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Compressed Sensing MRI Reconstruction Using Generative Adversarial Network with Rician De-noising

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Abstract

Compressed sensing magnetic resonance imaging (CS-MRI) has been a widely studied field in biomedical signal processing owing to its success and practical MR machines integrated with the technology already rolling on in market. Since then, a decade-long research in the field has paved its way into deep-learning techniques being incorporated into the field to simplify some of the complex, theoretical issues that the original setup pose, without compromising efficiency. These machine-learning methods are particularly useful in context of input signal adaptability. The data acquisition process of MRI is noisy in nature with various types of noises associated, such as Rician noise, Gaussian Noise, and motion artifacts like breathing artifacts. However, the repercussions of noise has been scarcely incorporated into the study of most of these methods. In this context, training a model directly with the obtained data is somewhat inefficient. This paper proposes a Generative Adversarial Network (GAN)-based technique for obtaining a concise and more representative model which attempts to be a more robust noise immune network. Keeping in mind increased efficiency and reduced reconstruction time, the proposed method attempts to address the problem of MR image reconstruction. The results obtained from the proposed method has been compared with traditional CS-MRI and other contemporary methods using objective parameters such as PSNR, SSIM index, BRISQUE and FID score, and subjective parameters such as mean opinion score and LPIPS.

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Acknowledgements

The authors would like to thank Center for High Performance Computing, a collaboration of Center for Development of Advanced computing (C-DAC) and Assam Engineering College, Guwahati, Assam, India, for allowing access to the laboratory to perform necessary research.

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Authors and Affiliations

  1. Department of E.C.E., Gauhati University, Guwahati, Assam, India

    Mrinmoy Sandilya

  2. School of ECE, KLE Technological University, Hubli, Karnataka, India

    S R Nirmala

  3. Department of E.T.E., Assam Engineering College, Guwahati, Assam, India

    Navajit Saikia

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Correspondence to Mrinmoy Sandilya.

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Dataset—publicly available FastMRI dataset, url - https://fastmri.med.nyu.edu/

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The following packages/repositories have been made use of or been customized in the manuscript: PIX2PIX - https://github.com/phillipi/pix2pix Keras WGAN -https://github.com/keras-team/keras-contrib/blob/master/examples/improved_wgan.py liveplotloss - https://github.com/stared/livelossplot VGG -16 -https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3 SPORCO - https://github.com/bwohlberg/sporco

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I give my consent for the publication of the following manuscript, which can include photograph(s) and/or details within the text (“Material”) to be published in the Journal—Applied Magnetic Resonance. I have discussed this consent statement with S.R. Nirmala and Navajit Saikia, who are authors of this paper. I understand that this copy may be available in both print and on the Internet, and will be available to a broader audience through marketing channels and other third parties. Therefore, anyone can read material published in the Journal.

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Sandilya, M., Nirmala, S.R. & Saikia, N. Compressed Sensing MRI Reconstruction Using Generative Adversarial Network with Rician De-noising. Appl Magn Reson 52, 1635–1656 (2021). https://doi.org/10.1007/s00723-021-01416-0

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