Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
369-04-001 ISMRM Abstract

Quantitative Analysis of Vascular Characteristics in Post-Mortem Human Midbrain T2*w MRI

Accepted
Marshall Xu1, Kevin R Sitek2, Omer Faruk Gulban3,4, Saskia Bollmann 5
1School of Electrical Engineering and Computer Science, The University of Queesland, Brisbane, Australia
2Communication Sciences & Disorders, Biomedical Engineering, Northwestern University, Chicago, United States of America
3Brain innovation B.V., Maastricht, Netherlands
4Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
5The University of Queensland, Brisbane, Australia
Presenting Author: Saskia Bollmann

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. Sitek, K. R., Gulban, O. F., Calabrese, E., Johnson, G. A., Lage-Castellanos, A., Moerel, M., Ghosh, S. S., & De Martino, F. (2019). Mapping the human subcortical auditory system using histology, postmortem MRI and in vivo MRI at 7T. eLife, 8, e48932. https://doi.org/10.7554/eLife.48932 [doi]
2. Calabrese, E., Hickey, P., Hulette, C., Zhang, J., Parente, B., Lad, S. P., & Johnson, G. A. (2015). Postmortem diffusion MRI of the human brainstem and thalamus for deep brain stimulator electrode localization: Postmortem Diffusion MRI for DBS Electrode Localization. Human Brain Mapping, 36(8), 3167–3178. https://doi.org/10.1002/hbm.22836 [doi]
3. Tustison, N. J., Avants, B. B., Cook, P. A., Yuanjie Zheng, Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 29(6), 1310–1320. https://doi.org/10.1109/TMI.2010.2046908 [doi]
4. Tustison, N. J., Cook, P. A., Holbrook, A. J., Johnson, H. J., Muschelli, J., Devenyi, G. A., Duda, J. T., Das, S. R., Cullen, N. C., Gillen, D. L., Yassa, M. A., Stone, J. R., Gee, J. C., & Avants, B. B. (2021). The ANTsX ecosystem for quantitative biological and medical imaging. Scientific Reports, 11(1), 9068. https://doi.org/10.1038/s41598-021-87564-6 [doi]
5. Xu, M., Ribeiro, F. L., Barth, M., Bernier, M., Bollmann, S., Chatterjee, S., Cognolato, F., Gulban, O. F., Itkyal, V., Liu, S., Mattern, H., Polimeni, J. R., Shaw, T. B., Speck, O., & Bollmann, S. (2024). VesselBoost: A Python Toolbox for Small Blood Vessel Segmentation in Human Magnetic Resonance Angiography Data. Aperture Neuro, 4. https://doi.org/10.52294/001c.123217 [doi]
6. Hanke, M., Baumgartner, F. J., Ibe, P., Kaule, F. R., Pollmann, S., Speck, O., Zinke, W., & Stadler, J. (2014). A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie. Scientific Data, 1(1), 140003. https://doi.org/10.1038/sdata.2014.3 [doi]
7. Adil, S. M., Calabrese, E., Charalambous, L. T., Cook, J. J., Rahimpour, S., Atik, A. F., Cofer, G. P., Parente, B. A., Johnson, G. A., Lad, S. P., & White, L. E. (2021). A high-resolution interactive atlas of the human brainstem using magnetic resonance imaging. NeuroImage, 237, 118135. https://doi.org/10.1016/j.neuroimage.2021.118135 [doi]
8. Lee, T. C., Kashyap, R. L., & Chu, C. N. (1994). Building Skeleton Models via 3-D Medial Surface Axis Thinning Algorithms. CVGIP: Graphical Models and Image Processing, 56(6), 462–478. https://doi.org/10.1006/cgip.1994.1042 [doi]
9. Jeppesen, N., Mikkelsen, L. P., Dahl, A. B., Christensen, A. N., & Dahl, V. A. (2021). Quantifying effects of manufacturing methods on fiber orientation in unidirectional composites using structure tensor analysis. Composites Part A: Applied Science and Manufacturing, 149, 106541. https://doi.org/10.1016/j.compositesa.2021.106541 [doi]
10. Maurer, C. R., Rensheng Qi, & Raghavan, V. (2003). A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2), 265–270. https://doi.org/10.1109/TPAMI.2003.1177156 [doi]
11. Meyer-Spradow, J., Ropinski, T., Mensmann, J., & Hinrichs, K. (2009). Voreen: A Rapid-Prototyping Environment for Ray-Casting-Based Volume Visualizations. IEEE Computer Graphics and Applications, 29(6), 6–13. https://doi.org/10.1109/MCG.2009.130 [doi]
12. Drees, D., Scherzinger, A., Hägerling, R., Kiefer, F., & Jiang, X. (2021). Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets. BMC Bioinformatics, 22(1), 346. https://doi.org/10.1186/s12859-021-04262-w [doi]
13. Duvernoy, H. M. (1999). Human Brain Stem Vessels. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-07813-6 [doi]
14. Gagnon, L., Sakadžić, S., Lesage, F., Musacchia, J. J., Lefebvre, J., Fang, Q., Yücel, M. A., Evans, K. C., Mandeville, E. T., Cohen-Adad, J., Polimeni, J. R., Yaseen, M. A., Lo, E. H., Greve, D. N., Buxton, R. B., Dale, A. M., Devor, A., & Boas, D. A. (2015). Quantifying the Microvascular Origin of BOLD-fMRI from First Principles with Two-Photon Microscopy and an Oxygen-Sensitive Nanoprobe. The Journal of Neuroscience, 35(8), 3663–3675. https://doi.org/10.1523/JNEUROSCI.3555-14.2015 [doi]
15. Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.-C., Pujol, S., Bauer, C., Jennings, D., Fennessy, F., Sonka, M., Buatti, J., Aylward, S., Miller, J. V., Pieper, S., & Kikinis, R. (2012). 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magnetic Resonance Imaging, 30(9), 1323–1341. https://doi.org/10.1016/j.mri.2012.05.001 [doi]

Cite this abstract