Kavli Affiliate: Zhuo Li
| First 5 Authors: Zhuo Li, Zhuo Li, , ,
| Summary:
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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