SMUG: Towards robust MRI reconstruction by smoothed unrolling

• URL: http://arxiv.org/abs/2303.12735v1
• Date: Tue, 14 Mar 2023 02:21:55 GMT
• Title: SMUG: Towards robust MRI reconstruction by smoothed unrolling
• Authors: Hui Li, Jinghan Jia, Shijun Liang, Yuguang Yao, Saiprasad Ravishankar, Sijia Liu
• Abstract summary: deep learning (DL) has gained much popularity for accelerated magnetic resonance imaging (MRI) Recent studies have shown that DL-based MRI reconstruction models could be oversensitive to tiny input perturbations. We propose SMOOTHED UNROLLING (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning operation.
• Score: 18.431095609802654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although deep learning (DL) has gained much popularity for accelerated magnetic resonance imaging (MRI), recent studies have shown that DL-based MRI reconstruction models could be oversensitive to tiny input perturbations (that are called 'adversarial perturbations'), which cause unstable, low-quality reconstructed images. This raises the question of how to design robust DL methods for MRI reconstruction. To address this problem, we propose a novel image reconstruction framework, termed SMOOTHED UNROLLING (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning operation. RS, which improves the tolerance of a model against input noises, has been widely used in the design of adversarial defense for image classification. Yet, we find that the conventional design that applies RS to the entire DL process is ineffective for MRI reconstruction. We show that SMUG addresses the above issue by customizing the RS operation based on the unrolling architecture of the DL-based MRI reconstruction model. Compared to the vanilla RS approach and several variants of SMUG, we show that SMUG improves the robustness of MRI reconstruction with respect to a diverse set of perturbation sources, including perturbations to the input measurements, different measurement sampling rates, and different unrolling steps. Code for SMUG will be available at https://github.com/LGM70/SMUG.

Related papers

• LDPM: Towards undersampled MRI reconstruction with MR-VAE and Latent Diffusion Prior [2.3007720628527104]
  A Latent Diffusion Prior based undersampled MRI reconstruction (LDPM) method is proposed. A sketcher module is utilized to provide appropriate control and balance the quality and fidelity of the reconstructed MR images. A VAE adapted for MRI tasks (MR-VAE) is explored, which can serve as the backbone for future MR-related tasks.
  arXiv  Detail & Related papers  (2024-11-05T09:51:59Z)
• Unifying Subsampling Pattern Variations for Compressed Sensing MRI with Neural Operators [72.79532467687427]
  Compressed Sensing MRI reconstructs images of the body's internal anatomy from undersampled and compressed measurements. Deep neural networks have shown great potential for reconstructing high-quality images from highly undersampled measurements. We propose a unified model that is robust to different subsampling patterns and image resolutions in CS-MRI.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Robust MRI Reconstruction by Smoothed Unrolling (SMUG) [17.391075587858058]
  We propose a novel image reconstruction framework, termed Smoothed Unrolling (SMUG) SMUG advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning approach. We show that SMUG improves the robustness of MRI reconstruction with respect to a diverse set of instability sources.
  arXiv  Detail & Related papers  (2023-12-12T22:57:14Z)
• DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models [11.068359534951783]
  DiffCMR perceives conditioning signals from the under-sampled MRI image slice and generates its corresponding fully-sampled MRI image slice. We validate DiffCMR with cine reconstruction and T1/T2 mapping tasks on MICCAI 2023 Cardiac MRI Reconstruction Challenge dataset. Results show that our method achieves state-of-the-art performance, exceeding previous methods by a significant margin.
  arXiv  Detail & Related papers  (2023-12-08T06:11:21Z)
• 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)
• Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction [3.083408283778178]
  This study introduces a novel approach for image reconstruction based on a diffusion model conditioned on the native data domain. The proposed method demonstrates a significant promise, particularly for reconstructing images at high acceleration factors.
  arXiv  Detail & Related papers  (2023-09-02T01:33:50Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstruction [25.078280843551322]
  We introduce a self-supervised pretraining procedure using contrastive learning to improve the accuracy of undersampled MRI reconstruction. Our experiments demonstrate improved reconstruction accuracy across a range of acceleration factors and datasets.
  arXiv  Detail & Related papers  (2023-06-01T10:29:58Z)
• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction [50.1787181309337]
  We propose a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling. The proposed algorithm has the potential to achieve real-time i-MRI for DBS and can be used for general purpose MR-guided intervention.
  arXiv  Detail & Related papers  (2022-03-28T14:03:45Z)
• ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer [60.27951773998535]
  We propose a recurrent transformer model, namely textbfReconFormer, for MRI reconstruction. It can iteratively reconstruct high fertility magnetic resonance images from highly under-sampled k-space data. We show that it achieves significant improvements over the state-of-the-art methods with better parameter efficiency.
  arXiv  Detail & Related papers  (2022-01-23T21:58:19Z)

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.