MR image reconstruction based on comprehensive sparse prior |
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Authors: | ,
Ding Xinghao
,
Chen Xianbo
,
Huang Yue
,
Mi Zengyuan |
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Affiliation: | School of Information Science and Technology, Xiamen University, Xiamen 361005, China |
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Abstract: | 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 nonzero 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. |
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Keywords: | Image-wise sparse prior Patch-wise sparse prior Beta-Bernoulli process Low-dimensional-structure Compressive sampling |
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