Deep unrolling Shrinkage Network for Dynamic MR imaging

• URL: http://arxiv.org/abs/2307.09818v1
• Date: Wed, 19 Jul 2023 08:06:37 GMT
• Title: Deep unrolling Shrinkage Network for Dynamic MR imaging
• Authors: Yinghao Zhang, Xiaodi Li, Weihang Li, Yue Hu
• Abstract summary: We propose a novel operator, called soft thresholding with channel attention (AST), that learns the threshold for each channel. We put forward a novel deep unrolling shrinkage network (DUS-Net) by unrolling the alternating direction method of multipliers. Experimental results on an open-access dynamic cine MR dataset demonstrate that the proposed DUS-Net outperforms the state-of-the-art methods.
• Score: 8.590614722154063
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep unrolling networks that utilize sparsity priors have achieved great success in dynamic magnetic resonance (MR) imaging. The convolutional neural network (CNN) is usually utilized to extract the transformed domain, and then the soft thresholding (ST) operator is applied to the CNN-transformed data to enforce the sparsity priors. However, the ST operator is usually constrained to be the same across all channels of the CNN-transformed data. In this paper, we propose a novel operator, called soft thresholding with channel attention (AST), that learns the threshold for each channel. In particular, we put forward a novel deep unrolling shrinkage network (DUS-Net) by unrolling the alternating direction method of multipliers (ADMM) for optimizing the transformed $l_1$ norm dynamic MR reconstruction model. Experimental results on an open-access dynamic cine MR dataset demonstrate that the proposed DUS-Net outperforms the state-of-the-art methods. The source code is available at \url{https://github.com/yhao-z/DUS-Net}.

Related papers

• JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRI [6.081607038128913]
  We propose a novel deep unrolling network, JotlasNet, for dynamic MRI reconstruction. Joint low-rank and sparse unrolling networks have shown superior performance in dynamic MRI reconstruction.
  arXiv  Detail & Related papers  (2025-02-17T12:43:04Z)
• Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware [78.17783007774295]
  This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval. A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed. The converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
  arXiv  Detail & Related papers  (2024-12-05T09:41:33Z)
• Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems [74.52117784544758]
  This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
  arXiv  Detail & Related papers  (2023-06-08T06:15:17Z)
• T2LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstruction [10.402013638387334]
  We propose a deep unrolling network that utilizes the convolutional neural network (CNN) to adaptively learn the transformed domain for leveraging tensor low-rank priors. Under the supervised mechanism, the learning of the tensor low-rank domain is directly guided by the reconstruction accuracy. Experiments on two dynamic cardiac MRI datasets demonstrate that T2LR-Net outperforms the state-of-the-art optimization-based and unrolling network-based methods.
  arXiv  Detail & Related papers  (2022-09-08T14:11:02Z)
• Interference Cancellation GAN Framework for Dynamic Channels [74.22393885274728]
  We introduce an online training framework that can adapt to any changes in the channel. Our framework significantly outperforms recent neural network models on highly dynamic channels.
  arXiv  Detail & Related papers  (2022-08-17T02:01:18Z)
• Dynamic MRI using Learned Transform-based Deep Tensor Low-Rank Network (DTLR-Net) [9.658908705889777]
  We introduce a model-based deep learning network by learning the tensor low-rank prior to the cardiac dynamic MR images. The proposed framework is able to provide improved recovery results compared with the state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2022-06-02T02:55:41Z)
• MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking [72.65494220685525]
  We propose a new dynamic modality-aware filter generation module (named MFGNet) to boost the message communication between visible and thermal data. We generate dynamic modality-aware filters with two independent networks. The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively. To address issues caused by heavy occlusion, fast motion, and out-of-view, we propose to conduct a joint local and global search by exploiting a new direction-aware target-driven attention mechanism.
  arXiv  Detail & Related papers  (2021-07-22T03:10:51Z)
• AutoPruning for Deep Neural Network with Dynamic Channel Masking [28.018077874687343]
  We propose a learning based auto pruning algorithm for deep neural network. A two objectives' problem that aims for the the weights and the best channels for each layer is first formulated. An alternative optimization approach is then proposed to derive the optimal channel numbers and weights simultaneously.
  arXiv  Detail & Related papers  (2020-10-22T20:12:46Z)
• A Deep-Unfolded Reference-Based RPCA Network For Video Foreground-Background Separation [86.35434065681925]
  This paper proposes a new deep-unfolding-based network design for the problem of Robust Principal Component Analysis (RPCA) Unlike existing designs, our approach focuses on modeling the temporal correlation between the sparse representations of consecutive video frames. Experimentation using the moving MNIST dataset shows that the proposed network outperforms a recently proposed state-of-the-art RPCA network in the task of video foreground-background separation.
  arXiv  Detail & Related papers  (2020-10-02T11:40:09Z)
• Deep Low-rank Prior in Dynamic MR Imaging [30.70648993986445]
  We introduce two novel schemes to introduce the learnable low-rank prior into deep network architectures. In the unrolling manner, we put forward a model-based unrolling sparse and low-rank network for dynamic MR imaging, dubbed SLR-Net. In the plug-and-play manner, we present a plug-and-play LR network module that can be easily embedded into any other dynamic MR neural networks.
  arXiv  Detail & Related papers  (2020-06-22T09:26:10Z)
• Model Fusion via Optimal Transport [64.13185244219353]
  We present a layer-wise model fusion algorithm for neural networks. We show that this can successfully yield "one-shot" knowledge transfer between neural networks trained on heterogeneous non-i.i.d. data.
  arXiv  Detail & Related papers  (2019-10-12T22:07:15Z)

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.