363-01-016 ISMRM Abstract

Deep Learning-Based Automatic Quantification of Venous Engorgement in Spontaneous Intracranial Hypotension

Primary: Analysis Methods - Segmentation and Detection

Secondary: Neuro - Neurofluids

363-01-016 · Neurological Application of Advanced Diffusion and Quantitative MRI · Monday, 11 May, 8:20 AM–9:15 AM · Digital Posters Row D

Keywords: Deep learning Automated segmentation Quantitative evaluation Spontaneous intracranial hypotension

Accepted

Po-Hsun Su¹, Yen-Feng Wang^2,3,4, Pei-Yun Wu¹, Shuu-Jiun Wang^2,3,4, Chia-Feng Lu ¹

¹Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan

²School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

³Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan

⁴Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan

Presenting Author: Chia-Feng Lu

Synopsis

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References

1. Bond, K., et al., Spontaneous intracranial hypotension: atypical radiologic appearances, imaging mimickers, and clinical look-alikes. American Journal of Neuroradiology, 2020. 41(8): p. 1339-1347. [PMID: 32646948] [pmid]

2. Ferrante, E., M. Trimboli, and F. Rubino, Spontaneous intracranial hypotension: review and expert opinion. Acta Neurologica Belgica, 2020. 120(1): p. 9-18. [PMID: 31215003] [pmid]

3. Davidson, B., et al., Spontaneous intracranial hypotension: a review and introduction of an algorithm for management. World neurosurgery, 2017. 101: p. 343-349. [PMID: 28192268] [pmid]

4. Khanam, R. and M. Hussain, Yolov11: An overview of the key architectural enhancements. arXiv 2024. arXiv preprint arXiv:2410.17725, 2024. [https://doi.org/10.48550/arXiv.2410.17725] [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/363-01-016