Spatial orthogonal attention generative adversarial network for MRI reconstruction

Zhou, Wenzhong; Du, Huiqian; Mei, Wenbo; Fang, Liping

doi:10.1002/mp.14509

Cited by 13 publications

(11 citation statements)

References 34 publications

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“…Compared with the same model without attention modules, this design achieves a higher reconstruction accuracy. However, a key limitation of attention modules is their high computational demand, which is addressed by the memory-efficient selfattention module proposed by Zhou et al [82].…”

Section: E Attentionmentioning

confidence: 99%

AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis

et al. 2022

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Section: E Attentionmentioning

confidence: 99%

AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis

et al. 2022

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“…Tan et al devised a CNN model with residual connections in which channel-and spatial-attention modules were engineered to reconstruct X-ray images of the lung, from a large dataset (>55,000 images) [80]. Other studies focused on MRI reconstruction and demonstrated accurate and generalisable hybrid models by analysing large and diverse imaging data [15,114,129,139].…”

Section: Table IIImentioning

confidence: 99%

Is Attention all You Need in Medical Image Analysis? A Review

Papanastasiou,

Dikaios,

Huang

et al. 2024

IEEE J. Biomed. Health Inform.

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Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. Despite their important advances, typical CNN have relatively limited capabilities in modelling "global" pixel interactions, which restricts their generalisation ability to understand out-ofdistribution data with different "global" information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments ("Transf/Attention") which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced an analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated.

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“…for MRI reconstruction. Zhu et al [43] showed that spatial attention can be applied to GAN and reconstruct desired MR images. However, the study did not use channel-wise attention.…”

Section: Model Wasmentioning

confidence: 99%

A Modified Generative Adversarial Network Using Spatial and Channel-Wise Attention for CS-MRI Reconstruction

Wang

2021

IEEE Access

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Compressed sensing (CS) can speed up the magnetic resonance imaging (MRI) process and reconstruct high-quality images from under-sampled k-space data. However, traditional CS-MRI suffers from slow iterations and artifacts caused by noise when the acceleration factor is high. Currently, deep learning has been introduced to address these issues. Although some improvements have been achieved, the reconstruction problem under high under-sampling rates has not been solved. Thus, our study proposed a novel CS-MRI reconstruction method called RSCA-GAN. The generator of RSCA-GAN is a residual U-net consisting of both spatial and channel-wise attention. The proposed RSCA-GAN was compared to the zero-filling, DAGAN, RefineGAN, and RCA-GAN using both cartesian and non-cartesian undersampling masks on brain and knee datasets. The sampling rates of cartesian masks are 25%, 16.7%, and 12.5% and the sampling rates of spiral and radial masks are 30%, 20%, and 10%. Peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and normalized mean square error (NMSE) were used to evaluate the reconstructed image quality, and the rank-sum test was adopted to evaluate the significant difference among different approaches. P < 0.05 indicated statistical significance. The results demonstrated that RSCA-GAN outperforms the other approaches for all the quantitative metrics. Thus, RSCA-GAN exhibits excellent reconstruction performance at high under-sampling rates and is suitable for clinical applications.

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Spatial orthogonal attention generative adversarial network for MRI reconstruction

Cited by 13 publications

References 34 publications

AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis

AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis

Is Attention all You Need in Medical Image Analysis? A Review

A Modified Generative Adversarial Network Using Spatial and Channel-Wise Attention for CS-MRI Reconstruction

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