Marco Pizzolato 1,2, Thina Lundsgaard Thøgersen1,2, Mario Corral Bolaños1,2, Tim B Dyrby1,2
1Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
2Department of Radiology and Nuclear Medicine - Amager and Hvidovre, Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Copenhagen, Denmark
Presenting Author: Marco Pizzolato
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.
1. Jensen, J. H., Glenn, G. R., & Helpern, J. A. (2016). Fiber ball imaging. Neuroimage, 124, 824-833. https://doi.org/10.1016/j.neuroimage.2015.09.049 [doi]
2. Barakovic, M., Pizzolato, M., Tax, C. M., Rudrapatna, U., Magon, S., Dyrby, T. B., ... & Canales-Rodríguez, E. J. (2023). Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology. Frontiers in neuroscience, 17, 1209521. https://doi.org/10.3389/fnins.2023.1209521 [doi]
3. Canales‐Rodríguez, E. J., Pizzolato, M., Zhou, F. L., Barakovic, M., Thiran, J. P., Jones, D. K., ... & Dyrby, T. B. (2024). Pore size estimation in axon‐mimicking microfibers with diffusion‐relaxation MRI. Magnetic resonance in medicine, 91(6), 2579-2596. https://doi.org/10.1002/mrm.29991 [doi]
4. Anderson, A. W. (2005). Measurement of fiber orientation distributions using high angular resolution diffusion imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 54(5), 1194-1206. https://doi.org/10.1002/mrm.20667 [doi]
5. Pizzolato, M., Canales-Rodríguez, E. J., Andersson, M., & Dyrby, T. B. (2023). Axial and radial axonal diffusivities and radii from single encoding strongly diffusion-weighted MRI. Medical Image Analysis, 86, 102767. https://doi.org/10.1016/j.media.2023.102767 [doi]
6. Pizzolato, M., Andersson, M., Canales-Rodríguez, E. J., Thiran, J. P., & Dyrby, T. B. (2022). Axonal T2 estimation using the spherical variance of the strongly diffusion-weighted MRI signal. Magnetic resonance imaging, 86, 118-134. https://doi.org/10.1016/j.mri.2021.11.012 [doi]
7. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324 [doi]
8. Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine learning, 63(1), 3-42. https://doi.org/10.1007/s10994-006-6226-1 [doi]
9. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830. https://doi.org/10.5555/1953048.2078195 [doi]
10. Ma, X., Uğurbil, K., & Wu, X. (2020). Denoise magnitude diffusion magnetic resonance images via variance-stabilizing transformation and optimal singular-value manipulation. Neuroimage, 215, 116852. https://doi.org/10.1016/j.neuroimage.2020.116852 [doi]
11. Foi, A. (2011, March). Noise estimation and removal in MR imaging: The variance-stabilization approach. In 2011 IEEE International symposium on biomedical imaging: from nano to macro (pp. 1809-1814). IEEE. https://doi.org/10.1109/ISBI.2011.5872758 [doi]
12. Gavish, M., & Donoho, D. L. (2017). Optimal shrinkage of singular values. IEEE Transactions on Information Theory, 63(4), 2137-2152. https://doi.org/10.1109/TIT.2017.2653801 [doi]
13. Kellner, E., Dhital, B., Kiselev, V. G., & Reisert, M. (2016). Gibbs‐ringing artifact removal based on local subvoxel‐shifts. Magnetic resonance in medicine, 76(5), 1574-1581. https://doi.org/10.1002/mrm.26054 [doi]
14. Andersson, J. L., & Sotiropoulos, S. N. (2016). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage, 125, 1063-1078. https://doi.org/10.1016/j.neuroimage.2015.10.019 [doi]
15. Stejskal, E. O., & Tanner, J. E. (1965). Spin diffusion measurements: spin echoes in the presence of a time‐dependent field gradient. The journal of chemical physics, 42(1), 288-292. https://doi.org/10.1063/1.1695690 [doi]