mlsanityData to 14 Oct 2026

Localized FNO for Spatiotemporal Hemodynamic Upsampling in Aneurysm MRI

arXiv 2507.13789 ↗First posted 18 Jul 2025cs.CV cs.AI physics.comp-phcited by 2

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Abstract (arXiv)

Hemodynamic analysis is essential for predicting aneurysm rupture and guiding treatment. While magnetic resonance flow imaging enables time-resolved volumetric blood velocity measurements, its low spatiotemporal resolution and signal-to-noise ratio limit its diagnostic utility. To address this, we propose the Localized Fourier Neural Operator (LoFNO), a novel 3D architecture that enhances both spatial and temporal resolution with the ability to predict wall shear stress (WSS) directly from clinical imaging data. LoFNO integrates Laplacian eigenvectors as geometric priors for improved structural awareness on irregular, unseen geometries and employs an Enhanced Deep Super-Resolution Network (EDSR) layer for robust upsampling. By combining geometric priors with neural operator frameworks, LoFNO de-noises and spatiotemporally upsamples flow data, achieving superior velocity and WSS predictions compared to interpolation and alternative deep learning methods, enabling more precise cerebrovascular diagnostics.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 18 Jul 2025.

Builds on

15 of 30 references.

References · 15

  1. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
  2. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  3. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
  4. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
  5. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
  6. 2021Physics-informed neural networks (PINNs) for fluid mechanics: A reviewcited by 473
  7. 2022Fourier Neural Operator with Learned Deformations for PDEs on General Geometriescited by 427
  8. 2019Towards Physics-informed Deep Learning for Turbulent Flow Predictioncited by 204
  9. 2022Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modelingcited by 65
  10. 2023Super-Resolution Neural Operatorcited by 49
  11. 2022Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamicscited by 44
  12. 2023Domain Agnostic Fourier Neural Operatorscited by 38
3 more
  1. 2023Canonical normalizing flows for manifold learningcited by 15
  2. 2024Explicit and data-Efficient Encoding via Gradient Flowcited by 3
  3. 2024Energy-Based Prior Latent Space Diffusion model for Reconstruction of Lumbar Vertebrae from Thick Slice MRIcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Operator Flow Matching for Timeseries Forecastingcited by 1
  2. 2026Parameter-Efficient Adaptation of Pretrained Language Models for Time-Series Forecastingcited by 0