RNLFNet: Residual non-local Fourier network for undersampled MRI reconstruction

Zhu, Man; Xiong, Dongping; Ouyang, Lijun; Ouyang, Yan; Chen, Zhongze; Zhang, Xiaozhi

doi:10.1016/j.bspc.2023.104632

Cited by 9 publications

(7 citation statements)

References 39 publications

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“…The final complex MRIs were obtained by applying k-space inverse filtering to the predicted deghosted edge maps. Zhou et al [ 61 ] developed a deep Residual Non-Local Fourier Network (RNLFNet), which incorporated non-local Fourier attention and residual blocks. The model effectively learned information from both the spatial and frequency domains, capturing local details and global context between degraded MR images and ground truth image pairs, leading to improved reconstruction quality.…”

Section: Papers Improving Deep Mri Reconstruction Methodsmentioning

confidence: 99%

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review

Singh,

Monga,

de Moura

et al. 2023

Bioengineering

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Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches. This Systematic Literature Review (SLR) comprehensively analyzes deep MRI reconstruction models, emphasizing the key elements of recently proposed methods and highlighting their strengths and weaknesses. This SLR involves searching and selecting relevant studies from various databases, including Web of Science and Scopus, followed by a rigorous screening and data extraction process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It focuses on various techniques, such as residual learning, image representation using encoders and decoders, data-consistency layers, unrolled networks, learned activations, attention modules, plug-and-play priors, diffusion models, and Bayesian methods. This SLR also discusses the use of loss functions and training with adversarial networks to enhance deep MRI reconstruction methods. Moreover, we explore various MRI reconstruction applications, including non-Cartesian reconstruction, super-resolution, dynamic MRI, joint learning of reconstruction with coil sensitivity and sampling, quantitative mapping, and MR fingerprinting. This paper also addresses research questions, provides insights for future directions, and emphasizes robust generalization and artifact handling. Therefore, this SLR serves as a valuable resource for advancing fast MRI, guiding research and development efforts of MRI reconstruction for better image quality and faster data acquisition.

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Section: Papers Improving Deep Mri Reconstruction Methodsmentioning

confidence: 99%

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review

Singh,

Monga,

de Moura

et al. 2023

Bioengineering

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“…It is worth noting that due to the imaging characteristics of human body, there is non-local self-similarity in medical images [48]. There are also inherent feature correlations existing in MRI images, and some patches are likely to repeat at different positions within and across adjacent slices [49]. Nevertheless, since the receptive field size of the convolutional operations is relatively small, the CNN-based models always fail to capture the long-range dependencies.…”

Section: Related Workmentioning

confidence: 99%

TTMRI: Multislice texture transformer network for undersampled MRI reconstruction

Zhang,

Zhou,

Wan

et al. 2024

IET Image Processing

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Magnetic resonance imaging (MRI) is a non‐interposition imaging technique that provides rich anatomical and physiological information. Yet it is limited by the long imaging time. Recently, deep neural networks have shown potential to significantly accelerate MRI. However, most of these approaches ignore the correlation between adjacent slices in MRI image sequences. In addition, the existing deep learning‐based methods for MRI are mainly based on convolutional neural networks (CNNs). They fail to capture long‐distance dependencies due to the small receptive field. Inspired by the feature similarity in adjacent slices and impressive performance of Transformer for exploiting the long‐distance dependencies, a novel multislice texture transformer network is presented for undersampled MRI reconstruction (TTMRI). Specifically, the proposed TTMRI is consisted of four modules, namely the texture extraction, correlation calculation, texture transfer and texture synthesis. It takes three adjacent slices as inputs, in which the middle one is the target image to be reconstructed, and the other two are auxiliary images. The multiscale features are extracted by the texture extraction module and their inter‐dependencies are calculated by the correlation calculation module, respectively. Then the relevant features are transferred by the texture transfer module and fused by the texture synthesis module. By considering inter‐slice correlations and leveraging the Transformer architecture, the joint feature learning across target and adjacent slices are encouraged. Moreover, TTMRI can be stacked with multiple layers to recover more texture information at different levels. Extensive experiments demonstrate that the proposed TTMRI outperforms other state‐of‐the‐art methods in both quantitative and qualitative evaluationsions.

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“…Xie et al [28] proposed a strategy called Domain Guided-CNN, aiming to incorporate edge information in breast ultrasound (BUS) images for cancer diagnosis tasks to mimic doctors' diagnostic behavior. Zhou et al [29] proposed a method called Non-Local Fourier Attention, which combines self-attention mechanism with Fourier transform to capture distant spatial dependencies in the frequency domain of MRI. Xie et al [30] proposed a Multi-scale Efficient Network (MEN), which integrates various attention mechanisms to achieve comprehensive extraction of detailed features and semantic information in COVID-19 images through progressive learning.…”

Section: Related Workmentioning

confidence: 99%

MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging

Xue,

Lu,

Wang

et al. 2024

PLoS ONE

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In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively.

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RNLFNet: Residual non-local Fourier network for undersampled MRI reconstruction

Cited by 9 publications

References 39 publications

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review

TTMRI: Multislice texture transformer network for undersampled MRI reconstruction

MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging

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