EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image
  Reconstruction
Yipeng Sun Yixing Huang Linda-Sophie Schneider Mareike Thies Mingxuan Gu Siyuan Mei Siming Bayer Andreas Maier
Computed Tomography (CT) image reconstruction is crucial for accurate
diagnosis and deep learning approaches have demonstrated significant potential
in improving reconstruction quality. However, the choice of loss function
profoundly affects the reconstructed images. Traditional mean squared error
loss often produces blurry images lacking fine details, while alternatives
designed to improve may introduce structural artifacts or other undesirable
effects. To address these limitations, we propose Eagle-Loss, a novel loss
function designed to enhance the visual quality of CT image reconstructions.
Eagle-Loss applies spectral analysis of localized features within gradient
changes to enhance sharpness and well-defined edges. We evaluated Eagle-Loss on
two public datasets across low-dose CT reconstruction and CT field-of-view
extension tasks. Our results show that Eagle-Loss consistently improves the
visual quality of reconstructed images, surpassing state-of-the-art methods
across various network architectures. Code and data are available at
\url{https://github.com/sypsyp97/Eagle_Loss}.