Model-based Dynamic 3D MRI Reconstructions using Neural Fields and Tensor Product Expansions

• URL: http://arxiv.org/abs/2605.08275v1
• Date: Fri, 08 May 2026 04:04:30 GMT
• Title: Model-based Dynamic 3D MRI Reconstructions using Neural Fields and Tensor Product Expansions
• Abstract summary: We introduce a discretization-free, memory-efficient, model-based framework for dynamic 2D and 3D MRI reconstruction from highly undersampled data.<n>Our method outperforms state-of-the-art model-based reconstructions in dynamic 2D and 3D settings.
• Score: 38.95952035777659
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional MRI reconstruction methods treat images and coil sensitivities as discrete objects, leading to high memory demands and limited structural awareness that hamper effective regularization. These limitations hinder accurate reconstruction in highly undersampled scenarios, such as dynamic 3D cardiac magnetic resonance (CMR). We introduce a discretization-free, memory-efficient, model-based framework for dynamic 2D and 3D MRI reconstruction from highly undersampled data. We represent magnetization and coil sensitivities as continuous objects -- differentiable functions -- using tensor products of univariate neural fields. This tensor product structure enables scalable optimization in high-dimensional spatiotemporal settings. Our method outperforms state-of-the-art model-based reconstructions in dynamic 2D and 3D MR settings, preserving structure and motion even under aggressive undersampling (e.g., acceleration factor 16).

Related papers

• MotionDPS: Motion-Compensated 3D Brain MRI Reconstruction [39.243053176227065]
  We propose a motion-compensated 3D MRI that estimates the anatomical image, rigid-body motion parameters, and coil sensitivity maps directly from motion-corrupted k-space data.<n>Our approach integrates pretrained 3D complex-valued score-based diffusion models as expressive anatomical image priors within a physics-based forward model.<n> Experiments on simulated and real-motion brain MRI datasets demonstrate that the proposed method achieves improved image quality and motion robustness.
  arXiv  Detail & Related papers  (2026-05-21T07:53:06Z)
• Physics-Driven 3D Gaussian Rendering for Zero-Shot MRI Super-Resolution [13.552352258334908]
  High-resolution Magnetic Resonance Imaging (MRI) is vital for clinical diagnosis but limited by long acquisition times and motion artifacts.<n>Super-resolution (SR) reconstructs low-resolution scans into high-resolution images, yet existing methods are mutually constrained.<n>We propose a zero-shot MRI SR framework using explicit Gaussian representation to balance data requirements and efficiency.
  arXiv  Detail & Related papers  (2026-03-10T13:00:42Z)
• Flow-Guided Implicit Neural Representation for Motion-Aware Dynamic MRI Reconstruction [19.66198868468091]
  Dynamic magnetic resonance imaging (dMRI) captures anatomy temporally-resolved but is often challenged by limited sampling and motion-induced artifacts.<n>In this work, we propose a novel implicit neural representation framework that jointly models both the dynamic image sequence and its underlying motion field.<n> Experiments on dynamic cardiac MRI datasets demonstrate that the method outperforms state-of-the-art motion-inspired and deep learning approaches.
  arXiv  Detail & Related papers  (2025-11-21T04:51:23Z)
• 4D3R: Motion-Aware Neural Reconstruction and Rendering of Dynamic Scenes from Monocular Videos [52.89084603734664]
  We present 4D3R, a pose-free dynamic neural rendering framework that decouples static and dynamic components through a two-stage approach.<n>Our approach achieves up to 1.8dB PSNR improvement over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-11-07T13:25:50Z)
• From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction [15.340881123379567]
  In MRI, slice-to-volume reconstruction is critical for recovering consistent 3D brain volumes from 2D slices.<n>We propose a progressive refinement implicit neural representation framework (PR-INR)<n>Our PR-INR unifies motion correction, structural refinement, and volumetric synthesis within a geometry-aware coordinate space.
  arXiv  Detail & Related papers  (2025-06-19T10:58:43Z)
• DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos [52.46386528202226]
  We introduce the Deformable Gaussian Splats Large Reconstruction Model (DGS-LRM)<n>It is the first feed-forward method predicting deformable 3D Gaussian splats from a monocular posed video of any dynamic scene.<n>It achieves performance on par with state-of-the-art monocular video 3D tracking methods.
  arXiv  Detail & Related papers  (2025-06-11T17:59:58Z)
• Event-boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction [50.873820265165975]
  We introduce the first approach combining event cameras, which capture high-temporal-resolution, continuous motion data, with deformable 3D-GS for dynamic scene reconstruction.<n>We propose a GS-Threshold Joint Modeling strategy, creating a mutually reinforcing process that greatly improves both 3D reconstruction and threshold modeling.<n>We contribute the first event-inclusive 4D benchmark with synthetic and real-world dynamic scenes, on which our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-25T08:23:38Z)
• 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)
• EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision [85.17951804790515]
  EmerNeRF is a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes. It simultaneously captures scene geometry, appearance, motion, and semantics via self-bootstrapping. Our method achieves state-of-the-art performance in sensor simulation.
  arXiv  Detail & Related papers  (2023-11-03T17:59:55Z)
• Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction [15.444386058967579]
  There are still problems with dynamic MRI k-space reconstruction based on Compressed Sensing (CS) In this paper, we propose a novel robust lowrank dynamic MRI reconstruction optimization model via highly under-sampled Fourier Transform (DFT) Experiments on dynamic MRI data demonstrate the superior performance proposed method in terms of both reconstruction accuracy and time complexity.
  arXiv  Detail & Related papers  (2023-10-23T13:34:59Z)

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.