Edge-Enhancement DenseNet for X-ray Fluoroscopy Image Denoising in Cardiac Electrophysiology Procedures

Yimin Luo, YingLiang Ma, Hugh O’ Brien, Kui Jiang, Vikram Kohli, Sesilia Maidelin, Mahrukh Saeed, Emily Deng, Kuberan Pushparajah, Kawal S Rhode

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
102 Downloads (Pure)


Purpose: Reducing X-ray dose increases safety in cardiac electrophysiology procedures but also increases image noise and artifacts which may affect the discernibility of devices and anatomical cues. Previous denoising methods based on convolutional neural networks (CNNs) have shown improvements in the quality of low-dose X-ray fluoroscopy images but may compromise clinically important details required by cardiologists. Methods: In order to obtain denoised X-ray fluoroscopy images whilst preserving details, we propose a novel deep-learning-based denoising framework, namely edge-enhancement densenet (EEDN), in which an attention-awareness edge-enhancement module is designed to increase edge sharpness. In this framework, a CNN-based denoiser is first used to generate an initial denoising result. Contours representing edge information are then extracted using an attention block and a group of interacted ultra-dense blocks for edge feature representation. Finally, the initial denoising result and enhanced edges are combined to generate the final X-ray image. The proposed denoising framework was tested on a total of 3262 clinical images taken from 100 low-dose X-ray sequences acquired from 20 patients. The performance was assessed by pairwise voting from five cardiologists as well as quantitative indicators. Furthermore, we evaluated our technique's effect on catheter detection using 416 images containing coronary sinus catheters in order to examine its influence as a pre-processing tool. Results: The average signal-to-noise ratio of X-ray images denoised with EEDN was 24.5, which was 2.2 times higher than that of the original images. The accuracy of catheter detection from EEDN denoised sequences showed no significant difference compared with their original counterparts. Moreover, EEDN received the highest average votes in our clinician assessment when compared to our existing technique and the original images. Conclusion: The proposed deep learning-based framework shows promising capability for denoising interventional X-ray fluoroscopy images. The results from the catheter detection show that the network does not affect the results of such an algorithm when used as a pre-processing step. The extensive qualitative and quantitative evaluations suggest that the network may be of benefit to reduce radiation dose when applied in real time in the catheter laboratory.

Original languageEnglish
Pages (from-to)1262-1275
Number of pages14
JournalMedical Physics
Issue number2
Early online date18 Jan 2022
Publication statusPublished - Feb 2022

Bibliographical note

Medical Physics© 2022 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.


  • Cardiac electrophysiology procedures
  • Convolutional neural network
  • Denoising
  • Edge enhancement
  • X-ray fluoroscopy

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging


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