mlsanityData to 7 Oct 2026

Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI

arXiv 2302.12835 ↗First posted 24 Feb 2023eess.IV cs.CV cs.LGcited by 9

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Abstract (arXiv)

4D flow MRI is a non-invasive imaging method that can measure blood flow velocities over time. However, the velocity fields detected by this technique have limitations due to low resolution and measurement noise. Coordinate-based neural networks have been researched to improve accuracy, with SIRENs being suitable for super-resolution tasks. Our study investigates SIRENs for time-varying 3-directional velocity fields measured in the aorta by 4D flow MRI, achieving denoising and super-resolution. 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. Our approach is quick to execute and straightforward to implement for novel cases, achieving 4D super-resolution.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 13 of its 82 references that are in the map. First posted on arXiv 24 Feb 2023.

Builds on

13 of 82 references.

References · 13

  1. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  2. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
  3. 2018On the Spectral Bias of Neural Networkscited by 1,321
  4. 2017Deep Image Priorcited by 1,097
  5. 2020Learned Initializations for Optimizing Coordinate-Based Neural Representationscited by 240
  6. 2020Frequency Bias in Neural Networks for Input of Non-Uniform Densitycited by 140
  7. 2019Reconstructing continuous distributions of 3D protein structure from cryo-EM imagescited by 56
  8. 20204DFlowNet: Super-Resolution 4D Flow MRI using Deep Learning and Computational Fluid Dynamicscited by 18
  9. 2022Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modelingcited by 16
  10. 2020Deep variational network for rapid 4D flow MRI reconstructioncited by 11
  11. 2022Physics-informed compressed sensing for PC-MRI: an inverse Navier-Stokes problemcited by 6
  12. 2022Joint reconstruction and segmentation of noisy velocity images as an inverse Navier-Stokes problemcited by 5
1 more
  1. 2022Data-driven generation of 4D velocity profiles in the aneurysmal ascending aortacited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 5; 2026: 2.

9 papers.

  1. 2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
  2. 2023Generalized super-resolution 4D Flow MRI unicodex2013 using ensemble learning to extend across the cardiovascular systemcited by 3
  3. 2025Enhanced Vascular Flow Simulations in Aortic Aneurysm via Physics-Informed Neural Networks and Deep Operator Networkscited by 3
  4. 2025Data Denoising and Derivative Estimation for Data-Driven Modeling of Nonlinear Dynamical Systemscited by 1
  5. 2025On the accuracy of implicit neural representations for cardiovascular anatomies and hemodynamic fieldscited by 1
  6. 2024Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regressioncited by 0
  7. 2025Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRIcited by 0
  8. 2026Unsupervised 4D Flow MRI Velocity Enhancement and Unwrapping Using Divergence-Free Neural Networkscited by 0
  9. 2026Attention-guided super-resolution of 4D flow MRI in carotid arteriescited by 0