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Luo, Yimin, Ma, YingLiang 
ORCID: https://orcid.org/0000-0001-5770-5843, O'Brien, Hugh, Jiang, Kui, Kohli, Vikram, Maidelin, Sesilia, Deng, Emily, Pushparajah, Kuberan and Rhode, Kawal S.
(2022)
Edge-enhancement densenet for X-ray fluoroscopy image denoising in cardiac electrophysiology procedures.
Medical Physics, 49 (2).
pp. 1262-1275.
ISSN 0094-2405
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Abstract
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.
| Item Type: | Article |
|---|---|
| Additional Information: | Research Funding: King's College London-China Scholarship Scheme; National Institute for Health Research Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust and King's College London; Wellcome/EPSRC Centre for Medical Engineering. Grant Number: [WT 203148/Z/16/Z] |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 05 Jan 2023 12:30 |
| Last Modified: | 19 May 2023 09:39 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/90431 |
| DOI: | 10.1002/mp.15426 |
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