Research Progress of Deep Learning in Low-Dose Ct Image Denoising

Zhang, Fan; Liu, Jingyu; Liu, Ying; Zhang, Xinhong

doi:10.1093/rpd/ncac284

Cited by 10 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Medical images [42] usually have complex structures and are highly noisy, e.g., X-rays [43,44], CT scans [45], MRIs [46], etc., which must be of high quality in diagnosis and analysis. The main application directions of graph neural networks in medical image denoising [47,48] include image noise reduction, image enhancement, motion artifact removal, data recovery, super-resolution reconstruction, and sequence image denoising. These applications will be specifically described below.…”

Section: Application Of Graph Neural Network In Medical Image Denoisingmentioning

confidence: 99%

Review of Graph Neural Networks for Medical Image

Wang

2023

EAI Endorsed Trans e-Learn

View full text Add to dashboard Cite

As deep learning continues to evolve, more and more applications are generating data from non-Euclidean domains and representing them as graphs with complex relationships and interdependencies between objects. This poses a significant challenge to deep learning algorithms. Because, due to the uniqueness of graphs, applying deep learning to ubiquitous graph data is not an easy task. To solve the problem in non-Euclidean domains, graph Neural Networks (GNNs) have emerged. A graph neural network (GNN) is a neural model that captures dependencies between graphs by passing messages between graph nodes. With the continuous development of medical image technology, medical image diagnosis plays a crucial role in clinical practice. However, in practice, medical images are often affected by noise, artifacts, and other interfering factors, which may lead to inaccurate and unstable diagnostic results. Therefore, image-denoising techniques become especially critical in medical image processing. Therefore, researchers have proposed innovative methods based on graph neural networks for effective noise removal, preserving the key features of the image and improving the quality and usability of medical images. This paper reviews the research progress of graph neural networks in the field of medical image denoising. It also summarises the problems and challenges of current research and looks at the future direction of medical image-denoising research.

show abstract

Section: Application Of Graph Neural Network In Medical Image Denoisingmentioning

confidence: 99%

Review of Graph Neural Networks for Medical Image

Wang

2023

EAI Endorsed Trans e-Learn

View full text Add to dashboard Cite

show abstract

“…In the training stage, we applied the minibatch sampling method on 128 image patches to train the AI model. The learning rate is set to 10 -4 and two exponential decay parameters used to control the momentum are 0.9 and 0.999 [23].…”

Section: Transfer Learning (Tl) Encoder-decoder Network With Symmetri...mentioning

confidence: 99%

Virtual and real-world implementation of deep-learning-based image denoising model on projection domain in digital tomosynthesis and cone-beam computed tomography data

Jin

Chang²,

Wang³

et al. 2022

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

Reducing the radiation dose will cause severe image noise and artifacts, and degradation of image quality will also affect the accuracy of diagnosis. To find a solution, we comprise a 2D and 3D concatenating convolutional encoder-decoder (CCE-3D) and the structural sensitive loss (SSL), via transfer learning (TL) for denoising for low-dose computed tomography (LDCT), radiography, and tomosynthesis. The simulation and real-world practicing results show that many of the figures-of-merit (FOMs) increase in both projections (2-3 times) and CT imaging (1.5-2 times). From the PSNR and structural similarity index of measurement (SSIM), the CCE-3D model is effective in denoising but keeps the shape of the structure. Hence, we have developed a denoising model that can be served as a promising tool to be implemented in the next generation X-ray radiography, tomosynthesis, and LDCT systems.

show abstract

“…2 Common to all of these in vivo multi-channel imaging applications is the need to manage ionizing radiation dose applied to the subject. In the past, iterative reconstruction techniques, including regularization based on prior assumptions, were the preeminent means of reducing dose and associated image noise in CT. More recently, however, there has been an explosion of interest in supervised deep learning (DL) methods for CT image noise removal and dose management [3][4][5] and for the augmentation of iterative reconstruction. [6][7][8] Supervised DL methods offer several key advantages over classic denoising methods and iterative reconstruction techniques.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised learning of robust models for cardiac and photon-counting x-ray CT denoising

Clark

Schwartz

Euler

et al. 2023

Medical Imaging 2023: Physics of Medical Imaging

View full text Add to dashboard Cite

Supervised deep learning methods have rapidly advanced to the forefront of medical imaging research; however, they face limitations in advanced CT applications. For instance, label generation is difficult for retrospective cardiac CT data where dose modulation yields variable image quality by phase. Similarly, a model trained to denoise multi-energy data may not generalize across differing contrast and energy channel counts. In this work, we propose and demonstrate several innovations for unsupervised denoising of such multi-channel CT data sets with a focus on improving model robustness to varying noise levels, image contrast, and channel counts. Specifically, we update our past Constrained Bregman Framework for unsupervised denoising to simplify model training procedures, to automate regularization hyperparameter selection, and to spatially adapt denoising performance. Furthermore, we propose a new data normalization strategy involving weighted singular value decomposition, convolutional mean subtraction, and noise variance scaling which abstracts the image denoising problem from the contrast and channel count of the input data. We combine these improvements with a hybrid Inception Net, half U-Net network structure, ideally suited for 3D data processing, and a plug-and- play scheme for incorporating traditional regularizers into deep learning cost functions. We demonstrate the value of these improvements for network training and denoising using retrospectively gated cardiac photon-counting CT data acquired with a Siemens NAEOTOM Alpha scanner (2-3x noise reduction). This clinical model robustly generalizes to 40-channel preclinical cardiac, photon-counting CT data acquired in a mouse without the need for additional training (6- 10x noise reduction, negligible intensity bias).

show abstract

Research Progress of Deep Learning in Low-Dose Ct Image Denoising

Cited by 10 publications

References 35 publications

Review of Graph Neural Networks for Medical Image

Review of Graph Neural Networks for Medical Image

Virtual and real-world implementation of deep-learning-based image denoising model on projection domain in digital tomosynthesis and cone-beam computed tomography data

Unsupervised learning of robust models for cardiac and photon-counting x-ray CT denoising

Contact Info

Product

Resources

About