期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 23, 期 12, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2014.2360122
关键词
Compressed sensing; magnetic resonance imaging; Bayesian nonparametrics; dictionary learning
资金
- National Natural Science Foundation of China [30900328, 61172179, 61103121, 81301278]
- Fundamental Research Funds for the Central Universities [2011121051, 2013121023]
- Natural Science Foundation of Fujian Province, China [2012J05160]
We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k-space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.
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