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Compressive Sensing MRI Reconstruction with Shearlet Sparsity and non-Convex Hybrid Total Variation

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Abstract

Compressive sensing MRI methods attempt to reconstruct MR image from partially sampled k-space data to reduce the acquisition time of MRI. The quality of reconstruction in CS-MRI methods highly depends on the priors used in the reconstruction process. In this work, we formulate the MRI reconstruction method with a prior comprised of shearlet domain sparsity and non-convex hybrid TV. Shearlet transform effectively represents rich geometrical features of the image compared to wavelet transform. The hybrid TV provides the advantage of first- and second-order TV, while non-convex model preserves edges better compared to the convex model. The proposed model is solved using alternating direction method of multipliers method. The qualitative and quantitative analysis of the reconstruction results demonstrates the efficiency of the proposed method in reconstructing high-quality MR images.

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Acknowledgements

This work is an outcome of the R&D work undertaken in the project under the Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia).

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  1. Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, 440010, India

    Nikhil Dhengre & Saugata Sinha

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  1. Nikhil Dhengre
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  2. Saugata Sinha
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Correspondence to Nikhil Dhengre.

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Dhengre, N., Sinha, S. Compressive Sensing MRI Reconstruction with Shearlet Sparsity and non-Convex Hybrid Total Variation. Appl Magn Reson 53, 1517–1525 (2022). https://doi.org/10.1007/s00723-022-01493-9

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