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Anisotropic neural deblurring for MRI acceleration

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

MRI has become the tool of choice for brain imaging, providing unrivalled contrast between soft tissues, as well as a wealth of information about anatomy, function, and neurochemistry. Image quality, in terms of spatial resolution and noise, is strongly dependent on acquisition duration. A typical brain MRI scan may last several minutes, with total protocol duration often exceeding 30 minutes. Long scan duration leads to poor patient experience, long waiting time for appointments, and high costs. Therefore, shortening MRI scans is crucial. In this paper, we investigate the enhancement of low-resolution (LR) brain MRI scanning, to enable shorter acquisition times without compromising the diagnostic value of the images.

Methods

We propose a novel fully convolutional neural enhancement approach. It is optimized for accelerated LR MRI acquisitions obtained by reducing the acquisition matrix size only along phase encoding direction. The network is trained to transform the LR acquisitions into corresponding high-resolution (HR) counterparts in an end-to-end manner. In contrast to previous neural-based MRI enhancement algorithms, such as DAGAN, the LR images used for training are real acquisitions rather than smoothed, downsampled versions of the HR images.

Results

The proposed method is validated qualitatively and quantitatively for an acceleration factor of 4. Favourable comparison is demonstrated against the state-of-the-art DeblurGAN and DAGAN algorithms in terms of PSNR and SSIM scores. The result was further confirmed by an image quality rating experiment performed by four senior neuroradiologists.

Conclusions

The proposed method may become a valuable tool for scan time reduction in brain MRI. In continuation of this research, the validation should be extended to larger datasets acquired for different imaging protocols, and considering several MRI machines produced by different vendors.

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Funding

The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel

    Maya Mayberg

  2. Department of Diagnostic Imaging, Sheba Medical Center, Affiliated to the Sackler School of Medicine, Tel-Aviv University, Ramat Gan, Israel

    Michael Green, Mark Vasserman, Dominique Raichman, Eugenia Belenky, Michael Wolf, Shai Shrot, Eli Konen & Arnaldo Mayer

  3. Manuel and Raquel Klachky Chair of Image Processing, School of Electrical Engineering, Tel-Aviv University, Tel Aviv, Israel

    Nahum Kiryati

Authors
  1. Maya Mayberg
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  2. Michael Green
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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Mayberg, M., Green, M., Vasserman, M. et al. Anisotropic neural deblurring for MRI acceleration. Int J CARS 17, 315–327 (2022). https://doi.org/10.1007/s11548-021-02535-6

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  • DOI: https://doi.org/10.1007/s11548-021-02535-6

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