Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks

  • Enes Albay
  • Ugur Demir
  • Gozde Unal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11121)


Spatial resolution is one of the main constraints in diffusion Magnetic Resonance Imaging (dMRI). Increasing resolution leads to a decrease in SNR of the diffusion images. Acquiring high resolution images without reducing SNRs requires larger magnetic fields and long scan times which are typically not applicable in the clinical settings. Currently feasible voxel size is around 1 mm\( ^{3} \) for a diffusion image. In this paper, we present a deep neural network based post-processing method to increase the spatial resolution in diffusion MRI. We utilize Generative Adversarial Networks (GANs) to obtain a higher resolution diffusion MR image in the spatial dimension from lower resolution diffusion images. The obtained real data results demonstrate a first time proof of concept that GANs can be useful in super-resolution problem of diffusion MRI for upscaling in the spatial dimension.


Magnetic resonance imaging (MRI) Diffusion MRI (dMRI) Super resolution Generative adversarial networks (GANs) 


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Istanbul Technical UniversityIstanbulTurkey

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