Automated Whole-Brain Focal Cortical Dysplasia Detection Using MR Fingerprinting With Deep Learning
The MRF-DL framework demonstrated efficacy for whole-brain FCD detection. Multiparametric MRF features from a single scan offer promising inputs for developing a deep-learning tool capable of detecting subtle epileptic lesions.
Authors
- Ting‐Yu Su (ORCID: https://orcid.org/0000-0001-5054-9163)
- Andreas V. Alexopoulos (ORCID: https://orcid.org/0000-0003-0214-3286)
- Irène Wang (ORCID: https://orcid.org/0000-0002-3829-5217)
- Spencer Morris (ORCID: https://orcid.org/0000-0001-8002-7446)
- Ingmar Blümcke (ORCID: https://orcid.org/0000-0001-8676-0788)
- Ken Sakaie (ORCID: https://orcid.org/0000-0002-5633-4494)
- Siyuan Hu (ORCID: https://orcid.org/0000-0003-3137-0605)
- Joon Yul Choi (ORCID: https://orcid.org/0000-0002-2428-0209)
- Imad Najm (ORCID: https://orcid.org/0000-0001-5192-2089)
- Dan Ma (ORCID: https://orcid.org/0000-0003-1664-9579)
- Stephen E. Jones (ORCID: https://orcid.org/0000-0001-6511-9078)
- Xiao Feng Wang (ORCID: https://orcid.org/0000-0001-5966-6673)
- Zheng Ding (ORCID: https://orcid.org/0009-0003-4358-0295)
- Hiroatsu Murakami (ORCID: https://orcid.org/0000-0001-7349-3182)
Institutions
- Cleveland Clinic (US)
- Duke University (US)
- Yonsei University (KR)
- Universitätsklinikum Erlangen (DE)
- The Neurological Institute (US)
- Case Western Reserve University (US)
Publication Details
- Journal
- Neurology
- Published
- 2025-05-16
- DOI
- https://doi.org/10.1212/wnl.0000000000213691
- Citations
- 6
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 5.05