MVCT image enhancement using reference-based encoder–decoder convolutional neural network

Daily MVCT (Megavoltage Computed Tomography) in TomoTherapy plays a crucial role in patients’ setup and dose reconstruction. However, MVCT images suffer from high noise and low tissue contrast due to the limited number of X-ray photons and low detector quantum efficiency (DQE). In this study, we pro...

Full description

Saved in:
Bibliographic Details
Published in:Expert systems with applications Vol. 241; p. 122576
Main Authors: Jin, Shuang, Xu, Xiaotong, Su, Zhe, Tang, Long, Zheng, Mengxun, Liang, Peiwen, Zhang, Hua
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2024
Subjects:
Convolutional neural networks
Denoising
Megavoltage computed tomography
Reference image guide
Reference image guide
Convolutional neural networks
Megavoltage computed tomography
Denoising
ISSN:0957-4174, 1873-6793
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Daily MVCT (Megavoltage Computed Tomography) in TomoTherapy plays a crucial role in patients’ setup and dose reconstruction. However, MVCT images suffer from high noise and low tissue contrast due to the limited number of X-ray photons and low detector quantum efficiency (DQE). In this study, we propose an approach to enhance MVCT images using the KVCT (Kilovoltage Computed Tomography) image of the same patient as an auxiliary reference image. Specifically, we propose a reference-based encoder–decoder convolutional neural network (RefED-CNN) by incorporating a feature extraction and alignment (FEA) module to introduce the features of the reference image as side information into the MVCT image enhancement process. The FEA module automatically searches and aligns relevant features between the reference image and the noisy image, and transfers the relevant texture from reference KVCT images to the target MVCT image. Evaluations conducted on both phantom and real patient data show that our method outperforms other denoising methods by effectively reducing noise and preserving intricate structural details. •Propose a novel MVCT image enhancement method based on a reference image-guided CNN.•Introduce KVCT as reference images into MVCT denoising process at the feature level.•Propose an FEA module to transfer similar textures from KVCT image to MVCT image.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.122576