with sparse-view projection measurements. Subsequently, an alternating optimization algorithm is adopted to optimize the associative objective function. We refer to the median prior constrained TV regularization as "TV_MP" for simplicity. Experimental results on digital phantoms and clinical phantom demonstrated that the proposed TV_MP with appropriate control parameters can not only ensure a higher signal to noise ratio (SNR) of the reconstructed image, but also its resolution compared with the original TV method.
authors: National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China; Key Laboratory of Instrumentation Science & Dynamic Measurement, North University of China, Taiyuan 030051, China.
authors: Yi
authors: Y
authors: Liu
authors: National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China; Key Laboratory of Instrumentation Science & Dynamic Measurement, North University of China, Taiyuan 030051, China.
authors: Hong
authors: H
authors: Shangguan
authors: National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China; Key Laboratory of Instrumentation Science & Dynamic Measurement, North University of China, Taiyuan 030051, China.
authors: Quan
authors: Q
authors: Zhang
authors: East China University of Science & Technology, Shanghai, China; Laboratory of Image Science and Technology (LIST), Southeast University, Nanjing 210096, China.
authors: Hongqing
authors: H
authors: Zhu
authors: Laboratory of Image Science and Technology (LIST), Southeast University, Nanjing 210096, China; Centre de Recherche en Information Biomedicale Sino-Francais (CRIBs), Rennes 35042, France.
authors: Huazhong
authors: H
authors: Shu
authors: National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China; Key Laboratory of Instrumentation Science & Dynamic Measurement, North University of China, Taiyuan 030051, China. Electronic address: gzgtg@163.com.
authors: Zhiguo
authors: Z
authors: Gui
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D006801:Humans; D008490:Medical Informatics / Q000379:methods; D015233:Models, Statistical; D016011:Normal Distribution; D019047:Phantoms, Imaging; D011336:Probability; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014965:X-Rays
keywords: Low-dose CT; Median prior; Reconstruction; Sparse-view; Total variation