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.
翻译:低剂量计算透析(CT)解密算法旨在降低常规CT采购中的病人剂量,同时保持高图像质量。最近,引入了基于深度学习~(DL)的基于管道的方法,由于模型容量高,在这项任务上优于常规的取消算法。然而,为将基于DL的分解方法转换到临床实践,这些数据驱动的方法必须超越所见的培训数据,大力推广。因此,我们提出了一种混合的解密方法,其中包括一套可训练的双边联合过滤器(JBFs),结合一个基于同步的DL值的分解网络,以预测指导图像。我们提议的分解管道将基于DL的特性提取带来的高模型能力与常规JBFF的可靠性结合起来。 输油管的普及能力表现表现表现表现表现为:不安装金属的 abdomen扫描,用金属下方的DMS(RED-N_N__________BAR__BAR_BAR_BAR_BAR__BAR_BAR_BAR_BAR_BAR_BAR_BAR_BAR_BAR_BAR_BAR_BAR_B_$)以及使用基于金属的基于金属的bbM_BAR_BAR_BAR_$的扫描(RE_N__N_____________________________________________________________________________________________________________________________________________________________________________________________________________________