Low-rank Tensor Assisted K-space Generative Model for Parallel Imaging Reconstruction

• URL: http://arxiv.org/abs/2212.05503v1
• Date: Sun, 11 Dec 2022 13:34:43 GMT
• Title: Low-rank Tensor Assisted K-space Generative Model for Parallel Imaging Reconstruction
• Authors: Wei Zhang, Zengwei Xiao, Hui Tao, Minghui Zhang, Xiaoling Xu, Qiegen Liu
• Abstract summary: We present a new idea, low-rank tensor assisted k-space generative model (LR-KGM) for parallel imaging reconstruction. This means that we transform original prior information into high-dimensional prior information for learning. Experimental comparisons with the state-of-the-arts demonstrated that the proposed LR-KGM method achieved better performance.
• Score: 14.438899814473446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although recent deep learning methods, especially generative models, have shown good performance in fast magnetic resonance imaging, there is still much room for improvement in high-dimensional generation. Considering that internal dimensions in score-based generative models have a critical impact on estimating the gradient of the data distribution, we present a new idea, low-rank tensor assisted k-space generative model (LR-KGM), for parallel imaging reconstruction. This means that we transform original prior information into high-dimensional prior information for learning. More specifically, the multi-channel data is constructed into a large Hankel matrix and the matrix is subsequently folded into tensor for prior learning. In the testing phase, the low-rank rotation strategy is utilized to impose low-rank constraints on tensor output of the generative network. Furthermore, we alternately use traditional generative iterations and low-rank high-dimensional tensor iterations for reconstruction. Experimental comparisons with the state-of-the-arts demonstrated that the proposed LR-KGM method achieved better performance.

Related papers

• Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics [10.673414267895355]
  We present a novel approach for compressing over parameterized models. Our algorithm improves the training efficiency by more than 2x, without compromising generalization.
  arXiv  Detail & Related papers  (2023-11-08T23:57:03Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval [68.7008281316644]
  ReDi is a learning-free Retrieval-based Diffusion sampling framework. We show that ReDi improves the model inference efficiency by 2x speedup.
  arXiv  Detail & Related papers  (2023-02-05T03:01:28Z)
• Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problem [97.64313409741614]
  We prove fast mixing and characterize the stationary distribution of the Langevin Algorithm for inverting random weighted DNN generators. We propose to do posterior sampling in the latent space of a pre-trained generative model.
  arXiv  Detail & Related papers  (2022-06-18T03:47:37Z)
• Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection [5.672132510411465]
  This paper presents a principled approach for detecting anomalous and out-of-distribution (OOD) samples in deep neural networks (DNN) We propose the application of linear statistical dimensionality reduction techniques on the semantic features produced by a DNN. We show that the "feature reconstruction error" (FRE), which is the $ell$-norm of the difference between the original feature in the high-dimensional space and the pre-image of its low-dimensional reduced embedding, is highly effective for OOD and anomaly detection.
  arXiv  Detail & Related papers  (2022-03-20T00:55:20Z)
• Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover [87.28082715343896]
  We consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications. We design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. The proposed REST network is shown to outperform state-of-the-art model-based and data-driven algorithms in both compressive sensing and radar imaging problems.
  arXiv  Detail & Related papers  (2021-10-20T06:15:45Z)
• Deep Manifold Learning for Dynamic MR Imaging [30.70648993986445]
  We develop a deep learning method on a nonlinear manifold to explore the temporal redundancy of dynamic signals to reconstruct cardiac MRI data. The proposed method can obtain improved reconstruction compared with a compressed sensing (CS) method k-t SLR and two state-of-the-art deep learning-based methods, DC-CNN and CRNN.
  arXiv  Detail & Related papers  (2021-03-09T02:18:08Z)
• DCTRGAN: Improving the Precision of Generative Models with Reweighting [1.2622634782102324]
  We introduce a post-hoc correction to deep generative models to further improve their fidelity. The correction takes the form of a reweighting function that can be applied to generated examples. We show that the weighted GAN examples significantly improve the accuracy of the generated samples without a large loss in statistical power.
  arXiv  Detail & Related papers  (2020-09-03T18:00:27Z)
• Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data [17.415937218905125]
  CS-MRI has shown promise in reconstructing under-sampled MR images. Deep learning models have been developed that model the iterative nature of classical techniques by unrolling iterations in a neural network. In this paper, we explore a novel strategy to train an unrolled reconstruction network in an unsupervised fashion by adopting a loss function widely-used in classical optimization schemes.
  arXiv  Detail & Related papers  (2020-07-29T17:46:55Z)
• Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation [105.33409035876691]
  This paper explores the problem of multi-view spectral clustering (MVSC) based on tensor low-rank modeling. We design a novel structured tensor low-rank norm tailored to MVSC. We show that the proposed method outperforms state-of-the-art methods to a significant extent.
  arXiv  Detail & Related papers  (2020-04-30T11:52:12Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)

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.