Localized FNO for Spatiotemporal Hemodynamic Upsampling in Aneurysm MRI

• URL: http://arxiv.org/abs/2507.13789v1
• Date: Fri, 18 Jul 2025 10:00:38 GMT
• Title: Localized FNO for Spatiotemporal Hemodynamic Upsampling in Aneurysm MRI
• Authors: Kyriakos Flouris, Moritz Halter, Yolanne Y. R. Lee, Samuel Castonguay, Luuk Jacobs, Pietro Dirix, Jonathan Nestmann, Sebastian Kozerke, Ender Konukoglu,
• Abstract summary: 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.
• Score: 7.787686784329426
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• 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)
• Input layer regularization and automated regularization hyperparameter tuning for myelin water estimation using deep learning [1.9594393134885413]
  We propose a novel deep learning method which combines classical regularization with data augmentation for estimating myelin water fraction (MWF) in the brain via biexponential analysis.<n>In particular, we study the biexponential model, one of the signal models used for MWF estimation.
  arXiv  Detail & Related papers  (2025-01-30T00:56:28Z)
• 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)
• Temporal Dynamic Synchronous Functional Brain Network for Schizophrenia Diagnosis and Lateralization Analysis [8.280225660612862]
  The study was validated on COBRE and UCLA datasets and achieved 83.62% and 89.71% average accuracies. Interestingly, this study showed that the lower order perceptual system and higher order network regions in the left hemisphere are more severely dysfunctional than in the right hemisphere in SZ.
  arXiv  Detail & Related papers  (2023-03-31T02:54:01Z)
• Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model [0.0]
  We present an architecture that is tailored to predict high-resolution (spatial and temporal) velocity fields for complex synthetic vascular geometries. Compared to CFD simulations, the velocity field can be estimated with a mean absolute error of 0.024 m/s, whereas the run time reduces from several hours on a high-performance cluster to a few seconds on a consumer graphical processing unit.
  arXiv  Detail & Related papers  (2023-02-13T17:56:00Z)
• Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networks [46.751292014516025]
  Deep neural networks have been applied to reconstruct undersampled k-space data and have shown improved reconstruction performance. This work proposes a novel framework to learn k-space sampling trajectories by considering it as an Ordinary Differential Equation (ODE) problem. Experiments were conducted on different in-viv datasets (textite.g., brain and knee images) acquired with different sequences.
  arXiv  Detail & Related papers  (2022-04-05T20:28:42Z)
• 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)
• Physics-informed neural networks for improving cerebral hemodynamics predictions [0.0]
  In this work, we put forth a physics-informed deep learning framework that augments sparse clinical measurements with fast computational fluid dynamics simulations. Our framework employs in-vivo real-time TCD velocity measurements at several locations in the brain and the baseline vessel cross-sectional areas acquired from 3D images. We validated the predictions of our model against in-vivo velocity measurements obtained via 4D MRI scans.
  arXiv  Detail & Related papers  (2021-08-25T22:19:41Z)
• Influence Estimation and Maximization via Neural Mean-Field Dynamics [60.91291234832546]
  We propose a novel learning framework using neural mean-field (NMF) dynamics for inference and estimation problems. Our framework can simultaneously learn the structure of the diffusion network and the evolution of node infection probabilities.
  arXiv  Detail & Related papers  (2021-06-03T00:02:05Z)
• 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)
• Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso [102.84915019938413]
  Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques. The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge. We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
  arXiv  Detail & Related papers  (2020-09-29T21:17:16Z)
• Towards learned optimal q-space sampling in diffusion MRI [1.5640063295947522]
  We propose a unified estimation framework for fiber tractography. The proposed solution offers substantial improvements in the quality of signal estimation as well as the accuracy of ensuing analysis. We present a comprehensive comparative analysis based on the Human Connectome Project data.
  arXiv  Detail & Related papers  (2020-09-07T10:46:12Z)

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.