Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
668-03-003 ISMRM Abstract

VASAL: A Vascular Substrate Algorithm to Generate Microvascular Network Phantoms

Accepted
Elizabeth Powell 1,2, Geoff J Parker1,2,3, Marco Palombo4,5
1Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
2UCL Hawkes Institute, University College London, London, United Kingdom
3Bioxydyn Limited, Manchester, United Kingdom
4School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
5Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
Presenting Author: Elizabeth Powell

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. J Guyon, C Chapouly, L Andrique, A Bikfalvi, and T Daubon. “The Normal and Brain Tumor Vasculature: Morphological and Functional Characteristics and Therapeutic Targeting”. In: Frontiers in Physiology (2021). doi: 10.3389/fphys.2021.622615. [doi]
2. D Kuriakose and Z Xiao. “Pathophysiology and Treatment of Stroke: Present Status and Future Perspectives”. In: International Journal of Molecular Sciences (2020). doi: 10.3390/ijms21207609. [doi]
3. B Hussain, C Fang, and J Chang. “Blood–Brain Barrier Breakdown: An Emerging Biomarker of Cognitive Impairment in Normal Aging and Dementia”. In: Frontiers in Neuroscience (2021). doi: 10.3389/fnins.2021.688090. [doi]
4. D Le Bihan, E Breton, D Lallemand, ML Aubin, J. Vignaud, and M Laval-Jeantet. “Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging.” In: Radiology (1988). doi: 10.1148/radiology.168.2.3393671. [doi]
5. E Panagiotaki, S Walker-Samuel, B Siow, SP Johnson, V Rajkumar, RB Pedley, MF Lythgoe, and DC Alexander. “Noninvasive quantification of solid tumor microstructure using VERDICT MRI”. In: Cancer Research (2014). doi: 10.1158/0008-5472.CAN-13-2511. [doi]
6. S Lasic, M Nilsson, J Latt, F Stahlberg, and D Topgaard. “Apparent exchange rate mapping with diffusion MRI”. In: Magnetic Resonance in Medicine (2011). doi: 10.1002/mrm.22782. [doi]
7. R Bai, Z Li, C Sun, YC Hsu, H Liang, and P Basser. “Feasibility of filter-exchange imaging (FEXI) in measuring different exchange processes in human brain”. In: NeuroImage (2020). doi: 10.1016/j.neuroimage.2020.117039. [doi]
8. E Powell, Y Ohene, M Battiston, BR Dickie, LM Parkes, and GJM Parker. “Blood-brain barrier water exchange measurements using FEXI: Impact of modeling paradigm and relaxation time effects”. In: Magnetic Resonance in Medicine (2023). doi: 10.1002/mrm.29616. [doi]
9. TG Close, J-D Tournier, F Calamante, LA Johnston, I Mareels, and A Connelly. “A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms”. In: NeuroImage (2009). doi: 10.1016/j.neuroimage.2009.03.077. [doi]
10. PF Neher, FB Laun, B Stieltjes, and KH Maier-Hein. “Fiberfox: Facilitating the creation of realistic white matter software phantoms”. In: Magnetic Resonance in Medicine (2014). doi: 10.1002/mrm.25045. [doi]
11. K Ginsburger, F Matuschke, F Poupon, J-F Mangin, M Axer, and C Poupon. “MEDUSA: A GPU-based tool to create realistic phantoms of the brain microstructure using tiny spheres”. In: NeuroImage (2019). doi: 10.1016/j.neuroimage.2019.02.055. [doi]
12. R Callaghan, DC Alexander, M Palombo, and H Zhang. “ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation”. In: NeuroImage (2020). doi: 10.1016/j.neuroimage.2020.117107. [doi]
13. JL Villarreal-Haro, R Gardier, EJ Canales-Rodriguez, E Fischi-Gomez, G Girard, J-P Thiran, and J Rafael-Patino. “CACTUS: a computational framework for generating realistic white matter microstructure substrates”. In: Frontiers in Neuroinformatics (2023). doi: 10.3389/fninf.2023.1208073. [doi]
14. Marco Palombo, DC Alexander, and H Zhang. “A generative model of realistic brain cells with application to numerical simulation of the diffusion-weighted MR signal”. In: NeuroImage (2019). doi: 10.1016/j.neuroimage.2018.12.025. [doi]
15. A Ianus, DC Alexander, H Zhang, and M Palombo. “Mapping complex cell morphology in the grey matter with double diffusion encoding MR: A simulation study”. In: NeuroImage (2021). doi: 10.1016/j.neuroimage.2021.118424. [doi]
16. A Linninger, G Hartung, S Badr, and R Morley. “Mathematical synthesis of the cortical circulation for the whole mouse brain-part I. theory and image integration”. In: Computers in Biology and Medicine (2019). doi: 10.1016/j.compbiomed.2019.05.004. [doi]
17. RM Henkelman, JJ Neil, and Q-S Xiang. “A quantitative interpretation of IVIM measurements of vascular perfusion in the rat brain”. In: Magnetic Resonance in Medicine (1994). doi: 10.1002/mrm.1910320407. [doi]
18. LA Scott, BR Dickie, SD Rawson, G Coutts, TL Burnett, SM Allan, GJM Parker, and LM Parkes. “Characterisation of microvessel blood velocity and segment length in the brain using multi-diffusion-time diffusion-weighted MRI”. In: Journal of Cerebral Blood Flow and Metabolism (2021). doi: 10.1177/0271678X20978523. [doi]
19. MG Hall and DC Alexander. “Convergence and Parameter Choice for Monte Carlo Simulations of Diffusion MRI”. In: IEEE Transactions on Medical Imaging (2009). doi: 10.1109/TMI.2009.2015756. [doi]
20. DA Fedosov, B Caswell, AS Popel, and GE Karniadakis. “Blood Flow and Cell-Free Layer in Microvessels”. In: Microcirculation (2010). doi: 10.1111/j.1549-8719.2010.00056.x. [doi]
21. R Neto Henriques, SN Jespersen, and N Shemesh. “Microscopic anisotropy misestimation in spherical-mean single diffusion encoding MRI”. In: Magnetic Resonance in Medicine (2019). doi: 10.1002/mrm.27606. [doi]
22. G Fournet, JR Li, AM Cerjanic, BP Sutton, L Ciobanu, and D Le Bihan. “A two-pool model to describe the IVIM cerebral perfusion”. In: Journal of Cerebral Blood Flow and Metabolism (2017). doi: 10.1177/0271678X16681310. [doi]
23. D Wu and J Zhang. “Evidence of the diffusion time dependence of intravoxel incoherent motion in the brain”. In: Magnetic Resonance in Medicine (2019). doi: 10.1002/mrm.27879. [doi]
24. RP Kennan, J-H Gao, J Zhong, and JC Gore. “A general model of microcirculatory blood flow effects in gradient sensitized MRI”. In: Medical Physics (1994). doi: 10.1118/1.597170. [doi]
25. A Wetscherek, B Stieltjes, and FB Laun. “Flow-compensated intravoxel incoherent motion diffusion imaging”. In: Magnetic Resonance in Medicine (2015). doi: 10.1002/mrm.25410. [doi]

Cite this abstract