  Prenatal ultrasound imaging is the first-choice modality to assess fetal
health. Medical image datasets for AI and ML methods must be diverse (i.e.
diagnoses, diseases, pathologies, scanners, demographics, etc), however there
are few public ultrasound fetal imaging datasets due to insufficient amounts of
clinical data, patient privacy, rare occurrence of abnormalities in general
practice, and limited experts for data collection and validation. To address
such data scarcity, we proposed generative adversarial networks (GAN)-based
models, diffusion-super-resolution-GAN and transformer-based-GAN, to synthesise
images of fetal ultrasound brain planes from one public dataset. We reported
that GAN-based methods can generate 256x256 pixel size of fetal ultrasound
trans-cerebellum brain image plane with stable training losses, resulting in
lower FID values for diffusion-super-resolution-GAN (average 7.04 and lower FID
5.09 at epoch 10) than the FID values of transformer-based-GAN (average 36.02
and lower 28.93 at epoch 60). The results of this work illustrate the potential
of GAN-based methods to synthesise realistic high-resolution ultrasound images,
leading to future work with other fetal brain planes, anatomies, devices and
the need of a pool of experts to evaluate synthesised images. Code, data and
other resources to reproduce this work are available at
\url{https://github.com/budai4medtech/midl2023}.
