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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. [ABSTRACT FROM AUTHOR]

Reducing radiation dose in cardiac catheter-based X-ray procedures increases safety but also image noise and artifacts. Excessive noise and artifacts can compromise vital image information, which can affect clinical decision-making. Developing more effective X-ray denoising methodologies will be beneficial to both patients and healthcare professionals by allowing imaging at lower radiation dose without compromising image information. This paper proposes a framework based on a convolutional neural network (CNN), namely Ultra-Dense Denoising Network (UDDN), for low-dose X-ray image denoising. To promote feature extraction, we designed a novel residual block which establishes a solid correlation among multiple-path neural units via abundant cross connections in its representation enhancement section. Experiments on synthetic additive noise X-ray data show that the UDDN achieves statistically significant higher peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) than other comparative methods. We enhanced the clinical adaptability of our framework by training using normally-distributed noise and tested on clinical data taken from procedures at St. Thomas’ hospital in London. The performance was assessed by using local SNR and by clinical voting using ten cardiologists. The results show that the UDDN outperforms the other comparative methods and is a promising solution to this challenging but clinically impactful task. [ABSTRACT FROM AUTHOR]