King's College London

Research portal

Ultra-DenseNet for Low-Dose X-Ray Image Denoising in Cardiac Catheter-Based Procedures

Research output: Chapter in Book/Report/Conference proceedingConference paper

Yimin Luo, Daniel Toth, Kui Jiang, Kuberan Pushparajah, Kawal Rhode

Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers
EditorsMihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair Young, Tommaso Mansi, Avan Suinesiaputra
PublisherSPRINGER
Pages31-42
Number of pages12
ISBN (Print)9783030390730
DOIs
Publication statusPublished - 1 Jan 2020
Event10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 13 Oct 201913 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12009 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period13/10/201913/10/2019

King's Authors

Abstract

The continuous development and prolonged use of X-ray fluoroscopic imaging in cardiac catheter-based procedures is associated with increasing radiation dose to both patients and clinicians. Reducing the radiation dose leads to increased image noise and artifacts, which may reduce discernable image information. Therefore, advanced denoising methods for low-dose X-ray images are needed to improve safety and reliability. Previous X-ray imaging denoising methods mainly rely on domain filtration and iterative reconstruction algorithms and some remaining artifacts still appear in the denoised X-ray images. Inspired by recent achievements of convolutional neural networks (CNNs) on feature representation in the medical image analysis field, this paper introduces an ultra-dense denoising network (UDDN) within the CNN framework for X-ray image denoising in cardiac catheter-based procedures. After patch-based iterative training, the proposed UDDN achieves a competitive performance in both simulated and clinical cases by achieving higher peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) when compared to previous CNN architectures.

View graph of relations

© 2018 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454