Synopsis
Motivation:
Goals:
Approach:
Results:
668-03-005
ISMRM Abstract
Accurate Estimation of Intravoxel Incoherent Motion Parameters Based on Implicit Neural Representation
Accepted
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 inReferences
1. D. Le Bihan, E. Breton, D. Lallemand, M. L. Aubin, J. Vignaud, and M. Laval-Jeantet, "Perfusion MR imaging of intravoxel incoherent motion: application to diffusion and perfusion in neurologic disorders," Radiology, vol. 161, pp. 401–407, 1986. doi: 10.1148/radiology.161.2.3763909 [doi]
2. D.-M. Koh, D. J. Collins, and M. R. Orton, "Intravoxel incoherent motion in body diffusion-weighted MRI: reality and challenges," American Journal of Roentgenology, vol. 196, pp. 1351–1361, 2011. doi: 10.2214/AJR.10.5515 [doi]
3. M. Iima, "Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in Oncology: Applications, Challenges, and Future Trends," Magnetic Resonance in Medicine and Science, vol. 20, pp. 125–138, 2021. doi: 10.2463/mrms.rev.2019-0124 [doi]
4. R. Klaassen, O. J. Gurney-Champion, M. R. W. Engelbrecht, et al., "Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients," PloS one, vol. 13, e0194590, 2018. doi: 10.1371/journal.pone.0194590 [doi]
5. T. F. Coleman and Y. Li, "An interior trust region approach for nonlinear minimization subject to bounds," SIAM Journal on optimization, vol. 6, pp. 418–445, 1996. doi: 10.1137/0806023 [doi]
6. M. P. T. Kaandorp, S. Barbieri, R. Klaassen, et al., "Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients," Magnetic resonance in medicine, vol. 86, pp. 2250–2265, 2021. doi: 10.1002/mrm.28852 [doi]
7. H.-M. Huang, "An unsupervised convolutional neural network method for estimation of intravoxel incoherent motion parameters," Physics in Medicine & Biology, vol. 67, 215018, 2022. doi: 10.1088/1361-6560/ac9a1f [doi]
8. R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, "Convolutional neural networks: an overview and application in radiology," Insights into Imaging, vol. 9, pp. 611–629, 2018. doi: 10.1007/s13244-018-0639-9 [doi]
9. B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, "NeRF: Representing scenes as neural radiance fields for view synthesis," Communications of the ACM, vol. 65, pp. 99–106, 2021. doi: 10.1145/3503250 [doi]
10. V. Sitzmann, J. Martel, A. Bergman, D. Lindell, and G. Wetzstein, "Implicit neural representations with periodic activation functions," Advances in Neural Information Processing Systems, vol. 33, pp. 7462–7473, 2020. doi: 10.48550/arXiv.2006.09661 [doi]
11. Y.-P. Liao, S.-I. Urayama, T. Isa, and H. Fukuyama, "Optimal model mapping for intravoxel incoherent motion MRI," Frontiers in Human Neuroscience, vol. 15, p. 617152, 2021. doi: 10.3389/fnhum.2021.617152 [doi]
12. C. Federau, K. O'Brien, R. Meuli, P. Hagmann, and P. Maeder, "Measuring brain perfusion with intravoxel incoherent motion (IVIM): initial clinical experience," Journal of Magnetic Resonance Imaging, vol. 39, no. 3, pp. 624–632, 2014. doi: 10.1002/jmri.24195 [doi]
13. X. Hu, Y. Dai, A. O. Ezzati, J. Lee, J. Deng, and X. Jia, "Report on the quantitative intra-voxel incoherent motion diffusion MRI reconstruction grand challenge," Medical Physics, vol. 52, no. 7, e17998, 2025. doi: 10.1002/mp.17998 [doi]
Cite this abstract
http://echo.ismrm.org/p/ISMRM2026/668-03-005