Cascaded Perceptual Networks for Low Dose CT Denoising

Radiology is a critical tool for physicians when treating many health complications. X-ray computed tomography (X-ray CT) is particularly useful in highlighting lesions and damaged tissue in the body. The adoption of X-ray tomography is seeing significant growth as the technology improves and radiologists are finding it increasingly useful. A significant drawback of X-ray CT is the associated radiation exposure which is harmful to the body. If the radiation dose is decreased in the acquisition protocol, the images become degraded and no longer useful for radiologists. Current methods for reducing radiation risk include guidelines on acceptable exposure, and very recently reconstruction techniques which aim to denoise images acquired at low radiation dose. However, image post-processing techniques are preferred because they are scanner independent, and don’t require the denoising algorithm to have access to the raw scanner data which is often not made available by manufacturers. With advances in deep-learning via convolutional neural networks, several methods have been proposed to denoise low dose CT images in order to predict the normal dose image. In this work, existing methods are improved on with the addition of our contributions. We propose a training regime using a cascade of neural networks the first of which uses a perceptual loss, and the second which performs a residue prediction using mean-squared-error. Secondly, we propose a new loss function which incorporates perceptual loss, structural dissimilarity and mean-squared-error. We show that both proposed methods result in significant improvement when compared to the related works. As a secondary contribution, this work recommends several design considerations for building deep learning networks for image denoising. We show results from extensive empirical study to support our recommendations.