Densely connected wavelet-based autoencoder for MR image reconstruction

Aghabiglou A., Ekşioğlu E. M.

45th International Conference on Telecommunications and Signal Processing, TSP 2022, Virtual, Online, Çek Cumhuriyeti, 13 - 15 Temmuz 2022, ss.212-215 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/tsp55681.2022.9851354
  • Basıldığı Şehir: Virtual, Online
  • Basıldığı Ülke: Çek Cumhuriyeti
  • Sayfa Sayıları: ss.212-215
  • Anahtar Kelimeler: Deep learning, Densely Connected Residual Network, Magnetic resonance imaging, MR Image Reconstruction
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

© 2022 IEEE.Recently, methods based on deep learning have been introduced to the literature as a solution for accelerating magnetic resonance imaging technique. However, Image reconstruction from subsampled data is an ill-posed problem. In the current study, the wavelet package has been applied to deep networks. The replacement of the conventional downsampling and upsampling layers with Discrete Wavelet Transform (DWT) and Inverse Wavelet Transform (IWT) improved the reconstruction results. Moreover, the consequence of this substitution has been investigated on potent densely connected deep networks. The proposed novelty resulted in promising performance improvement in MR Image reconstruction.