Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs

TL;DR

This paper introduces a self-supervised CT denoising method that generates pseudo-CT pairs using trained noise models, significantly enhancing denoising performance with limited training data.

Contribution

It proposes a novel approach to generate realistic pseudo-CT pairs using ensemble noise models, improving denoising accuracy in low-dose CT images.

Findings

The ensemble noise model produces realistic CT noise.

The method outperforms existing supervised and self-supervised denoising models.

Significant improvement in denoising performance on the AAPM dataset.

Abstract

Recently, Self-supervised learning methods able to perform image denoising without ground truth labels have been proposed. These methods create low-quality images by adding random or Gaussian noise to images and then train a model for denoising. Ideally, it would be beneficial if one can generate high-quality CT images with only a few training samples via self-supervision. However, the performance of CT denoising is generally limited due to the complexity of CT noise. To address this problem, we propose a novel self-supervised learning-based CT denoising method. In particular, we train pre-train CT denoising and noise models that can predict CT noise from Low-dose CT (LDCT) using available LDCT and Normal-dose CT (NDCT) pairs. For a given test LDCT, we generate Pseudo-LDCT and NDCT pairs using the pre-trained denoising and noise models and then update the parameters of the denoising model using these pairs to remove noise in the test LDCT. To make realistic Pseudo LDCT, we train multiple noise models from individual images and generate the noise using the ensemble of noise models. We evaluate our method on the 2016 AAPM Low-Dose CT Grand Challenge dataset. The proposed ensemble noise model can generate realistic CT noise, and thus our method significantly improves the denoising performance existing denoising models trained by supervised- and self-supervised learning.