  Research exploring CycleGAN-based synthetic image generation has recently
accelerated in the medical community, as it is able to leverage unpaired
datasets effectively. However, clinical acceptance of these synthetic images
pose a significant challenge as they are subject to strict evaluation
protocols. A commonly established drawback of the CycleGAN, the introduction of
artifacts in generated images is unforgivable in the case of medical images. In
an attempt to alleviate this drawback, we explore different constraints of the
CycleGAN along with investigation of adaptive control of these constraints. The
benefits of imposing additional constraints on the CycleGAN, in the form of
structure retaining losses is also explored. A generalized frequency loss
inspired by arxiv:2012.12821 that preserves content in the frequency domain
between source and target is investigated and compared with existing losses
such as the MIND loss arXiv:1809.04536. CycleGAN implementations from the
ganslate framework (https://github.com/ganslate-team/ganslate) are used for
experimentation in this thesis. Synthetic images generated from our methods are
quantitatively and qualitatively investigated and outperform the baseline
CycleGAN and other approaches. Furthermore, no observable artifacts or loss in
image quality is found, which is critical for acceptance of these synthetic
images. The synthetic medical images thus generated are also evaluated using
domain-specific evaluation and using segmentation as a downstream task, in
order to clearly highlight their applicability to clinical workflows.
