MR IMAGE RECONSTRUCTION BASED ON COMPREHENSIVE SPARSE PRIOR

作     者：Ding Xinghao Chen Xianbo Huang Yue Mi Zengyuan 

作者机构：School of Information Science and Technology Xiamen University 

出 版 物：《Journal of Electronics(China)》 (电子科学学刊（英文版）)

年 卷 期：2012年第29卷第6期

页      面：611-616页

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the National Natural Science Foundation of China (No. 30900328, 61172179) the Fundamental Research Funds for the Central Universities (No.2011121051) the Natural Science Foundation of Fujian Province of China (No. 2012J05160) 

主　　题：Image-wise sparse prior Patch-wise sparse prior Beta-Bernoulli process Low-dimensional-structure Compressive sampling 

摘      要：In this paper, a novel Magnetic Resonance (MR) reconstruction framework which combines image-wise and patch-wise sparse prior is proposed. For addressing, a truncated beta-Bernoulli process is firstly employed to enforce sparsity on overlapping image patches emphasizing local structures. Due to its properties, beta-Bernoulli process can adaptive infer the sparsity (number of non-zero coefficients) of each patch, an appropriate dictionary, and the noise variance simultaneously, which are prerequisite for iterative image reconstruction. Secondly, a General Gaussian Distribution (GGD) prior is introduced to engage image-wise sparsity for wavelet coefficients, which can be then estimated by a threshold denoising algorithm. Finally, MR image is reconstructed by patch-wise estimation, image-wise estimation and under-sampled k-space data with least square data fitting. Experimental results have demonstrated that proposed approach exhibits excellent reconstruction performance. Moreover, if the image is full of similar low-dimensional-structures, proposed algorithm has dramatically improved Peak Signal to Noise Ratio (PSNR) 7～9 dB, with comparisons to other state-of-art compressive sampling methods.