UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis

• URL: http://arxiv.org/abs/2507.03341v1
• Date: Fri, 04 Jul 2025 07:00:41 GMT
• Title: UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis
• Authors: Zhuo Li, Xuhang Chen, Shuqiang Wang,
• Abstract summary: This paper explores the use of a generative adversarial network (GAN) framework tailored for fUS image synthesis.<n>The proposed method incorporates architectural enhancements, including feature enhancement modules and techniques, aiming to improve the fidelity and physiological plausibility of generated images.<n>The study evaluates the performance of the framework against existing generative models, demonstrating its capability to produce high-quality fUS images.
• Score: 11.664045852204586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Functional ultrasound (fUS) is a neuroimaging technique known for its high spatiotemporal resolution, enabling non-invasive observation of brain activity through neurovascular coupling. Despite its potential in clinical applications such as neonatal monitoring and intraoperative guidance, the development of fUS faces challenges related to data scarcity and limitations in generating realistic fUS images. This paper explores the use of a generative adversarial network (GAN) framework tailored for fUS image synthesis. The proposed method incorporates architectural enhancements, including feature enhancement modules and normalization techniques, aiming to improve the fidelity and physiological plausibility of generated images. The study evaluates the performance of the framework against existing generative models, demonstrating its capability to produce high-quality fUS images under various experimental conditions. Additionally, the synthesized images are assessed for their utility in downstream tasks, showing improvements in classification accuracy when used for data augmentation. Experimental results are based on publicly available fUS datasets, highlighting the framework's effectiveness in addressing data limitations.

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