Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT

• URL: http://arxiv.org/abs/2207.07368v1
• Date: Fri, 15 Jul 2022 09:30:32 GMT
• Title: Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT
• Authors: Fabian Wagner and Mareike Thies and Felix Denzinger and Mingxuan Gu and Mayank Patwari and Stefan Ploner and Noah Maul and Laura Pfaff and Yixing Huang and Andreas Maier
• Abstract summary: Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions. Deep learning(DL)-based methods were introduced, outperforming conventional denoising algorithms on this task due to their high model capacity. We propose a hybrid denoising approach consisting of a set of trainable joint bilateral filters (JBFs) combined with a convolutional DL-based denoising network.
• Score: 7.879949714759592
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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