StruNet: Perceptual and low-rank regularized transformer for medical image denoising

A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective

Low-dose computed tomography (LDCT) scans are essential in reducing radiation exposure but often suffer from significant image noise that can impair diagnostic accuracy. While deep learning approaches have enhanced LDCT denoising capabilities, the predominant reliance on objective metrics like PSNR and SSIM has resulted in over-smoothed images that lack critical detail. This paper explores advanced deep learning methods tailored specifically to improve perceptual quality in LDCT images, focusing on generating diagnostic-quality images preferred in clinical practice. We review and compare current methodologies, including perceptual loss functions and generative adversarial networks, addressing the significant limitations of current benchmarks and the subjective nature of perceptual quality evaluation. Through a systematic analysis, this study underscores the urgent need for developing methods that balance both perceptual and diagnostic quality, proposing new directions for future research in the field.

Noise-assisted hybrid attention networks for low-dose PET and CT denoising

BACKGROUND

Positron emission tomography (PET) and computed tomography (CT) play a vital role in tumor-related medical diagnosis, assessment, and treatment planning. However, full-dose PET and CT pose the risk of excessive radiation exposure to patients, whereas low-dose images compromise image quality, impacting subsequent tumor recognition and disease diagnosis.

PURPOSE

To solve such problems, we propose a Noise-Assisted Hybrid Attention Network (NAHANet) to reconstruct full-dose PET and CT images from low-dose PET (LDPET) and CT (LDCT) images to reduce patient radiation risks while ensuring the performance of subsequent tumor recognition.

METHODS

NAHANet contains two branches: the noise feature prediction branch (NFPB) and the cascaded reconstruction branch. Among them, NFPB providing noise features for the cascade reconstruction branch. The cascaded reconstruction branch comprises a shallow feature extraction module and a reconstruction module which contains a series of cascaded noise feature fusion blocks (NFFBs). Among these, the NFFB fuses the features extracted from low-dose images with the noise features obtained by NFPB to improve the feature extraction capability. To validate the effectiveness of the NAHANet method, we performed experiments using two public available datasets: the Ultra-low Dose PET Imaging Challenge dataset and Low Dose CT Grand Challenge dataset.

RESULTS

As a result, the proposed NAHANet achieved higher performance on common indicators. For example, on the CT dataset, the PSNR and SSIM indicators were improved by 4.1 dB and 0.06 respectively, and the rMSE indicator was reduced by 5.46 compared with the LDCT; on the PET dataset, the PSNR and SSIM was improved by 3.37 dB and 0.02, and the rMSE was reduced by 9.04 compared with the LDPET.

CONCLUSIONS

This paper proposes a transformer-based denoising algorithm, which utilizes hybrid attention to extract high-level features of low dose images and fuses noise features to optimize the denoising performance of the network, achieving good performance improvements on low-dose CT and PET datasets.

A new visual State Space Model for low-dose CT denoising

BACKGROUND

Low-dose computed tomography (LDCT) can mitigate potential health risks to the public. However, the severe noise and artifacts in LDCT images can impede subsequent clinical diagnosis and analysis. Convolutional neural networks (CNNs) and Transformers stand out as the two most popular backbones in LDCT denoising. Nonetheless, CNNs suffer from a lack of long-range modeling capabilities, while Transformers are hindered by high computational complexity.

PURPOSE

In this study, our main goal is to develop a simple and efficient model that can both focus on local spatial context and model long-range dependencies with linear computational complexity for LDCT denoising.

METHODS

In this study, we make the first attempt to apply the State Space Model to LDCT denoising and propose a novel LDCT denoising model named Visual Mamba Encoder-Decoder Network (ViMEDnet). To efficiently and effectively capture both the local and global features, we propose the Mixed State Space Module (MSSM), where the depth-wise convolution, max-pooling, and 2D Selective Scan Module (2DSSM) are coupled together through a partial channel splitting mechanism. 2DSSM is capable of capturing global information with linear computational complexity, while convolution and max-pooling can effectively learn local signals to facilitate detail restoration. Furthermore, the network uses a weighted gradient-sensitive hybrid loss function to facilitate the preservation of image details, improving the overall denoising performance.

RESULTS

The performance of our proposed ViMEDnet is compared to five state-of-the-art LDCT denoising methods, including an iterative algorithm, two CNN-based methods, and two Transformer-based methods. The comparative experimental results demonstrate that the proposed ViMEDnet can achieve better visual quality and quantitative assessment outcomes. In visual evaluation, ViMEDnet effectively removes noise and artifacts, while exhibiting superior performance in restoring fine structures and low-contrast structural edges, resulting in minimal deviation of denoised images from NDCT. In quantitative assessment, ViMEDnet obtains the lowest RMSE and the highest PSNR, SSIM, and FSIM scores, further substantiating the superiority of ViMEDnet.

CONCLUSIONS

The proposed ViMEDnet possesses excellent LDCT denoising performance and provides a new alternative to LDCT denoising models beyond the existing CNN and Transformer options.

Recent developments in denoising medical images using deep learning: An overview of models, techniques, and challenges

Medical imaging plays a critical role in diagnosing and treating various medical conditions. However, interpreting medical images can be challenging even for expert clinicians, as they are often degraded by noise and artifacts that can hinder the accurate identification and analysis of diseases, leading to severe consequences such as patient misdiagnosis or mortality. Various types of noise, including Gaussian, Rician, and Salt-pepper noise, can corrupt the area of interest, limiting the precision and accuracy of algorithms. Denoising algorithms have shown the potential in improving the quality of medical images by removing noise and other artifacts that obscure essential information. Deep learning has emerged as a powerful tool for image analysis and has demonstrated promising results in denoising different medical images such as MRIs, CT scans, PET scans, etc. This review paper provides a comprehensive overview of state-of-the-art deep learning algorithms used for denoising medical images. A total of 120 relevant papers were reviewed, and after screening with specific inclusion and exclusion criteria, 104 papers were selected for analysis. This study aims to provide a thorough understanding for researchers in the field of intelligent denoising by presenting an extensive survey of current techniques and highlighting significant challenges that remain to be addressed. The findings of this review are expected to contribute to the development of intelligent models that enable timely and accurate diagnoses of medical disorders. It was found that 40% of the researchers used models based on Deep convolutional neural networks to denoise the images, followed by encoder-decoder (18%) and other artificial intelligence-based techniques (15%) (Like DIP, etc.). Generative adversarial network was used by 12%, transformer-based approaches (13%) and multilayer perceptron was used by 2% of the researchers. Moreover, Gaussian noise was present in 35% of the images, followed by speckle noise (16%), poisson noise (14%), artifacts (10%), rician noise (7%), Salt-pepper noise (6%), Impulse noise (3%) and other types of noise (9%). While the progress in developing novel models for the denoising of medical images is evident, significant work remains to be done in creating standardized denoising models that perform well across a wide spectrum of medical images. Overall, this review highlights the importance of denoising medical images and provides a comprehensive understanding of the current state-of-the-art deep learning algorithms in this field.

Enhancing Medical Image Denoising with Innovative Teacher-Student Model-Based Approaches for Precision Diagnostics

The realm of medical imaging is a critical frontier in precision diagnostics, where the clarity of the image is paramount. Despite advancements in imaging technology, noise remains a pervasive challenge that can obscure crucial details and impede accurate diagnoses. Addressing this, we introduce a novel teacher-student network model that leverages the potency of our bespoke NoiseContextNet Block to discern and mitigate noise with unprecedented precision. This innovation is coupled with an iterative pruning technique aimed at refining the model for heightened computational efficiency without compromising the fidelity of denoising. We substantiate the superiority and effectiveness of our approach through a comprehensive suite of experiments, showcasing significant qualitative enhancements across a multitude of medical imaging modalities. The visual results from a vast array of tests firmly establish our method's dominance in producing clearer, more reliable images for diagnostic purposes, thereby setting a new benchmark in medical image denoising.