Sparse Mixture-of-Experts for Non-Uniform Noise Reduction in MRI Images

• URL: http://arxiv.org/abs/2501.14198v2
• Date: Wed, 12 Mar 2025 02:32:20 GMT
• Title: Sparse Mixture-of-Experts for Non-Uniform Noise Reduction in MRI Images
• Authors: Zeyun Deng, Joseph Campbell,
• Abstract summary: We introduce a novel approach leveraging a sparse mixture-of-experts framework for MRI image denoising.<n>Each expert is a specialized denoising convolutional neural network fine-tuned to target specific noise characteristics associated with different image regions.<n>Our method demonstrates superior performance over state-of-the-art denoising techniques on both synthetic and real-world MRI datasets.
• Score: 4.1738581761446145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Magnetic Resonance Imaging (MRI) is an essential diagnostic tool in clinical settings but its utility is often hindered by noise artifacts introduced during the imaging process. Effective denoising is critical for enhancing image quality while preserving anatomical structures. However traditional denoising methods which typically assume uniform noise distributions struggle to handle the non-uniform noise commonly present in MRI images. In this paper we introduce a novel approach leveraging a sparse mixture-of-experts framework for MRI image denoising. Each expert is a specialized denoising convolutional neural network fine-tuned to target specific noise characteristics associated with different image regions. Our method demonstrates superior performance over state-of-the-art denoising techniques on both synthetic and real-world MRI datasets. Furthermore we show that it generalizes effectively to unseen datasets highlighting its robustness and adaptability.

Related papers

• Two-stage Deep Denoising with Self-guided Noise Attention for Multimodal Medical Images [8.643724626327852]
  This study addresses the limitation of the contemporary denoising methods with an artificial intelligence (AI)-driven two-stage learning strategy. The proposed method learns to estimate the residual noise from the noisy images. It incorporates a novel noise attention mechanism to correlate estimated residual noise with noisy inputs to perform denoising in a course-to-refine manner.
  arXiv  Detail & Related papers  (2025-03-10T01:26:47Z)
• 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.<n>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)
• Contextual Checkerboard Denoise -- A Novel Neural Network-Based Approach for Classification-Aware OCT Image Denoising [1.8032335403003321]
  We introduce a novel neural network based method -- emphContextual Checkerboard Denoising, that can learn denoising from only a dataset of noisy images.<n>Our proposed method significantly improves image quality, providing clearer and more detailed OCT images, while enhancing diagnostic accuracy.
  arXiv  Detail & Related papers  (2024-11-29T08:51:43Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  Deep neural networks have shown great potential for reconstructing high-fidelity images from undersampled measurements. Our model is based on neural operators, a discretization-agnostic architecture. Our inference speed is also 1,400x faster than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising [0.0]
  The application of Deep Neural Networks (DNNs) to image denoising has challenged traditional denoising methods. This paper presents a comprehensive evaluation of these methods on MRI data afflicted with Gaussian and Poisson noise types. Our main contribution lies in the effective adaptation and implementation of the SURE, eSURE, and particularly the ePURE frameworks for medical images.
  arXiv  Detail & Related papers  (2024-07-22T17:04:21Z)
• Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI [34.361078452552945]
  Real-world MRI acquisitions already contain inherent noise due to thermal fluctuations. We propose a posterior sampling strategy with a novel NoIse Level Adaptive Data Consistency (Nila-DC) operation. Our method surpasses the state-of-the-art MRI reconstruction methods, and is highly robust against various noise levels.
  arXiv  Detail & Related papers  (2024-03-08T12:07:18Z)
• 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)
• Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders (DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN) [68.8204255655161]
  Cycle Consistent Generative Adversarial Network (GAN) is implemented to yield high-field, high resolution, high signal-to-noise ratio (SNR) Magnetic Resonance Imaging (MRI) images. Images were utilized to train a Denoising Autoencoder (DAE) and a Cycle-GAN, with paired and unpaired cases. This work demonstrates the use of a generative deep learning model that can outperform classical DAEs to improve low-field MRI images and does not require image pairs.
  arXiv  Detail & Related papers  (2023-07-12T00:01:00Z)
• 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)
• DDM$^2$: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models [0.3149883354098941]
  We propose a self-supervised denoising method for MRI denoising using diffusion denoising generative models. Our framework integrates statistic-based denoising theory into diffusion models and performs denoising through conditional generation.
  arXiv  Detail & Related papers  (2023-02-06T18:56:39Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Zero-shot Blind Image Denoising via Implicit Neural Representations [77.79032012459243]
  We propose an alternative denoising strategy that leverages the architectural inductive bias of implicit neural representations (INRs) We show that our method outperforms existing zero-shot denoising methods under an extensive set of low-noise or real-noise scenarios.
  arXiv  Detail & Related papers  (2022-04-05T12:46:36Z)
• MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion [38.62448918459113]
  We propose a new denoising method based on score-based reverse diffusion sampling. Our network, trained only with coronal knee scans, excels even on out-of-distribution in vivo liver MRI data.
  arXiv  Detail & Related papers  (2022-03-23T10:35:06Z)

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.