Texture-Aware StarGAN for CT data harmonisation

• URL: http://arxiv.org/abs/2503.15058v1
• Date: Wed, 19 Mar 2025 09:50:32 GMT
• Title: Texture-Aware StarGAN for CT data harmonisation
• Authors: Francesco Di Feola, Ludovica Pompilio, Cecilia Assolito, Valerio Guarrasi, Paolo Soda,
• Abstract summary: We propose a texture-aware StarGAN for CT data harmonization, enabling one-to-many translations across different reconstruction kernels.<n>Our approach introduces a multi-scale texture loss function that embeds texture information across different spatial and angular scales into the harmonization process.
• Score: 0.8714814768600079
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Computed Tomography (CT) plays a pivotal role in medical diagnosis; however, variability across reconstruction kernels hinders data-driven approaches, such as deep learning models, from achieving reliable and generalized performance. To this end, CT data harmonization has emerged as a promising solution to minimize such non-biological variances by standardizing data across different sources or conditions. In this context, Generative Adversarial Networks (GANs) have proved to be a powerful framework for harmonization, framing it as a style-transfer problem. However, GAN-based approaches still face limitations in capturing complex relationships within the images, which are essential for effective harmonization. In this work, we propose a novel texture-aware StarGAN for CT data harmonization, enabling one-to-many translations across different reconstruction kernels. Although the StarGAN model has been successfully applied in other domains, its potential for CT data harmonization remains unexplored. Furthermore, our approach introduces a multi-scale texture loss function that embeds texture information across different spatial and angular scales into the harmonization process, effectively addressing kernel-induced texture variations. We conducted extensive experimentation on a publicly available dataset, utilizing a total of 48667 chest CT slices from 197 patients distributed over three different reconstruction kernels, demonstrating the superiority of our method over the baseline StarGAN.

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