  Low-dose computed tomography (CT) denoising algorithms aim to enable reduced
patient dose in routine CT acquisitions while maintaining high image quality.
Recently, deep learning~(DL)-based methods were introduced, outperforming
conventional denoising algorithms on this task due to their high model
capacity. However, for the transition of DL-based denoising to clinical
practice, these data-driven approaches must generalize robustly beyond the seen
training data. We, therefore, propose a hybrid denoising approach consisting of
a set of trainable joint bilateral filters (JBFs) combined with a convolutional
DL-based denoising network to predict the guidance image. Our proposed
denoising pipeline combines the high model capacity enabled by DL-based feature
extraction with the reliability of the conventional JBF. The pipeline's ability
to generalize is demonstrated by training on abdomen CT scans without metal
implants and testing on abdomen scans with metal implants as well as on head CT
data. When embedding two well-established DL-based denoisers (RED-CNN/QAE) in
our pipeline, the denoising performance is improved by $10\,\%$/$82\,\%$ (RMSE)
and $3\,\%$/$81\,\%$ (PSNR) in regions containing metal and by $6\,\%$/$78\,\%$
(RMSE) and $2\,\%$/$4\,\%$ (PSNR) on head CT data, compared to the respective
vanilla model. Concluding, the proposed trainable JBFs limit the error bound of
deep neural networks to facilitate the applicability of DL-based denoisers in
low-dose CT pipelines.
