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K sparse autoencoder-based accelerated reconstruction of magnetic resonance imaging

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Abstract

Owing to the sequential collection of phase encoded data in k-space, magnetic resonance (MR) imaging suffers from long acquisition time. One possible measure to reduce the long acquisition time is to reconstruct MR image using a subset of k-space MR data rather than the complete set. In this work, we propose to implement a K sparse autoencoder model for reconstruction of MR image from undersampled k-space data. Autoencoder models, which have shown great ability in capturing the complex features of input data, can be used to reconstruct high-quality MR image. The reconstruction process involved solving an optimization problem whose solution was expected to satisfy the data consistency and also lie in close proximity to the output space of trained K sparse autoencoder. Observing the effect of sparsity value enforced by K sparse autoencoder on the reconstructed output, we implemented the cascaded form of reconstruction, incorporating three K sparse autoencoders with three different K values. Using MR-PD and MR-T1 images, reconstruction performance of the proposed approach was compared with those of the conventional reconstruction approaches. The quantitative as well as the qualitative analysis of the reconstructed images, obtained using the proposed approach, validates the efficiency of the proposed approach.

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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, 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. K sparse autoencoder-based accelerated reconstruction of magnetic resonance imaging. Vis Comput 38, 837–847 (2022). https://doi.org/10.1007/s00371-020-02054-6

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