Robust MRI Reconstruction by Smoothed Unrolling (SMUG)
- URL: http://arxiv.org/abs/2312.07784v1
- Date: Tue, 12 Dec 2023 22:57:14 GMT
- Title: Robust MRI Reconstruction by Smoothed Unrolling (SMUG)
- Authors: Shijun Liang, Van Hoang Minh Nguyen, Jinghan Jia, Ismail Alkhouri,
Sijia Liu, Saiprasad Ravishankar
- Abstract summary: 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.
- Score: 18.42785884266972
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As the popularity of deep learning (DL) in the field of magnetic resonance
imaging (MRI) continues to rise, recent research has indicated that DL-based
MRI reconstruction models might be excessively sensitive to minor input
disturbances, including worst-case additive perturbations. This sensitivity
often leads to unstable, aliased images. This raises the question of how to
devise DL techniques for MRI reconstruction that can be robust to train-test
variations. 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 approach. RS, which improves the tolerance of a
model against input noises, has been widely used in the design of adversarial
defense approaches for image classification tasks. Yet, we find that the
conventional design that applies RS to the entire DL-based MRI model is
ineffective. In this paper, we show that SMUG and its variants address the
above issue by customizing the RS process based on the unrolling architecture
of a DL-based MRI reconstruction model. Compared to the vanilla RS approach, we
show that SMUG improves the robustness of MRI reconstruction with respect to a
diverse set of instability sources, including worst-case and random noise
perturbations to input measurements, varying measurement sampling rates, and
different numbers of unrolling steps. Furthermore, we theoretically analyze the
robustness of our method in the presence of perturbations.
Related papers
- Noise Level Adaptive Diffusion Model for Robust Reconstruction of
Accelerated MRI [35.65325713990205]
Real-world MRI acquisitions already contain inherent noise due to thermal fluctuations.
Common scenarios can lead to sub-optimal performance or complete failure of existing diffusion model-based reconstruction techniques.
We propose a posterior sampling strategy with a novel NoIse Level Adaptive Data Consistency (Nila-DC) operation.
arXiv Detail & Related papers (2024-03-08T12:07:18Z) - 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) - SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models [76.43625653814911]
Diffusion models have gained popularity for accelerated MRI reconstruction due to their high sample quality.
They can effectively serve as rich data priors while incorporating the forward model flexibly at inference time.
We introduce SURE-based MRI Reconstruction with Diffusion models (SMRD) to enhance robustness during testing.
arXiv Detail & Related papers (2023-10-03T05:05:35Z) - 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) - SMUG: Towards robust MRI reconstruction by smoothed unrolling [18.431095609802654]
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.
arXiv Detail & Related papers (2023-03-14T02:21:55Z) - 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) - Reference-based Magnetic Resonance Image Reconstruction Using Texture
Transforme [86.6394254676369]
We propose a novel Texture Transformer Module (TTM) for accelerated MRI reconstruction.
We formulate the under-sampled data and reference data as queries and keys in a transformer.
The proposed TTM can be stacked on prior MRI reconstruction approaches to further improve their performance.
arXiv Detail & Related papers (2021-11-18T03:06:25Z)
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.