669-01-014 ISMRM Abstract

Central Vein Sign Detection: Evaluating the Impact of Lesion Size and Transfer Learning

Primary: Neuro - Multiple Sclerosis

Secondary: Analysis Methods - Segmentation and Detection

669-01-014 · Neuroinflammation: Metabolites, Function, and AI · Thursday, 14 May, 8:30 AM–9:25 AM · Digital Posters Row J

Keywords: Multiple Sclerosis Differential Diagnosis Central Vein Sign McDonald Criteria

Accepted

Tommaso Di Noto ^1,2,3, Till Huelnhagen⁴, Matej Kudrna⁵, Manuela Vaneckova⁵, Tobias Kober⁴, Jonas Richiardi^3,6, Lynn Daboul^7,8, Bryan Quah⁹, Jin Jin¹⁰, Omar Al-Louzi^8,9, Daniel S Reich⁸, Pascal Sati⁹, Bénédicte Maréchal^1,2,3, Jonathan A Disselhorst^1,2,3

¹Swiss Innovation Hub, Siemens Healthineers International AG, Lausanne, Switzerland

²LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

³Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland

⁴MR Research & Clinical Translation, Siemens Healthineers AG, Erlangen, Germany

⁵Charles University and General University Hospital, Prague, Czech Republic

⁶CIBM Center for Biomedical Imaging, Lausanne, Switzerland

⁷Department of Neurology, Brigham and Women's Hospital, Boston, United States of America

⁸Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institute of Health, Bethesda, United States of America

⁹Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, United States of America

¹⁰Siemens Healthineers Pty Ltd, Brisbane, Australia

Presenting Author: Tommaso Di Noto

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.

References

1. Montalban, Xavier, et al. “Diagnosis of multiple sclerosis: 2024 revisions of the McDonald criteria.” The Lancet Neurology 24.10 (2025): 850-865. 10.1016/S1474-4422(25)00270-4 [doi]

2. Campion, Tom, et al. “FLAIR* to visualize veins in white matter lesions: a new tool for the diagnosis of multiple sclerosis?” European radiology 27.10 (2017): 4257-4263. 10.1007/s00330-017-4822-z [doi]

3. Dworkin, Jordan D., et al. “Automated integration of multimodal MRI for the probabilistic detection of the central vein sign in white matter lesions.” American Journal of Neuroradiology 39.10 (2018): 1806-1813. 10.3174/ajnr.A5765 [doi]

4. Huelnhagen, Till, et al. “Improved Automated Central Vein Sign Assessment by Multi-Level Classification.” ISMRM 2022. https://archive.ismrm.org/2022/0199.html

5. Manning, Abigail R., et al. “Multicenter automated central vein sign detection performs as well as manual assessment for the diagnosis of multiple sclerosis.” American Journal of Neuroradiology 46.3 (2025): 620-626. 10.3174/ajnr.A8510 [doi]

6. Sati, Pascal, et al. "The central vein sign and its clinical evaluation for the diagnosis of multiple sclerosis: a consensus statement from the North American Imaging in Multiple Sclerosis Cooperative." Nature Reviews Neurology 12.12 (2016): 714-722. 10.1038/nrneurol.2016.166 [doi]

7. Sati, Pascal, et al. “FLAIR*: a combined MR contrast technique for visualizing white matter lesions and parenchymal veins.” Radiology 265.3 (2012): 926-932. 10.1148/radiol.12120208 [doi]

8. Roy, Snehashis, et al. “Multiple sclerosis lesion segmentation from brain MRI via fully convolutional neural networks.” arXiv preprint arXiv:1803.09172 (2018). https://doi.org/10.48550/arXiv.1803.09172 [doi]

9. Di Noto, et al. “Streamlined central vein sign detection for application of the 2024 McDonald Criteria” ECTRIMS 2025

10. Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. “A survey of transfer learning.” Journal of Big data 3.1 (2016)

11. Barkhof, Frederik, et al. “2024 MAGNIMS–CMSC–NAIMS consensus recommendations on the use of MRI for the diagnosis of multiple sclerosis.” The Lancet Neurology 24.10 (2025): 866-879. 10.1016/S1474-4422(25)00304-7 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/669-01-014