Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidence

• URL: http://arxiv.org/abs/2209.13818v1
• Date: Wed, 28 Sep 2022 03:54:56 GMT
• Title: Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidence
• Authors: Haibo Yang, Shengjie Zhang, Xiaoyang Han, Botao Zhao, Yan Ren, Yaru Sheng, and Xiao-Yong Zhang
• Abstract summary: Small lesions in magnetic resonance imaging (MRI) images are crucial for clinical diagnosis of many kinds of diseases. MRI quality can be easily degraded by various noise, which can greatly affect the accuracy of diagnosis of small lesion. In this work, we propose a voxel-wise hybrid residual-CNN model to denoise 3D MR images with small lesions.
• Score: 4.636940840535911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Small lesions in magnetic resonance imaging (MRI) images are crucial for clinical diagnosis of many kinds of diseases. However, the MRI quality can be easily degraded by various noise, which can greatly affect the accuracy of diagnosis of small lesion. Although some methods for denoising MR images have been proposed, task-specific denoising methods for improving the diagnosis confidence of small lesions are lacking. In this work, we propose a voxel-wise hybrid residual MLP-CNN model to denoise three-dimensional (3D) MR images with small lesions. We combine basic deep learning architecture, MLP and CNN, to obtain an appropriate inherent bias for the image denoising and integrate each output layers in MLP and CNN by adding residual connections to leverage long-range information. We evaluate the proposed method on 720 T2-FLAIR brain images with small lesions at different noise levels. The results show the superiority of our method in both quantitative and visual evaluations on testing dataset compared to state-of-the-art methods. Moreover, two experienced radiologists agreed that at moderate and high noise levels, our method outperforms other methods in terms of recovery of small lesions and overall image denoising quality. The implementation of our method is available at https://github.com/laowangbobo/Residual_MLP_CNN_Mixer.

Related papers

• 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. 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)
• Enhancing Deep Learning-Driven Multi-Coil MRI Reconstruction via Self-Supervised Denoising [4.6017417632210655]
  Self-supervised denoising is a pre-processing step for training deep learning (DL) based reconstruction methods. We show that denoising is an essential pre-processing technique capable of improving the efficacy of DL-based MRI reconstruction methods.
  arXiv  Detail & Related papers  (2024-11-19T23:17:09Z)
• 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)
• 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)
• Denoising of Three-Dimensional Fast Spin Echo Magnetic Resonance Images of Knee Joints using Spatial-Variant Noise-Relevant Residual Learning of Convolution Neural Network [0.0]
  Two-dimensional (2D) fast spin echo (FSE) techniques play a central role in the clinical magnetic resonance imaging (MRI) of knee joints. 3D FSE provides high-isotropic-resolution magnetic resonance (MR) images of knee joints, but it has a reduced signal-to-noise ratio compared to 2D FSE. Deep-learning denoising methods are a promising approach for denoising MR images, but they are often trained using synthetic noise.
  arXiv  Detail & Related papers  (2022-04-21T03:45:11Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• 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)
• Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease Classification [8.050897403457995]
  We propose an interpretability aware adversarial training regime to improve robustness against out-of-distribution samples originating from different MRI hardware. We present preliminary results showing promising performance on out-of-distribution samples.
  arXiv  Detail & Related papers  (2021-11-15T04:42:47Z)
• Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution [65.25508348574974]
  We go below the MRI acceleration factors reported by all published papers that reference the original fastMRI challenge. We consider powerful deep learning based image enhancement methods to compensate for the underresolved images. The quality of the reconstructed images surpasses that of the other methods, yielding an MSE of 0.00114, a PSNR of 29.6 dB, and an SSIM of 0.956 at x16 acceleration factor.
  arXiv  Detail & Related papers  (2021-03-04T10:45:01Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.