UltraDfeGAN：用于高保真功能超声合成的细节增强生成对抗网络
UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis
AI总结：
本文提出UltraDfeGAN，通过特征增强模块和归一化技术改进GAN架构，用于高保真功能超声图像合成，并在公开数据集上验证其可提升下游分类任务的数据增强效果。
摘要：
功能超声（fUS）是一种以高时空分辨率著称的神经成像技术，能够通过神经血管耦合实现脑活动的无创观测。尽管其在新生儿监测和术中引导等临床应用中具有潜力，但fUS的发展仍面临数据稀缺以及生成逼真fUS图像能力受限等挑战。本文探索了一种专为fUS图像合成定制的生成对抗网络（GAN）框架。所提方法融合了架构增强，包括特征增强模块和归一化技术，旨在提升生成图像的保真度和生理合理性。研究将该框架与现有生成模型进行性能评估，证明其在多种实验条件下能够生成高质量fUS图像。此外，还评估了合成图像在下游任务中的实用性，结果显示将其用于数据增强时可提高分类准确率。实验结果基于公开可用的fUS数据集，凸显了该框架在解决数据局限性方面的有效性。
英文摘要：
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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