IEEE Trans Image Process - Bayesian nonparametric dictionary learning for compressed sensing MRI.

Tópicos

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Resumo

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.

Resumo Limpo

develop bayesian nonparametr model reconstruct magnet reson imag mris high undersampl k space data perform dictionari learn part imag reconstruct process end use beta process nonparametr dictionari learn prior repres imag patch spars combin dictionari element size dictionari patchspecif sparsiti pattern infer data addit dictionari learn variabl dictionari learn perform direct compress imag tailor mri consid addit investig total variat penalti term combin dictionari learn model show denois properti dictionari learn remov depend regular paramet noisi set deriv stochast optim algorithm base markov chain mont carlo bayesian model use altern direct method multipli effici perform total variat minim present empir result sever mri show propos regular framework can improv reconstruct accuraci method

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