Multi-Scale Texture Loss for CT denoising with GANs

• URL: http://arxiv.org/abs/2403.16640v1
• Date: Mon, 25 Mar 2024 11:28:52 GMT
• Title: Multi-Scale Texture Loss for CT denoising with GANs
• Authors: Francesco Di Feola, Lorenzo Tronchin, Valerio Guarrasi, Paolo Soda,
• Abstract summary: Generative Adversarial Networks (GANs) have proved as a powerful framework for denoising applications in medical imaging. This work presents a loss function that leverages the intrinsic multi-scale nature of the Gray-Level-Co-occurrence Matrix (GLCM) Our approach also introduces a self-attention layer that dynamically aggregates the multi-scale texture information extracted from the images.
• Score: 0.9349653765341301
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generative Adversarial Networks (GANs) have proved as a powerful framework for denoising applications in medical imaging. However, GAN-based denoising algorithms still suffer from limitations in capturing complex relationships within the images. In this regard, the loss function plays a crucial role in guiding the image generation process, encompassing how much a synthetic image differs from a real image. To grasp highly complex and non-linear textural relationships in the training process, this work presents a loss function that leverages the intrinsic multi-scale nature of the Gray-Level-Co-occurrence Matrix (GLCM). Although the recent advances in deep learning have demonstrated superior performance in classification and detection tasks, we hypothesize that its information content can be valuable when integrated into GANs' training. To this end, we propose a differentiable implementation of the GLCM suited for gradient-based optimization. Our approach also introduces a self-attention layer that dynamically aggregates the multi-scale texture information extracted from the images. We validate our approach by carrying out extensive experiments in the context of low-dose CT denoising, a challenging application that aims to enhance the quality of noisy CT scans. We utilize three publicly available datasets, including one simulated and two real datasets. The results are promising as compared to other well-established loss functions, being also consistent across three different GAN architectures. The code is available at: https://github.com/FrancescoDiFeola/DenoTextureLoss

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