Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
365-03-011 ISMRM Abstract

Enhanced Intravoxel Incoherent Motion three-compartmental modeling using fusion neural network

Primary: Diffusion - IVIM
Secondary: Acquisition & Reconstruction - AI methods
365-03-011 · Body Diffusion MRI Methods · Monday, 11 May, 1:50 PM–2:45 PM · Digital Posters Row F
Keywords: AI/ML Image Reconstruction Machine Learning/Artificial Intelligence Deep learning reconstruction Intravoxel incoherent motion (IVIM) Brain perfusion
Accepted
Nhat Hoang1, Abrar Faiyaz2, Emmanuel A Mensah 3, Md Nasir Uddin2,3,4, Jianhui Zhong1,5, Giovanni Schifitto2,4,5
1Physics, University of Rochester, Rochester, United States of America
2Neurology, University of Rochester, Rochester, United States of America
3Biomedical Engineering, University of Rochester, Rochester, United States of America
4Electrical and Computer Engineering, University of Rochester, Rochester, United States of America
5Department of Imaging Sciences, University of Rochester, Rochester, United States of America
Presenting Author: Emmanuel A Mensah

Synopsis

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References

1. Le Bihan D. What can we see with IVIM MRI? NeuroImage. 2019;187:56-67. doi: 10.1016/j.neuroimage.2017.12.062. [doi]
2. Thiel MM, Freeze WM, Jong JJ, Ramakers IHGB, Backes WH, Jansen JFA. Interstitial fluid as a proxy of glymphatic dysfunction in patients with cognitive impairment: The necessity of three-directional intravoxel incoherent motion. Alzheimer's & Dementia. 2021;17(S4):e052105. doi: 10.1002/alz.052105. [doi]
3. van der Thiel MM, Freeze WM, Verheggen ICM, Wong SM, de Jong JJA, Postma AA, Hoff EI, Gronenschild EHBM, Verhey FR, Jacobs HIL, Ramakers IHGB, Backes WH, Jansen JFA. Associations of increased interstitial fluid with vascular and neurodegenerative abnormalities in a memory clinic sample. Neurobiology of Aging. 2021;106:257-67. doi: 10.1016/j.neurobiolaging.2021.06.017. [doi]
4. van der Knaap N, Klinkhammer S, Postma AA, Slooter AJC, Horn J, van Heugten CM, Voorter PHM, van der Thiel MM, Drenthen GS, Backes WH. Assessing microstructural and microvascular abnormalities in hospitalized COVID-19 patients using intravoxel incoherent motion imaging.
5. Voorter PHM, Backes WH, Gurney-Champion OJ, Wong S-M, Staals J, Oostenbrugge RJv, Thiel MMvd, Jansen JFA, Drenthen GS. Improving microstructural integrity, interstitial fluid, and blood microcirculation images from multi‐b‐value diffusion MRI using physics‐informed neural networks in cerebrovascular disease. Magnetic Resonance in Medicine. 2023/10/01;90(4). doi: 10.1002/mrm.29753. [doi]
6. S.K.B S, Mathivanan SK, Karthikeyan P, Rajadurai H, Shivahare BD, Mallik S, Qin H. An enhanced multimodal fusion deep learning neural network for lung cancer classification. Systems and Soft Computing. 2024;6:200068. doi: 10.1016/j.sasc.2023.200068. [doi]
7. Lee YC, Cha J, Shim I, Park W-Y, Kang SW, Lim DH, Won H-H. Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction. npj Digital Medicine. 2023;6(1). doi: 10.1038/s41746-023-00748-4. [doi]
8. Kaandorp MPT, Zijlstra F, Karimi D, Gholipour A, While PT. Incorporating spatial information in deep learning parameter estimation with application to the intravoxel incoherent motion model in diffusion-weighted MRI. Medical Image Analysis. 2025;101:103414. doi: 10.1016/j.media.2024.103414. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/365-03-011