  Deep neural networks have shown great success in low dose CT denoising.
However, most of these deep neural networks have several hundred thousand
trainable parameters. This, combined with the inherent non-linearity of the
neural network, makes the deep neural network diffcult to understand with low
accountability. In this study we introduce JBFnet, a neural network for low
dose CT denoising. The architecture of JBFnet implements iterative bilateral
filtering. The filter functions of the Joint Bilateral Filter (JBF) are learned
via shallow convolutional networks. The guidance image is estimated by a deep
neural network. JBFnet is split into four filtering blocks, each of which
performs Joint Bilateral Filtering. Each JBF block consists of 112 trainable
parameters, making the noise removal process comprehendable. The Noise Map (NM)
is added after filtering to preserve high level features. We train JBFnet with
the data from the body scans of 10 patients, and test it on the AAPM low dose
CT Grand Challenge dataset. We compare JBFnet with state-of-the-art deep
learning networks. JBFnet outperforms CPCE3D, GAN and deep GFnet on the test
dataset in terms of noise removal while preserving structures. We conduct
several ablation studies to test the performance of our network architecture
and training method. Our current setup achieves the best performance, while
still maintaining behavioural accountability.
