Masked Autoencoders for Low dose CT denoising

TL;DR

This paper introduces a masked autoencoder approach for low-dose CT denoising that leverages unlabeled data to improve image quality without relying heavily on paired ground truth data.

Contribution

It adapts the masked autoencoder framework to the CT denoising task, enabling effective self-pretraining and structural preservation in low-dose CT images.

Findings

MAE boosts transformer denoising performance

Reduces dependence on ground truth data

Enhances structural preservation in images

Abstract

Low-dose computed tomography (LDCT) reduces the X-ray radiation but compromises image quality with more noises and artifacts. A plethora of transformer models have been developed recently to improve LDCT image quality. However, the success of a transformer model relies on a large amount of paired noisy and clean data, which is often unavailable in clinical applications. In computer vision and natural language processing fields, masked autoencoders (MAE) have been proposed as an effective label-free self-pretraining method for transformers, due to its excellent feature representation ability. Here, we redesign the classical encoder-decoder learning model to match the denoising task and apply it to LDCT denoising problem. The MAE can leverage the unlabeled data and facilitate structural preservation for the LDCT denoising model when ground truth data are missing. Experiments on the Mayo dataset validate that the MAE can boost the transformer's denoising performance and relieve the dependence on the ground truth data.