Exploring constraints on CycleGAN-based CBCT enhancement for adaptive radiotherapy

TL;DR

This paper investigates how imposing additional constraints, including structure retaining and frequency domain losses, on CycleGAN improves the quality of synthetic CBCT images for radiotherapy, reducing artifacts and enhancing clinical applicability.

Contribution

It introduces adaptive control of constraints and a frequency loss to improve CycleGAN-based CBCT synthesis, addressing artifact issues and ensuring clinical quality.

Findings

Synthetic images outperform baseline CycleGAN in quality.

No observable artifacts or quality loss in generated images.

Enhanced images are suitable for clinical workflows and segmentation tasks.

Abstract

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.