A multi-channel cycleGAN for CBCT to CT synthesis
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Abstract (arXiv)
Image synthesis is used to generate synthetic CTs (sCTs) from on-treatment cone-beam CTs (CBCTs) with a view to improving image quality and enabling accurate dose computation to facilitate a CBCT-based adaptive radiotherapy workflow. As this area of research gains momentum, developments in sCT generation methods are difficult to compare due to the lack of large public datasets and sizeable variation in training procedures. To compare and assess the latest advancements in sCT generation, the SynthRAD2023 challenge provides a public dataset and evaluation framework for both MR and CBCT to sCT synthesis. Our contribution focuses on the second task, CBCT-to-sCT synthesis. By leveraging a multi-channel input to emphasize specific image features, our approach effectively addresses some of the challenges inherent in CBCT imaging, whilst restoring the contrast necessary for accurate visualisation of patients' anatomy. Additionally, we introduce an auxiliary fusion network to further enhance the fidelity of generated sCT images.
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References · 5
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
- 2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 271
- 2023SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapycited by 53
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