Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI
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Abstract (arXiv)
The long acquisition time has limited the accessibility of magnetic resonance imaging (MRI) because it leads to patient discomfort and motion artifacts. Although several MRI techniques have been proposed to reduce the acquisition time, compressed sensing in magnetic resonance imaging (CS-MRI) enables fast acquisition without compromising SNR and resolution. However, existing CS-MRI methods suffer from the challenge of aliasing artifacts. This challenge results in the noise-like textures and missing the fine details, thus leading to unsatisfactory reconstruction performance. To tackle this challenge, we propose a hierarchical perception adversarial learning framework (HP-ALF). HP-ALF can perceive the image information in the hierarchical mechanism: image-level perception and patch-level perception. The former can reduce the visual perception difference in the entire image, and thus achieve aliasing artifact removal. The latter can reduce this difference in the regions of the image, and thus recover fine details. Specifically, HP-ALF achieves the hierarchical mechanism by utilizing multilevel perspective discrimination. This discrimination can provide the information from two perspectives (overall and regional) for adversarial learning. It also utilizes a global and local coherent discriminator to provide structure information to the generator during training. In addition, HP-ALF contains a context-aware learning block to effectively exploit the slice information between individual images for better reconstruction performance. The experiments validated on three datasets demonstrate the effectiveness of HP-ALF and its superiority to the comparative methods.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 58 references that are in the map. First posted on arXiv 27 Jan 2023.
Builds on
21 of 58 references.
References · 21
- 2016Least Squares Generative Adversarial Networkscited by 1,000
- 2017Wasserstein GANcited by 635
- 2017The Perception-Distortion Tradeoffcited by 582
- 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
- 2020A U-Net Based Discriminator for Generative Adversarial Networkscited by 128
- 2016Energy-based Generative Adversarial Networkcited by 119
- 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
- 2017Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Losscited by 82
- 2021Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformerscited by 76
- 2021Task Transformer Network for Joint MRI Reconstruction and Super-Resolutioncited by 58
- 2022Swin Transformer for Fast MRIcited by 56
- 2021AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysiscited by 38
9 more
- 2021COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensingcited by 32
- 2021Over-and-Under Complete Convolutional RNN for MRI Reconstructioncited by 29
- 2021Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Frameworkcited by 26
- 2021TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imagingcited by 18
- 2020Real or Not Real, that is the Questioncited by 13
- 2021Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstructioncited by 11
- 2019Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networkscited by 8
- 2020Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awarenesscited by 5
- 2021Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopycited by 1
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3 papers.