Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI

• URL: http://arxiv.org/abs/2302.10309v1
• Date: Fri, 27 Jan 2023 14:54:44 GMT
• Title: Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI
• Authors: Zhifan Gao, Yifeng Guo, Jiajing Zhang, Tieyong Zeng, Guang Yang
• Abstract summary: We propose a hierarchical perception adversarial learning framework (HP-ALF) to tackle the challenge of aliasing artifacts. HP-ALF can perceive the image information in the hierarchical mechanism: image-level perception and patch-level perception. The experiments validated on three datasets demonstrate the effectiveness of HP-ALF.
• Score: 16.432649991854984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• DDO-IN: Dual Domains Optimization for Implicit Neural Network to Eliminate Motion Artifact in Magnetic Resonance Imaging [1.0951772570165874]
  We present a novel dual-domain optimization (DDO) approach that integrates information from the pixel and frequency domains. Experimental results on the NYU fastMRI dataset demonstrate that our method outperforms existing approaches in multiple evaluation metrics.
  arXiv  Detail & Related papers  (2025-03-11T05:26:03Z)
• Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model [58.694932010573346]
  Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis. We propose a novel unsupervised purification method which leverages pixel-frequency information of noisy MRI images to guide a pre-trained diffusion model to recover clean MRI images.
  arXiv  Detail & Related papers  (2024-12-10T15:25:18Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction [62.61209705638161]
  There has been growing interest in deep learning-based CMR imaging algorithms. Deep learning methods require large training datasets. This dataset includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects.
  arXiv  Detail & Related papers  (2023-09-19T15:14:42Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• Attention Hybrid Variational Net for Accelerated MRI Reconstruction [7.046523233290946]
  The application of compressed sensing (CS)-enabled data reconstruction for accelerating magnetic resonance imaging (MRI) remains a challenging problem. This is due to the fact that the information lost in k-space from the acceleration mask makes it difficult to reconstruct an image similar to the quality of a fully sampled image. We propose a deep learning-based attention hybrid variational network that performs learning in both the k-space and image domain.
  arXiv  Detail & Related papers  (2023-06-21T16:19:07Z)
• BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding [51.911473457195555]
  BrainCLIP is a task-agnostic fMRI-based brain decoding model. It bridges the modality gap between brain activity, image, and text. BrainCLIP can reconstruct visual stimuli with high semantic fidelity.
  arXiv  Detail & Related papers  (2023-02-25T03:28:54Z)
• Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning [53.85892601302974]
  We propose an effective image dehazing method named Hierarchical Contrastive Dehazing (HCD) HCD consists of a hierarchical dehazing network (HDN) and a novel hierarchical contrastive loss (HCL)
  arXiv  Detail & Related papers  (2022-12-22T03:57:06Z)
• Facial Image Reconstruction from Functional Magnetic Resonance Imaging via GAN Inversion with Improved Attribute Consistency [5.705640492618758]
  We propose a new framework to reconstruct facial images from fMRI data. The proposed framework accomplishes two goals: (1) reconstructing clear facial images from fMRI data and (2) maintaining the consistency of semantic characteristics.
  arXiv  Detail & Related papers  (2022-07-03T11:18:35Z)
• Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease Classification [8.050897403457995]
  We propose an interpretability aware adversarial training regime to improve robustness against out-of-distribution samples originating from different MRI hardware. We present preliminary results showing promising performance on out-of-distribution samples.
  arXiv  Detail & Related papers  (2021-11-15T04:42:47Z)
• Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives [5.3148259096171175]
  One drawback of MRI is its comparatively slow scanning and reconstruction compared to other image modalities. Deep Neural Networks (DNNs) have been used in sparse MRI reconstruction models to recreate relatively high-quality images. Generative Adversarial Networks (GAN) based methods are proposed to solve fast MRI with enhanced image perceptual quality.
  arXiv  Detail & Related papers  (2021-05-04T23:59:00Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)
• Deep Residual Dense U-Net for Resolution Enhancement in Accelerated MRI Acquisition [19.422926534305837]
  We propose a deep-learning approach, aiming at reconstructing high-quality images from accelerated MRI acquisition. Specifically, we use Convolutional Neural Network (CNN) to learn the differences between the aliased images and the original images. Considering the peculiarity of the down-sampled k-space data, we introduce a new term to the loss function in learning, which effectively employs the given k-space data.
  arXiv  Detail & Related papers  (2020-01-13T19:01:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.