Int J Comput Assist Radiol Surg - MR image reconstruction based on framelets and nonlocal total variation using split Bregman method.

Tópicos

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Resumo

RPOSE: An efficient algorithm for magnetic resonance (MR) image reconstruction is needed, especially when sparse sampling is employed to accelerate data acquisition. The aim of this paper is to solve the sparse MRI problem based on nonlocal total variation (NLTV) and framelet sparsity using the split Bregman algorithm. A new method was developed and tested in a variety of MR image acquisitions.METHODS: The proposed method minimizes a linear combination of NLTV, least square data fitting and framelet terms to reconstruct the MR images from undersampled k-space data. The NLTV and framelet sparsity are taken as the L1-regularization functional and solved by using the split Bregman method. Experiments were conducted to compare the proposed algorithm with several different reconstruction methods, including the operator splitting algorithm, variable splitting method, composite splitting algorithm and its accelerated version called the fast composite splitting algorithm. A detailed evaluation study was done on the reconstruction of MR images which represent varying degrees of object structural complexity. Both qualitative visualization-based and quantitative metric-based evaluations were done.RESULTS: Numerical results on various data corresponding to different sampling rates showed the advantages of the new method in preserving geometrical features, textures and fine structures. The proposed algorithm was compared with previous methods in terms of the reconstruction accuracy and computational complexity with favorable results.CONCLUSION: An efficient new algorithm was developed for compressed MR image reconstruction based on NLTV and framelet sparsity. The algorithm effectively solves a hybrid regularizer based on framelet sparsity and NLTV using the split Bregman method. NLTV makes the recovered image quality sharper by preserving the edges or boundaries more accurately, and framelets often improve image quality. The comparison with alternative method yielded results that demonstrate the superiority of the proposed algorithm for compressed MR image reconstruction.

Resumo Limpo

rpose effici algorithm magnet reson mr imag reconstruct need especi spars sampl employ acceler data acquisit aim paper solv spars mri problem base nonloc total variat nltv framelet sparsiti use split bregman algorithm new method develop test varieti mr imag acquisitionsmethod propos method minim linear combin nltv least squar data fit framelet term reconstruct mr imag undersampl kspace data nltv framelet sparsiti taken lregular function solv use split bregman method experi conduct compar propos algorithm sever differ reconstruct method includ oper split algorithm variabl split method composit split algorithm acceler version call fast composit split algorithm detail evalu studi done reconstruct mr imag repres vari degre object structur complex qualit visualizationbas quantit metricbas evalu doneresult numer result various data correspond differ sampl rate show advantag new method preserv geometr featur textur fine structur propos algorithm compar previous method term reconstruct accuraci comput complex favor resultsconclus effici new algorithm develop compress mr imag reconstruct base nltv framelet sparsiti algorithm effect solv hybrid regular base framelet sparsiti nltv use split bregman method nltv make recov imag qualiti sharper preserv edg boundari accur framelet often improv imag qualiti comparison altern method yield result demonstr superior propos algorithm compress mr imag reconstruct

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