Low dose CT image enhancement based on dense transformer network

Zuyang Song; Jin Zhu

doi:10.1117/12.3028994

22 May 2024 Low dose CT image enhancement based on dense transformer network

Zuyang Song, Jin Zhu

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131760Q (2024) https://doi.org/10.1117/12.3028994
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Over the past year, the popular deep learning-based low-dose CT image enhancement methods mostly use the encoder-decoder structure. This structure expands the receptive field of the network by successive downsampling for deeper features. However, continuous downsampling leads to the loss of spatial information in the image, the excessively long distance between downsampling and upsampling also causes reconstruction errors. To solve the problems, this paper proposes a dense transformer network for low-dose CT image enhancement. This network uses dense connection as the basic architecture, and uses Up/Down-Tokenization in the Transformer module instead of regular up/down-sampling operations. In addition, by adding Tokons2Tokon modules between two consecutive multi-headed self-attention modules, improve the global modeling capability of the network and reduce computation. A channel fusion module is designed to replace the MLP in the standard transformer to introducing local information for reconstruction. Experiments on the AAPM LDCT dataset show that the proposed network can retain more texture details while suppressing noise compared to existing networks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zuyang Song and Jin Zhu "Low dose CT image enhancement based on dense transformer network", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131760Q (22 May 2024); https://doi.org/10.1117/12.3028994

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available