ArticleNeurology2025
Automated Whole-Brain Focal Cortical Dysplasia Detection Using MR Fingerprinting With Deep Learning.
Article in Neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Comparison of volume- and surface-based magnetic resonance imaging morphometry algorithms in the detection of focal cortical dysplasia.Epilepsia · 2026Article
- Neuroanatomical correlates of neuropsychological dysfunction in pediatric pharmacoresistant epilepsy due to focal cortical dysplasia type II.Epilepsia open · 2026Article
- Clinical use and radiological yield of magnetic resonance fingerprinting in epilepsy.Epilepsia · 2026Article
- The Brain Imaging and Neurophysiology Dataset of large-scale multimodal neural data.Scientific data · 2026Article
- AI in epilepsy neuroimaging.Current opinion in neurology · 2026Review
- Analysis of cortical dysplasias using b-tensor encoding diffusion MRI in an animal model.PloS one · 2026Article
- Surfaced-based detection of focal cortical dysplasia using magnetic resonance fingerprinting and machine learning.Epilepsia · 2026Article
- Multimodal artificial intelligence in epilepsy: antiseizure medication response, epileptogenic zone localization, and surgical outcome prediction.Frontiers in neurology · 2026Review
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14 authors.
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Abstract
BACKGROUND AND
objectivesFocal cortical dysplasia (FCD) is a common pathology for pharmacoresistant focal epilepsy, yet detection of FCD on clinical MRI is challenging. Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging technique providing fast and reliable tissue property measurements. The aim of this study was to develop an MRF-based deep-learning (DL) framework for whole-brain FCD detection.
methodsWe included patients with pharmacoresistant focal epilepsy and pathologically/radiologically diagnosed FCD, as well as age-matched and sex-matched healthy controls (HCs). All participants underwent 3D whole-brain MRF and clinical MRI scans. T1, T2, gray matter (GM), and white matter (WM) tissue fraction maps were reconstructed from a dictionary-matching algorithm based on the MRF acquisition. A 3D ROI was manually created for each lesion. All MRF maps and lesion labels were registered to the Montreal Neurological Institute space. Mean and SD T1 and T2 maps were calculated voxel-wise across using HC data. T1 and T2
resultsWe included 40 patients with FCD (mean age 28.1 years, 47.5% female; 11 with FCD IIa, 14 with IIb, 12 with mMCD, 3 with MOGHE) and 67 HCs. The DL model with optimal performance used all MRF-based inputs, including MRF-synthesized T1w, T1z, and T2z maps; tissue fraction maps; and morphometric maps. The patient-level sensitivity was 80% with an average of 1.7 false positives (FPs) per patient. Sensitivity was consistent across subtypes, lobar locations, and lesional/nonlesional clinical MRI. Models using clinical images showed lower sensitivity and higher FPs. The MRF-DL model also outperformed the established MAP18 pipeline in sensitivity, FPs, and lesion label overlap. DISCUSSION: 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.
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