PINGS-X: Physics-Informed Normalized Gaussian Splatting with Axes Alignment for Efficient Super-Resolution of 4D Flow MRI

• URL: http://arxiv.org/abs/2511.11048v1
• Date: Fri, 14 Nov 2025 08:01:24 GMT
• Title: PINGS-X: Physics-Informed Normalized Gaussian Splatting with Axes Alignment for Efficient Super-Resolution of 4D Flow MRI
• Authors: Sun Jo, Seok Young Hong, JinHyun Kim, Seungmin Kang, Ahjin Choi, Don-Gwan An, Simon Song, Je Hyeong Hong,
• Abstract summary: 4D flow MRI requires high resolution for critical conditions such as stenosis or aneurysms.<n>Recent studies have leveraged physics-informed neural networks for super-resolution MRI data, but their practical applicability is limited.<n>We propose PINGS-X, a novel framework modeling high-resolution flow velocities using axes-aligned Gaussian representations.
• Score: 4.233023129369643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 4D flow magnetic resonance imaging (MRI) is a reliable, non-invasive approach for estimating blood flow velocities, vital for cardiovascular diagnostics. Unlike conventional MRI focused on anatomical structures, 4D flow MRI requires high spatiotemporal resolution for early detection of critical conditions such as stenosis or aneurysms. However, achieving such resolution typically results in prolonged scan times, creating a trade-off between acquisition speed and prediction accuracy. Recent studies have leveraged physics-informed neural networks (PINNs) for super-resolution of MRI data, but their practical applicability is limited as the prohibitively slow training process must be performed for each patient. To overcome this limitation, we propose PINGS-X, a novel framework modeling high-resolution flow velocities using axes-aligned spatiotemporal Gaussian representations. Inspired by the effectiveness of 3D Gaussian splatting (3DGS) in novel view synthesis, PINGS-X extends this concept through several non-trivial novel innovations: (i) normalized Gaussian splatting with a formal convergence guarantee, (ii) axes-aligned Gaussians that simplify training for high-dimensional data while preserving accuracy and the convergence guarantee, and (iii) a Gaussian merging procedure to prevent degenerate solutions and boost computational efficiency. Experimental results on computational fluid dynamics (CFD) and real 4D flow MRI datasets demonstrate that PINGS-X substantially reduces training time while achieving superior super-resolution accuracy. Our code and datasets are available at https://github.com/SpatialAILab/PINGS-X.

Related papers

• M-Gaussian: An Magnetic Gaussian Framework for Efficient Multi-Stack MRI Reconstruction [8.108801952103073]
  We present M-Gaussian, adapting 3D Gaussian Splatting to MRI reconstruction.<n>On the FeTA dataset, M-Gaussian achieves 40.31 dB PSNR while being 14 times faster.
  arXiv  Detail & Related papers  (2026-02-24T12:57:34Z)
• Adapting HFMCA to Graph Data: Self-Supervised Learning for Generalizable fMRI Representations [57.054499278843856]
  Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies.<n>Traditional self-supervised learning methods inspired by computer vision often rely on positive and negative sample pairs.<n>We propose adapting a recently developed Hierarchical Functional Maximal Correlation Algorithm (HFMCA) to graph-structured fMRI data.
  arXiv  Detail & Related papers  (2025-10-05T12:35:01Z)
• From Promise to Practical Reality: Transforming Diffusion MRI Analysis with Fast Deep Learning Enhancement [35.368152968098194]
  FastFOD-Net is an end-to-end deep learning framework enhancing FODs with superior performance and delivering training/inference efficiency for clinical use.<n>This work will facilitate the more widespread adoption of, and build clinical trust in, deep learning based methods for diffusion MRI enhancement.
  arXiv  Detail & Related papers  (2025-08-13T17:56:29Z)
• Localized FNO for Spatiotemporal Hemodynamic Upsampling in Aneurysm MRI [7.787686784329426]
  Localized Operator (LoFNO) is a novel 3D architecture that enhances both spatial and temporal resolution.<n>LoFNO predicts wall shear stress directly from clinical imaging data.
  arXiv  Detail & Related papers  (2025-07-18T10:00:38Z)
• Towards a general-purpose foundation model for fMRI analysis [58.06455456423138]
  We introduce NeuroSTORM, a framework that learns from 4D fMRI volumes and enables efficient knowledge transfer across diverse applications.<n>NeuroSTORM is pre-trained on 28.65 million fMRI frames (>9,000 hours) from over 50,000 subjects across multiple centers and ages 5 to 100.<n>It outperforms existing methods across five tasks: age/gender prediction, phenotype prediction, disease diagnosis, fMRI-to-image retrieval, and task-based fMRI.
  arXiv  Detail & Related papers  (2025-06-11T23:51:01Z)
• X$^{2}$-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time Tomographic Reconstruction [64.2059940799033]
  Current methods discretize temporal resolution into fixed phases with respiratory gating devices.<n>X$2$-Gaussian, a novel framework, enables continuous-time 4DCT reconstruction by integrating dynamic radiative splatting with self-supervised respiratory motion learning.
  arXiv  Detail & Related papers  (2025-03-27T17:59:57Z)
• Deep learning for temporal super-resolution 4D Flow MRI [34.90138772411514]
  The aim of this study was to implement and evaluate a residual network for temporal super-resolution 4D Flow MRI.<n>Training and testing were performed using synthetic 4D Flow MRI data originating from patient-specific in-silico models, as well as using in-vivo datasets.<n>Our results highlight the potential of utilizing data-driven neural networks for temporal super-resolution 4D Flow MRI.
  arXiv  Detail & Related papers  (2025-01-15T13:01:47Z)
• 4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images [49.170407434313475]
  Existing methods often produce suboptimal results or require excessive computation time.<n>We propose 4D radiative Gaussian splatting (4DRGS) to achieve high-quality reconstruction efficiently.<n>4DRGS achieves impressive results in 5 minutes training, which is 32x faster than the state-of-the-art method.
  arXiv  Detail & Related papers  (2024-12-17T13:51:56Z)
• 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)
• Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI [1.207455285737927]
  Our study investigates SIRENs for time-varying 3-directional velocity fields measured in the aorta by 4D flow MRI. We trained our method on voxel coordinates and benchmarked our approach using synthetic measurements and a real 4D flow MRI scan. Our optimized SIREN architecture outperformed state-of-the-art techniques, producing denoised and super-resolved velocity fields from clinical data.
  arXiv  Detail & Related papers  (2023-02-24T08:42:04Z)
• Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers [72.047680167969]
  This article aims to introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods. We will detail the research in coupling physics and data driven models for MRI acceleration. Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies.
  arXiv  Detail & Related papers  (2022-04-01T22:48:08Z)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• Deep variational network for rapid 4D flow MRI reconstruction [0.5156484100374058]
  Long in vivo scan times necessitate accelerated imaging techniques that leverage data correlations. We propose an efficient model-based deep neural reconstruction network. The network is shown to reconstruct undersampled 4D flow MRI data in under a minute on standard consumer hardware.
  arXiv  Detail & Related papers  (2020-04-20T20:17:49Z)
• 4DFlowNet: Super-Resolution 4D Flow MRI using Deep Learning and Computational Fluid Dynamics [0.0795451369160375]
  An increase in the image resolution would provide more accuracy and allow better assessment of the blood flow, especially for patients with abnormal flows. We utilized computational fluid dynamics simulations to generate synthetic 4D flow MRI data. Our novel 4DFlowNet network was trained on this synthetic 4D flow data and was capable in producing noise-free super resolution 4D flow phase images with upsample factor of 2.
  arXiv  Detail & Related papers  (2020-04-15T12:16:52Z)

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.