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

TL;DR

UltraDfeGAN introduces an advanced GAN framework that significantly improves the quality and realism of functional ultrasound images, aiding clinical neuroimaging applications and data augmentation.

Contribution

The paper presents a novel GAN architecture with feature enhancement modules specifically designed for high-fidelity fUS image synthesis, addressing data scarcity and realism challenges.

Findings

Produced high-quality, realistic fUS images

Enhanced classification accuracy in downstream tasks

Outperformed existing generative models in fidelity

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.