Fast and accurate compressed sensing model in magnetic resonance imaging with median filter and split Bregman method

Fast and accurate compressed sensing model in magnetic resonance imaging with median filter and split Bregman method

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Magnetic resonance (MR) images have great importance to assist doctors in diagnosing diseases, however, the long MR images scan duration remains the primary obstacle in clinical medicine. Compressed sensing reconstructed technique in MR imaging (CS-MRI) makes it possible to reconstruct a faithful MR image from very few measurements data, which helps to reduce the scan time. The purpose of this study is to improve the accuracy and efficiency of the CS-MRI. The authors propose a fast compressed sensing reconstruction model for MR images that can alleviate the aliasing artefacts that come from the reconstruction process by jointly minimising a total variation term, a fitting data term and a median filter term. Moreover, they accelerate the proposed algorithm by applying the split Bregman method to solve the proposed model. Then, the proposed model is applied to reconstruct a large number of MR images. They also compare the performance of the proposed model with another model. It can be observed from the experimental results that the proposed model has shown higher precision in reconstructing image quality and much more efficiency than the other one. Additionally, they also give a discussion on how to choose proper parameters in the proposed model to obtain more satisfactory results.

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