Transformer CycleGAN with uncertainty estimation for CBCT based synthetic CT in adaptive radiotherapy

Objective. Clinical implementation of synthetic CT (sCT) from cone-beam CT (CBCT) for adaptive radiotherapy necessitates a high degree of anatomical integrity, Hounsfield unit (HU) accuracy, and image quality. To achieve these goals, a vision-transformer and anatomically sensitive loss functions are described. Better quantification of image quality is achieved using the alignment-invariant Fréchet inception distance (FID), and uncertainty estimation for sCT risk prediction is implemented in a scalable plug-and-play manner.Approach. Baseline U-Net, generative adversarial network (GAN), and CycleGAN models were trained to identify shortcomings in each approach. The proposed CycleGAN-Best model was empirically optimized based on a large ablation study and evaluated using classical image quality metrics, FID, gamma index, and a segmentation analysis. Two uncertainty estimation methods, Monte-Carlo Dropout (MCD) and test-time augmentation (TTA), were introduced to model epistemic and aleatoric uncertainty.Main results. FID was correlated to blind observer image quality scores with a Correlation Coefficient of -0.83, validating the metric as an accurate quantifier of perceived image quality. The FID and mean absolute error (MAE) of CycleGAN-Best was 42.11 ± 5.99 and 25.00 ± 1.97 HU, compared to 63.42 ± 15.45 and 31.80 HU for CycleGAN-Baseline, and 144.32 ± 20.91 and 68.00 ± 5.06 HU for the CBCT, respectively. Gamma 1%/1 mm pass rates were 98.66 ± 0.54% for CycleGAN-Best, compared to 86.72 ± 2.55% for the CBCT. TTA and MCD-based uncertainty maps were well spatially correlated with poor synthesis outputs.Significance. Anatomical accuracy was achieved by suppressing CycleGAN-related artefacts. FID better discriminated image quality, where alignment-based metrics such as MAE erroneously suggest poorer outputs perform better. Uncertainty estimation for sCT was shown to correlate with poor outputs and has clinical relevancy toward model risk assessment and quality assurance. The proposed model and accompanying evaluation and risk assessment tools are necessary additions to achieve clinically robust sCT generation models.