Noise reduction by multiple path neural network using Attention mechanisms with an emphasis on robustness against Errors: A pilot study on brain Diffusion-Weighted images.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date:

Abstract

PURPOSE: In deep learning-based noise reduction, larger networks offer advanced and complex functionality by utilizing its greater degree of freedom, but come with increased unpredictability, raising the potential risk of unforeseen errors. Here, we introduce a novel denoising model for diffusion-weighted images that intentionally limits the network output freedom by incorporating multiple pathways with varying degrees of freedom, with the aim of minimizing the chance of unintended alterations to the input. The purpose of this pilot study is to assess the model's ability to perform effective denoising under the constraints.

Authors

  • Yasuhiko Tachibana
    National Institute of Radiological Sciences, Chiba, Chiba, Japan.
  • Yujiro Otsuka
    Department of Radiology, Juntendo University School of Medicine.
  • Hayato Nozaki
    Department of Radiology, Graduate School of Medicine, Juntendo University, Japan.
  • Koji Kamagata
  • Shinichiro Mori
    Research Center for Charged Particle Therapy, National Institute of Radiological Sciences, Inage-ku, Chiba 263-8555, Japan.. Electronic address: mori.shinichiro@qst.go.jp.
  • Yuya Saito
  • Shigeki Aoki