Direct low-field MRI super-resolution using undersampled k-space

• URL: http://arxiv.org/abs/2603.00668v1
• Date: Sat, 28 Feb 2026 14:23:31 GMT
• Title: Direct low-field MRI super-resolution using undersampled k-space
• Abstract summary: We propose a novel framework for reconstructing high-field MR like images directly from undersampled low-field k-space.<n>Our approach employs a k-space dual channel U-Net that processes the real and imaginary components of undersampled k-space to restore missing frequency content.
• Score: 1.376408511310322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-field magnetic resonance imaging (MRI) provides affordable access to diagnostic imaging but suffers from prolonged acquisition and limited image quality. Accelerated imaging can be achieved with k-space undersampling, while super-resolution (SR) and image quality transfer (IQT) methods typically rely on spatial-domain post-processing. In this work, we propose a novel framework for reconstructing high-field MR like images directly from undersampled low-field k-space. Our approach employs a k-space dual channel U-Net that processes the real and imaginary components of undersampled k-space to restore missing frequency content. Experiments on low-field brain MRI demonstrate that our k-space-driven image enhancement consistently outperforms the counterpart spatial-domain method. Furthermore, reconstructions from undersampled k-space achieve image quality comparable to full k-space acquisitions. To the best of our knowledge, this is the first work that investigates low-field MRI SR/IQT directly from undersampled k-space.

Related papers

• Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Space [1.2599533416395765]
  We propose a novel deep learning framework based on a U-Net variant that operates directly in k-space to super-resolve low-field MR images.<n>Our results show that undersampled k-space reconstructions achieve comparable quality to full k-space acquisitions.
  arXiv  Detail & Related papers  (2026-03-14T21:25:46Z)
• Low-Field Magnetic Resonance Image Quality Enhancement using Undersampled k-Space and Out-of-Distribution Generalisation [1.376408511310322]
  We propose a novel framework that reconstructs high-field-like MR images directly from undersampled low-field MRI k-space.<n>This work is among the first to combine low-field MR image reconstruction, quality enhancement using undersampled k-space, and uncertainty quantification within a unified framework.
  arXiv  Detail & Related papers  (2026-03-14T21:11:30Z)
• No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space [18.698217373650092]
  Conventional clinical CMR pipelines rely on a sequential "reconstruct-then-analyze" paradigm.<n>We propose k-MTR, a k-space representation learning framework that aligns undersampled k-space data and fully-sampled images into a shared semantic manifold.<n>We demonstrate that this latent alignment enables the dense latent space embedded with high-level physiological semantics directly from undersampled frequencies.
  arXiv  Detail & Related papers  (2026-03-10T17:38:38Z)
• 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)
• Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstruction [5.910509015352437]
  Fast MRI reconstruction aims to restore high-quality images from the undersampled k-space.<n>Existing methods typically train deep learning models to map the undersampled data to artifact-free MRI images.<n>We propose a continuous k-space recovery network from a new perspective of implicit neural representation with image domain guidance.
  arXiv  Detail & Related papers  (2024-11-18T04:54:04Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  We propose a unified MRI reconstruction model robust to various measurement undersampling patterns and image resolutions.<n>Our model improves SSIM by 11% and PSNR by 4 dB over a state-of-the-art CNN (End-to-End VarNet) with 600$times$ faster inference than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction [31.313223729491703]
  We propose Adaptive Sampling for MR (ASMR), a sampling method that learns an adaptive policy to sequentially select k-space samples to optimize for target disease detection. ASMR reaches within 2% of the performance of a fully sampled while using only 8% of the k-space, as well as outperforming prior state-of-the-art work in k-space sampling such as EMRT, LO, and DPS.
  arXiv  Detail & Related papers  (2024-06-06T17:58:00Z)
• NeRF Solves Undersampled MRI Reconstruction [1.3597551064547502]
  This article presents a novel undersampled magnetic resonance imaging (MRI) technique that leverages the concept of Neural Radiance Field (NeRF) With radial undersampling, the corresponding imaging problem can be reformulated into an image modeling task from sparse-view rendered data. A multi-layer perceptron, which is designed to output an image intensity from a spatial coordinate, learns the MR physics-driven rendering relation between given measurement data and desired image.
  arXiv  Detail & Related papers  (2024-02-20T18:37:42Z)
• Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging [13.002583920505579]
  We propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. We integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying $l1$-norm and total-variation regularization.
  arXiv  Detail & Related papers  (2024-01-03T10:47:20Z)
• 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)
• Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling [10.74920257710449]
  In dynamic Magnetic Imaging (MRI), k-space is typically undersampled due to limited scan time. We propose a novel Transformer-based k-space Global Interpolation Network, termed k-GIN. Our k-GIN learns global dependencies among low- and high-frequency components of 2D+t k-space and uses it to interpolate unsampled data.
  arXiv  Detail & Related papers  (2023-07-24T10:20:14Z)
• 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)
• Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI [15.966488917066048]
  We propose an encoding enhanced (EN2) complex CNN for highly undersampled pulmonary MRI reconstruction. EN2 employs convolution along either the frequency or phase-encoding direction, resembling the mechanisms of k-space sampling. We also employ complex convolution to learn rich representations from the complex k-space data.
  arXiv  Detail & Related papers  (2023-06-21T02:08:27Z)
• Multi-Modal MRI Reconstruction with Spatial Alignment Network [51.74078260367654]
  In clinical practice, magnetic resonance imaging (MRI) with multiple contrasts is usually acquired in a single study. Recent researches demonstrate that, considering the redundancy between different contrasts or modalities, a target MRI modality under-sampled in the k-space can be better reconstructed with the helps from a fully-sampled sequence. In this paper, we integrate the spatial alignment network with reconstruction, to improve the quality of the reconstructed target modality.
  arXiv  Detail & Related papers  (2021-08-12T08:46:35Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)
• 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)

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.