Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
369-02-003 ISMRM Abstract

Classification of Parkinsonian Disorders: An Integrated Quantitative MRI Model

Accepted
Septian Hartono 1,2, Chang Liu1,3, Qicong Sun1, Habriyah Ulfah4, Devanshi Patidar2, Poh Choo Seow1, Weiling Lee1, Qiqi Lyu1, Robert Chen1,2,5, Eng King Tan1,2,5, Ling Ling Chan1,2,5
1Singapore General Hospital, Singapore, Singapore
2Duke-NUS Medical School, Singapore, Singapore
3The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
4National University of Singapore, Singapore, Singapore
5National Neuroscience Institute, Singapore, Singapore
Presenting Author: Septian Hartono

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. Beach TG, Adler CH. Importance of low diagnostic accuracy for early Parkinson's disease. Mov Disord. 2018;33(10):1551–1554. doi:10.1002/mds.27485. PMID:30288780; PMCID:PMC6544441. [doi] [pmid]
2. Ali K, Morris HR. Parkinson's disease: chameleons and mimics. Pract Neurol. 2015;15(1):14–25. doi:10.1136/practneurol-2014-000849. PMID:25253895. [doi] [pmid]
3. Tolosa E, Garrido A, Scholz SW, Poewe W. Challenges in the diagnosis of Parkinson's disease. Lancet Neurol. 2021;20(5):385–397. doi:10.1016/S1474-4422(21)00030-2. PMID:33894193; PMCID:PMC8185633. [doi] [pmid]
4. Chau MT, Todd G, Wilcox R, et al. Diagnostic accuracy of the appearance of Nigrosome-1 on magnetic resonance imaging in Parkinson's disease: A systematic review and meta-analysis. Parkinsonism Related Disord. 2020;78:12–20. doi:10.1016/j.parkreldis.2020.07.002. PMID:32668370. [doi] [pmid]
5. Welton, T, Hartono S, Lee WL, et al. Classification of Parkinson's disease by deep learning on midbrain MRI. Front Aging Neurosci. 2024;16:1425095. doi:10.3389/fnagi.2024.1425095. PMID:39228827; PMCID:PMC11369979. [doi] [pmid]
6. Vaillancourt DE, Barmpoutis A, Wu SS, et al. Automated imaging differentiation for Parkinsonism. JAMA Neurol. 2025;82(5):495–505. doi:10.1001/jamaneurol.2025.0112. PMID:40094699; PMCID:PMC11915115. [doi] [pmid]
7. Hartono S, Chen RC, Welton T, et al. Quantitative iron-neuromelanin MRI associates with motor severity in Parkinson's disease and matches radiological disease classification. Front Aging Neurosci. 2023;15:1287917. doi:10.3389/fnagi.2023.1287917. PMID:38090717; PMCID:PMC10711072. [doi] [pmid]
8. Nam Y, Gho SM, Kim DH, et al. Imaging of nigrosome 1 in substantia nigra at 3T using multiecho susceptibility map-weighted imaging (SMWI). J Magn Reson Imaging. 2017;46:528–536. doi:10.1002/jmri.25553. PMID:27859983. [doi] [pmid]
9. Rahman MM, Marculescu R. Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation. arXiv preprint. 2023;2303.16892. doi:10.48550/arXiv.2303.16892. [doi]
10. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23 Suppl 1:S208–219. doi:10.1016/j.neuroimage.2004.07.051. PMID:15501092. [doi] [pmid]
11. Jenkinson M, Beckmann CF, Behrens TEJ, et al. FSL. Neuroimage. 2012;62(2):782–790. doi:10.1016/j.neuroimage.2011.09.015. PMID:21979382. [doi] [pmid]
12. Bastiani M, Cottaar M, Fitzgibbon SP, et al. Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. Neuroimage. 2019;184:801–812. doi:10.1016/j.neuroimage.2018.09.073. PMID:30267859; PMCID:PMC6264528. [doi] [pmid]
13. Pasternak O, Sochen N, Gur Y, et al. Free water elimination and mapping from diffusion MRI. Magn Reson Med.2009;62(3):717–730. doi:10.1002/mrm.22055. PMID:19623619. [doi] [pmid]
14. Lu H, Kashani AH, Arfanakis K, et al. MarkVCID cerebral small vessel consortium: II. Neuroimaging protocols. Alzheimers Dement. 2021;17(4):716–725. doi:10.1002/alz.12216. PMID:33480157; PMCID:PMC8627001. [doi] [pmid]
15. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17(2):825–841. doi:10.1016/s1053-8119(02)91132-8. PMID:12377157. [doi] [pmid]
16. Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage.2009;48(1):63–72. doi:10.1016/j.neuroimage.2009.06.060. PMID:19573611; PMCID:PMC2733527. [doi] [pmid]
17. Andersson JLR, Jenkinson M, Smith S. Non-linear registration, aka spatial normalisation. FMRIB Technical Report TR07JA2. 2010.
18. Pauli WM, Nili AN, Tyszka JM. A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei. Sci Data. 2018;5:180063. doi:10.1038/sdata.2018.63. PMID:29664465; PMCID:PMC5903366. [doi] [pmid]
19. Diedrichsen J. A spatially unbiased atlas template of the human cerebellum. Neuroimage. 2006;33(1):127–138. doi:10.1016/j.neuroimage.2006.05.056. PMID:16904911. [doi] [pmid]
20. Diedrichsen J, Balsters JH, Flavell J, et al. A probabilistic MR atlas of the human cerebellum. Neuroimage. 2009;46(1):39–46. doi:10.1016/j.neuroimage.2009.01.045. PMID:19457380. [doi] [pmid]
21. Diedrichsen J, Maderwald S, Küper M, et al. Imaging the deep cerebellar nuclei: a probabilistic atlas and normalization procedure. Neuroimage. 2011;54(3):1786–1794. doi:10.1016/j.neuroimage.2010.10.035. PMID:20965257. [doi] [pmid]

Cite this abstract