Combined spatial and temporal deep learning for image noise reduction of fluoroscopic x-ray sequences

Chengyang Wu; Pu Zhang; Yan Xu; Jingwu Yao

doi:10.1117/12.2549693

16 March 2020 Combined spatial and temporal deep learning for image noise reduction of fluoroscopic x-ray sequences

Chengyang Wu, Pu Zhang, Yan Xu, Jingwu Yao

Proceedings Volume 11312, Medical Imaging 2020: Physics of Medical Imaging; 1131240 (2020) https://doi.org/10.1117/12.2549693
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Radiation dose is of an important consideration for x-ray fluoroscopy imaging of interventional C-arm systems. Low-dose imaging is always expected, but it also results in noisy images. Noise reduction is one of the important topics for fluoroscopic images. Recently, the advances in deep learning have achieved outstanding denoising results for x-ray images. However, most existing methods in the field focus only on 2D image denoising from frame-by-frame independently, and removing temporal noise in image sequence remains a challenging problem. Our goal is simultaneously to reduce both spatial and temporal noises for fluoroscopic image sequences in a unified framework. In this paper, we propose a deep learning algorithm that extensively utilizes temporal information to maximize the efficiency of noise reduction. The proposed convolutional neural network (CNN) is based on DenseNet¹ and DnCNN²but with improved multi-channel input layers for image sequence. That network architecture not only enables spatial domain deep learning from the input of every individual frame, but also is able to make full use of temporally correlative information among adjacent frames for temporal domain learning. In order to further suppress temporal noise resulting in visual flickers of image sequence, an additional term is introduced to the network loss function. Besides two conventional terms of L2 and perceptual losses, the new proposed loss calculates the statistical variance of the network performance caused by random influence of temporal imaging. The developed algorithm is evaluated with fluoroscopic phantom images and clinical patient data, showing superior performance for spatio-temporal denoising.

Citation Download Citation

Chengyang Wu, Pu Zhang, Yan Xu, and Jingwu Yao "Combined spatial and temporal deep learning for image noise reduction of fluoroscopic x-ray sequences", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131240 (16 March 2020); https://doi.org/10.1117/12.2549693

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Denoising

Image processing

X-ray imaging

Convolution

Imaging systems

Image denoising

Visualization

Show All Keywords

Keywords/Phrases

Search In:

Publication Years