  Deep learning offers potential for various healthcare applications, yet
requires extensive datasets of curated medical images where data privacy, cost,
and distribution mismatch across various acquisition centers could become major
problems. To overcome these challenges, we propose a generative adversarial
network (SkullGAN) to create large datasets of synthetic skull CT slices,
geared towards training models for transcranial ultrasound. With wide ranging
applications in treatment of essential tremor, Parkinson's, and Alzheimer's
disease, transcranial ultrasound clinical pipelines can be significantly
optimized via integration of deep learning. The main roadblock is the lack of
sufficient skull CT slices for the purposes of training, which SkullGAN aims to
address. Actual CT slices of 38 healthy subjects were used for training. The
generated synthetic skull images were then evaluated based on skull density
ratio, mean thickness, and mean intensity. Their fidelity was further analyzed
using t-distributed stochastic neighbor embedding (t-SNE), Fr\'echet inception
distance (FID) score, and visual Turing test (VTT) taken by four staff clinical
radiologists. SkullGAN-generated images demonstrated similar quantitative
radiological features to real skulls. t-SNE failed to separate real and
synthetic samples from one another, and the FID score was 49. Expert
radiologists achieved a 60\% mean accuracy on the VTT. SkullGAN makes it
possible for researchers to generate large numbers of synthetic skull CT
segments, necessary for training neural networks for medical applications
involving the human skull, such as transcranial focused ultrasound, mitigating
challenges with access, privacy, capital, time, and the need for domain
expertise.
