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Magnetic resonance image reconstruction using fast interpolated compressed sensing

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Abstract

Application of the compressed sensing (CS) in magnetic resonance imaging (MRI) significantly reduces the scan time as it is capable of recovering images at diagnostic resolution from a few random measurements in the k-space. In 2D multi-slice MRI, a strong inter-slice correlation exists which promotes further reduction in the scan time. In this paper, first, a fast interpolation technique is proposed to estimate missing samples in the k-space of some highly undersampled slices from neighboring low undersampled slices. Next, a novel wavelet tree sparsity based CS reconstruction method is developed for the reconstruction of images from raw 2D multi-slice data. The combination of fast interpolation and CS reconstruction gives accurate reconstruction of multi-slice MR images at significantly less computational time. Simulation results show that the proposed method gives better preservation of edges and other image details with less computational time than some of the recently developed fast CS reconstruction techniques.

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  1. http://mridata.org/.

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Authors and Affiliations

  1. Department of E.C.E, Tezpur University, Tezpur, India

    Sumit Datta & Bhabesh Deka

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  1. Sumit Datta
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  2. Bhabesh Deka
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Correspondence to Bhabesh Deka.

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Datta, S., Deka, B. Magnetic resonance image reconstruction using fast interpolated compressed sensing. J Opt 47, 154–165 (2018). https://doi.org/10.1007/s12596-017-0428-8

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  • DOI: https://doi.org/10.1007/s12596-017-0428-8

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