Surfaced-based detection of focal cortical dysplasia using magnetic resonance fingerprinting and machine learning.

ABSTRACT:

Objective

This study was undertaken to develop a framework for focal cortical dysplasia (FCD) detection using surface-based morphometric (SBM) analysis and machine learning (ML) applied to three-dimensional (3D) magnetic resonance fingerprinting (MRF).

Methods

We included 114 subjects (44 patients with medically intractable focal epilepsy and FCD, 70 healthy controls [HCs]). All subjects underwent high-resolution 3-T MRF scans generating T1 and T2 maps. All patients had clinical T1-weighted (T1w) images; 35 also had 3D fluid-attenuated inversion recovery (FLAIR). A 3D region of interest (ROI) was manually created for each lesion. All maps/images and lesion ROIs were registered to T1w images. Surface-based features were extracted following the Multi-center Epilepsy Lesion Det

SUBMITTER: Su TY 

PROVIDER: S-EPMC12893262 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature