Cascaded Convolutional Neural Networks with Perceptual Loss for Low Dose CT Denoising

TL;DR

This paper introduces a cascaded neural network approach with perceptual loss for low dose CT denoising, effectively preserving fine details and improving over traditional MSE-based methods.

Contribution

It proposes a novel cascaded neural network architecture utilizing perceptual loss to enhance detail preservation in low dose CT denoising.

Findings

Outperforms existing methods in detail preservation

More effective reconstruction of low contrast regions

Reduces over-smoothing compared to MSE-based approaches

Abstract

Low Dose CT Denoising research aims to reduce the risks of radiation exposure to patients. Recently researchers have used deep learning to denoise low dose CT images with promising results. However, approaches that use mean-squared-error (MSE) tend to over smooth the image resulting in loss of fine structural details in low contrast regions of the image. These regions are often crucial for diagnosis and must be preserved in order for Low dose CT to be used effectively in practice. In this work we use a cascade of two neural networks, the first of which aims to reconstruct normal dose CT from low dose CT by minimizing perceptual loss, and the second which predicts the difference between the ground truth and prediction from the perceptual loss network. We show that our method outperforms related works and more effectively reconstructs fine structural details in low contrast regions of the image.