  The use of deep learning has successfully solved several problems in the
field of medical imaging. Deep learning has been applied to the CT denoising
problem successfully. However, the use of deep learning requires large amounts
of data to train deep convolutional networks (CNNs). Moreover, due to large
parameter count, such deep CNNs may cause unexpected results. In this study, we
introduce a novel CT denoising framework, which has interpretable behaviour,
and provides useful results with limited data. We employ bilateral filtering in
both the projection and volume domains to remove noise. To account for
non-stationary noise, we tune the $\sigma$ parameters of the volume for every
projection view, and for every volume pixel. The tuning is carried out by two
deep CNNs. Due to impracticality of labelling, the two deep CNNs are trained
via a Deep-Q reinforcement learning task. The reward for the task is generated
by using a custom reward function represented by a neural network. Our
experiments were carried out on abdominal scans for the Mayo Clinic TCIA
dataset, and the AAPM Low Dose CT Grand Challenge. Our denoising framework has
excellent denoising performance increasing the PSNR from 28.53 to 28.93, and
increasing the SSIM from 0.8952 to 0.9204. We outperform several
state-of-the-art deep CNNs, which have several orders of magnitude higher
number of parameters (p-value (PSNR) = 0.000, p-value (SSIM) = 0.000). Our
method does not introduce any blurring, which is introduced by MSE loss based
methods, or any deep learning artifacts, which are introduced by WGAN based
models. Our ablation studies show that parameter tuning and using our reward
network results in the best possible results.
