EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction
This work addresses image quality issues in CT reconstruction for medical diagnosis, representing an incremental improvement with a new loss function.
The paper tackled the problem of blurry and artifact-prone CT image reconstruction by proposing Eagle-Loss, a novel loss function that enhances sharpness and edges, resulting in improved visual quality that surpasses state-of-the-art methods on low-dose CT reconstruction and CT field-of-view extension tasks.
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}.