Dual-Domain Cross-Iteration Squeeze-Excitation Network for Sparse Reconstruction of Brain MRI

• URL: http://arxiv.org/abs/2210.02523v1
• Date: Wed, 5 Oct 2022 19:44:56 GMT
• Title: Dual-Domain Cross-Iteration Squeeze-Excitation Network for Sparse Reconstruction of Brain MRI
• Authors: Xiongchao Chen, Yoshihisa Shinagawa, Zhigang Peng, Gerardo Hermosillo Valadez
• Abstract summary: Magnetic resonance imaging (MRI) is one of the most commonly applied tests in neurology and neurosurgery. Deep learning has provided new insights into the reconstruction of MRI. We present a new approach that fuses the information of k-space and MRI images using novel dual Squeeze-Excitation Networks and CrossIteration Residual Connections.
• Score: 1.0323063834827415
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Magnetic resonance imaging (MRI) is one of the most commonly applied tests in neurology and neurosurgery. However, the utility of MRI is largely limited by its long acquisition time, which might induce many problems including patient discomfort and motion artifacts. Acquiring fewer k-space sampling is a potential solution to reducing the total scanning time. However, it can lead to severe aliasing reconstruction artifacts and thus affect the clinical diagnosis. Nowadays, deep learning has provided new insights into the sparse reconstruction of MRI. In this paper, we present a new approach to this problem that iteratively fuses the information of k-space and MRI images using novel dual Squeeze-Excitation Networks and Cross-Iteration Residual Connections. This study included 720 clinical multi-coil brain MRI cases adopted from the open-source deidentified fastMRI Dataset. 8-folder downsampling rate was applied to generate the sparse k-space. Results showed that the average reconstruction error over 120 testing cases by our proposed method was 2.28%, which outperformed the existing image-domain prediction (6.03%, p<0.001), k-space synthesis (6.12%, p<0.001), and dual-domain feature fusion (4.05%, p<0.001).

Related papers

• 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)
• Recon-all-clinical: Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI [3.639043225506316]
  We introduce recon-all-clinical, a novel method for cortical reconstruction, registration, parcellation, and thickness estimation in brain MRI scans. Our approach employs a hybrid analysis method that combines a convolutional neural network (CNN) trained with domain randomization to predict signed distance functions. We tested recon-all-clinical on multiple datasets, including over 19,000 clinical scans.
  arXiv  Detail & Related papers  (2024-09-05T19:52:09Z)
• 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)
• CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction [62.61209705638161]
  There has been growing interest in deep learning-based CMR imaging algorithms. Deep learning methods require large training datasets. This dataset includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects.
  arXiv  Detail & Related papers  (2023-09-19T15:14:42Z)
• DD-CISENet: Dual-Domain Cross-Iteration Squeeze and Excitation Network for Accelerated MRI Reconstruction [1.0312968200748118]
  We present a novel DualDomain Cross-It Squeeze Excitation Network (DDCISENet) for accelerated MRI reconstruction. The information of kspaces and MRI images can be iteratively fused and maintained using the Cross-Iteration Residual connection (CIR) structures.
  arXiv  Detail & Related papers  (2023-04-28T20:44:48Z)
• A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation [9.388253054229155]
  implicit neural representation (INR) has emerged as a new deep learning paradigm for learning the internal continuity of an object. The proposed method outperforms existing methods by suppressing the aliasing artifacts and noise. The high-quality results and scanning specificity make the proposed method hold the potential for further accelerating the data acquisition of parallel MRI.
  arXiv  Detail & Related papers  (2022-10-19T10:16:03Z)
• 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)
• Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives [5.3148259096171175]
  One drawback of MRI is its comparatively slow scanning and reconstruction compared to other image modalities. Deep Neural Networks (DNNs) have been used in sparse MRI reconstruction models to recreate relatively high-quality images. Generative Adversarial Networks (GAN) based methods are proposed to solve fast MRI with enhanced image perceptual quality.
  arXiv  Detail & Related papers  (2021-05-04T23:59:00Z)
• 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)
• ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning [47.68307909984442]
  Single Image Super-Resolution (SISR) is a technique aimed to obtain high-resolution (HR) details from one single low-resolution input image. Deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts.
  arXiv  Detail & Related papers  (2021-02-25T14:52:23Z)
• Deep Residual Dense U-Net for Resolution Enhancement in Accelerated MRI Acquisition [19.422926534305837]
  We propose a deep-learning approach, aiming at reconstructing high-quality images from accelerated MRI acquisition. Specifically, we use Convolutional Neural Network (CNN) to learn the differences between the aliased images and the original images. Considering the peculiarity of the down-sampled k-space data, we introduce a new term to the loss function in learning, which effectively employs the given k-space data.
  arXiv  Detail & Related papers  (2020-01-13T19:01:17Z)

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.