  The explosive rise of the use of Computer tomography (CT) imaging in medical
practice has heightened public concern over the patient's associated radiation
dose. However, reducing the radiation dose leads to increased noise and
artifacts, which adversely degrades the scan's interpretability. Consequently,
an advanced image reconstruction algorithm to improve the diagnostic
performance of low dose ct arose as the primary concern among the researchers,
which is challenging due to the ill-posedness of the problem. In recent times,
the deep learning-based technique has emerged as a dominant method for low dose
CT(LDCT) denoising. However, some common bottleneck still exists, which hinders
deep learning-based techniques from furnishing the best performance. In this
study, we attempted to mitigate these problems with three novel accretions.
First, we propose a novel convolutional module as the first attempt to utilize
neighborhood similarity of CT images for denoising tasks. Our proposed module
assisted in boosting the denoising by a significant margin. Next, we moved
towards the problem of non-stationarity of CT noise and introduced a new noise
aware mean square error loss for LDCT denoising. Moreover, the loss mentioned
above also assisted to alleviate the laborious effort required while training
CT denoising network using image patches. Lastly, we propose a novel
discriminator function for CT denoising tasks. The conventional vanilla
discriminator tends to overlook the fine structural details and focus on the
global agreement. Our proposed discriminator leverage self-attention and
pixel-wise GANs for restoring the diagnostic quality of LDCT images. Our method
validated on a publicly available dataset of the 2016 NIH-AAPM-Mayo Clinic Low
Dose CT Grand Challenge performed remarkably better than the existing state of
the art method.
