569-03-011 ISMRM Abstract

Spectrally-Optimized Implicit Neural Representations for Super-Resolving Cerebrovascular 4D Flow MRI

Primary: Acquisition & Reconstruction - Image Reconstruction: AI

Secondary: Neuro - Neurofluids

569-03-011 · Super-Resolving MRI: Methods and Applications · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row J

Keywords: 4D flow MRI Cerebral blood flow Super Resolution Circle of Willis Implicit Neural Representations

Accepted

Oliver Welin Odeback ¹, Nerea González-Aranceta¹, Sebastiàn Jofre¹, Javier Bisbal^2,3,4, Edward Ferdian⁵, Alistair Young⁶, Pia Callmer¹, Mia Bonini⁷, David Nordsletten⁷, Susanne Schnell⁸, Tobias Granberg^{9,10,11,12,13}, Alexander Fyrdahl^1,14, David Marlevi¹

¹Department of Molecular Medicine and Surgery, Karolinska Institutet, Solna, Sweden

²Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile

³iHEALTH, Millennium Institute for Intelligent Healthcare Engineering, Chile

⁴School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

⁵Faculty of Informatics, Telkom University, Bandung, Indonesia

⁶Kings College London, London, United Kingdom

⁷Biomedical Engineering, University of Michigan, Ann Arbor, United States of America

⁸Department of Medical Physics, University of Greifswald, Greifswald, Germany

⁹Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States of America

¹⁰Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden., Sweden

¹¹Karolinska University Hospital, Stockholm, Sweden

¹²Karolinska Institutet, Solna, Sweden

¹³Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden

¹⁴Unit of Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden

Presenting Author: Oliver Welin Odeback

Synopsis

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References

1. Markl M, Frydrychowicz A, Kozerke S, Hope M, Wieben O. 4D flow MRI. Journal of Magnetic Resonance Imaging. 2012 Nov;36(5):1015-36. https://doi.org/10.1002/jmri.23632 [doi]

2. Szajer J, Ho-Shon K. A comparison of 4D flow MRI-derived wall shear stress with computational fluid dynamics methods for intracranial aneurysms and carotid bifurcations—a review. Magnetic resonance imaging. 2018 May 1;48:62-9. https://doi.org/10.1016/j.mri.2017.12.005 [doi]

3. Ferdian E, Suinesiaputra A, Dubowitz DJ, Zhao D, Wang A, Cowan B, Young AA. 4DFlowNet: super-resolution 4D flow MRI using deep learning and computational fluid dynamics. Frontiers in Physics. 2020 May 4;8:138. https://doi.org/10.3389/fphy.2020.00138 [doi]

4. Rutkowski DR, Roldán-Alzate A, Johnson KM. Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data. Scientific reports. 2021 May 13;11(1):10240. https://doi.org/10.1038/s41598-021-89636-z [doi]

5. Kissas G, Yang Y, Hwuang E, Witschey WR, Detre JA, Perdikaris P. Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks. Computer methods in applied mechanics and engineering. 2020 Jan 1;358:112623. https://doi.org/10.1016/j.cma.2019.112623 [doi]

6. Fathi MF, Perez-Raya I, Baghaie A, Berg P, Janiga G, Arzani A, D’Souza RM. Super-resolution and denoising of 4D-flow MRI using physics-informed deep neural nets. Computer Methods and Programs in Biomedicine. 2020 Dec 1;197:105729. https://doi.org/10.1016/j.cmpb.2020.105729 [doi]

7. Sitzmann V, Martel J, Bergman A, Lindell D, Wetzstein G. Implicit neural representations with periodic activation functions. Advances in neural information processing systems. 2020;33:7462-73.

8. Molaei A, Aminimehr A, Tavakoli A, Kazerouni A, Azad B, Azad R, Merhof D. Implicit neural representation in medical imaging: A comparative survey. InProceedings of the IEEE/CVF International Conference on Computer Vision 2023 (pp. 2381-2391).

9. Saragadam V, LeJeune D, Tan J, Balakrishnan G, Veeraraghavan A, Baraniuk RG. Wire: Wavelet implicit neural representations. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 18507-18516).

10. Saitta S, Carioni M, Mukherjee S, Schönlieb CB, Redaelli A. Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI. Computer methods and programs in biomedicine. 2024 Apr 1;246:108057. https://doi.org/10.1016/j.cmpb.2024.108057 [doi]

11. Yüce G, Ortiz-Jiménez G, Besbinar B, Frossard P. A structured dictionary perspective on implicit neural representations. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022 (pp. 19228-19238).

12. Lee J, Cookson A, Roy I, Kerfoot E, Asner L, Vigueras G, Sochi T, Deparis S, Michler C, Smith NP, Nordsletten DA. Multiphysics computational modeling in CHeart. SIAM Journal on Scientific Computing. 2016;38(3):C150-78. https://doi.org/10.1137/15M1014097 [doi]

13. Odeback OW, Balasubramanian AG, Schollenberger J, Ferdiand E, Young AA, Figueroa CA, Schnell S, Tammisola O, Vinuesa R, Granberg T, Fyrdahl A. Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI. arXiv preprint arXiv:2508.14950. 2025 Aug 20. https://doi.org/10.48550/arXiv.2508.14950 [doi]

14. Ferdian E, Marlevi D, Schollenberger J, Aristova M, Edelman ER, Schnell S, Figueroa CA, Nordsletten DA, Young AA. Cerebrovascular super-resolution 4D Flow MRI–Sequential combination of resolution enhancement by deep learning and physics-informed image processing to non-invasively quantify intracranial velocity, flow, and relative pressure. Medical Image Analysis. 2023 Aug 1;88:102831. https://doi.org/10.1016/j.media.2023.102831 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/569-03-011