Exploring constraints on CycleGAN-based CBCT enhancement for adaptive radiotherapy
This work addresses the challenge of clinical acceptance of synthetic medical images by reducing artifacts, which is critical for adaptive radiotherapy workflows.
The paper tackled the problem of artifacts in CycleGAN-generated synthetic CBCT images for adaptive radiotherapy by exploring and imposing additional constraints, such as structure-retaining and frequency-based losses, resulting in synthetic images that outperform baseline methods with no observable artifacts and improved quantitative and qualitative metrics.
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.