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Compressed Sensing Techniques Applied to Medical Images Obtained with Magnetic Resonance

Author

Listed:
  • A. Estela Herguedas-Alonso

    (Department of Physics, University of Oviedo, 33007 Oviedo, Spain)

  • Víctor M. García-Suárez

    (Department of Physics, University of Oviedo, 33007 Oviedo, Spain
    Nanomaterials and Nanotechnology Research Center (CINN-CSIC), Universidad de Oviedo, 33940 El Entrego, Spain)

  • Juan L. Fernández-Martínez

    (Department of Mathematics, University of Oviedo, 33007 Oviedo, Spain)

Abstract

The fast and reliable processing of medical images is of paramount importance to adequately generate data to feed machine learning algorithms that can prevent and diagnose health issues. Here, different compressed sensing techniques applied to magnetic resonance imaging are benchmarked as a means to reduce the acquisition time spent in the collection of data and signals that form the image. It is shown that by using these techniques, it is possible to reduce the number of signals needed and, therefore, substantially decrease the time to acquire the measurements. To this end, different algorithms are considered and compared: the iterative re-weighted least squares, the iterative soft thresholding algorithm, the iterative hard thresholding algorithm, the primal dual algorithm and the log barrier algorithm. Such algorithms have been implemented in different analysis programs that have been used to perform the reconstruction of the images, and it was found that the iterative soft thresholding algorithm gives the optimal results. It is found that the images obtained with this algorithm have lower quality than the original ones, but in any case, the quality should be good enough to distinguish each body structure and detect any health problems under an expert evaluation and/or statistical analysis.

Suggested Citation

  • A. Estela Herguedas-Alonso & Víctor M. García-Suárez & Juan L. Fernández-Martínez, 2023. "Compressed Sensing Techniques Applied to Medical Images Obtained with Magnetic Resonance," Mathematics, MDPI, vol. 11(16), pages 1-19, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3573-:d:1219658
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    References listed on IDEAS

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    1. Irena Orović & Vladan Papić & Cornel Ioana & Xiumei Li & Srdjan Stanković, 2016. "Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-16, October.
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