Learning-Based X-Ray Image Denoising Utilizing Model-Based Image Simulations

Hariharan, S; Kaethner, Christian; Strobel, Norbert; Kowarschik, Markus; Albarqouni, Shadi; Fahrig, Rebecca; Navab, Nassir

doi:10.1007/978-3-030-32226-7_61

Cited by 9 publications

(22 citation statements)

References 12 publications

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“…According to previous CNN‐based image denoising methods, 9 , 10 , 11 , 12 , 13 , 14 , 20 model optimization depends on iteratively minimizing the distance between the output image and the ground truth based on the feature level. As illustrated in Figure 1 , I output is our final denoising result and I ’ represents the corresponding ground truth.…”

Section: Methodsmentioning

confidence: 99%

“… 9 , 10 , 11 , 12 For example, the Denoising Convolution Neural Network (DnCNN) proposed in reference 10 is a benchmark for denoising of photographic and videographic images. Recently, CNN‐based frameworks 13 , 14 have been proposed for X‐ray fluoroscopy denoising. For example, the authors in reference 13 proposed a simple CNN‐based framework to simulate the nonlinear mapping between low‐dose and higher‐SNR X‐ray fluoroscopy image patches.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the authors in reference 13 proposed a simple CNN‐based framework to simulate the nonlinear mapping between low‐dose and higher‐SNR X‐ray fluoroscopy image patches. The authors in reference 14 compared different existing CNN‐based frameworks on clinical and phantom data. Although, these methods lose some detail, quantitative and qualitative analyses have demonstrated that deep learning‐based approaches outperform well‐established conventional X‐ray image denoising methods.…”

Section: Introductionmentioning

confidence: 99%

“…Besides visual perception, peak signal‐to‐noise ratio (PSNR) and structural similarity index measure (SSIM) are usually used as quantitative evaluation metrics to assess the model performance on image denoising tasks. 9 , 10 , 11 , 12 , 13 , 14 , 20 However, both of these need reference / ground truth / clean images to validate the effectiveness of the obtained model. In real interventional procedures, we have only X‐ray fluoroscopy images with no corresponding ground truth.…”

Section: Introductionmentioning

confidence: 99%

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Edge‐enhancement densenet for X‐ray fluoroscopy image denoising in cardiac electrophysiology procedures

Luo

Brien

et al. 2022

Medical Physics

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Edge‐enhancement densenet for X‐ray fluoroscopy image denoising in cardiac electrophysiology procedures

Luo

Brien

et al. 2022

Medical Physics

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“…With the availability of training data, either via dedicated data collection or synthetic generation, AI algorithms can be developed to analyze intra-operative images in near realtime and supply contextual information to improve decision making. Omitting applications to endoscopic video sources which are discussed in depth in Section V, and focusing first on interventional X-ray imaging, benefits of real-time machine learning range from segmentation of tools [53], [81], [82], anatomical landmark detection [51], [52], anatomy localization [83], and denoising [84], [85], to surgical phase recognition [81]. Corresponding developments can be found for ultrasound imaging [86]- [88].…”

Section: Towards Prospectively Planned Intelligent Imagingmentioning

confidence: 99%

CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions

et al. 2020

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Data-driven computational approaches have evolved to enable extraction of information from medical images with a reliability, accuracy and speed which is already transforming their interpretation and exploitation in clinical practice. While similar benefits are longed for in the field of interventional imaging, this ambition is challenged by a much higher heterogeneity. Clinical workflows within interventional suites and operating theatres are extremely complex and typically rely on poorly integrated intra-operative devices, sensors, and support infrastructures. Taking stock of some of the most exciting developments in machine learning and artificial intelligence for computer assisted interventions, we highlight the crucial need to take context and human factors into account in order to address these challenges. Contextual artificial intelligence for computer assisted intervention, or CAI4CAI, arises as an emerging opportunity feeding into the broader field of surgical data science. Central challenges being addressed in CAI4CAI include how to integrate the ensemble of prior knowledge and instantaneous sensory information from experts, sensors and actuators; how to create and communicate a faithful and actionable shared representation of the surgery among a mixed human-AI actor team; how to design interventional systems and associated cognitive shared control schemes for online uncertainty-aware collaborative decision making ultimately producing more precise and reliable interventions.Index Terms-Artificial intelligence, computer assisted interventions, interventional workflow, intra-operative imaging, surgical planning, data fusion, surgical scene understanding, contextaware user interface, machine and deep learning, surgical data science

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STN: Stochastic Triplet Neighboring Approach to Self-supervised Denoising from Limited Noisy Images

Wan¹,

Shi²,

Liu³

2023

MultiMedia Modeling

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Learning-Based X-Ray Image Denoising Utilizing Model-Based Image Simulations

Cited by 9 publications

References 12 publications

Edge‐enhancement densenet for X‐ray fluoroscopy image denoising in cardiac electrophysiology procedures

Edge‐enhancement densenet for X‐ray fluoroscopy image denoising in cardiac electrophysiology procedures

CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions

STN: Stochastic Triplet Neighboring Approach to Self-supervised Denoising from Limited Noisy Images

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