Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks

Albay E., Demir U., Ünal G.

1st International Workshop on PRedictive Intelligence in MEdicine (PRIME), Granada, Nikaragua, 16 Eylül 2018, cilt.11121, ss.155-163 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 11121
  • Doi Numarası: 10.1007/978-3-030-00320-3_19
  • Basıldığı Şehir: Granada
  • Basıldığı Ülke: Nikaragua
  • Sayfa Sayıları: ss.155-163
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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.