Combining magnetic resonance fingerprinting with voxel‐based morphometric analysis to reduce false positives for focal cortical dysplasia detection

• Published in: Epilepsia (Copenhagen) Vol. 65; no. 6; pp. 1631 - 1643
• Main Authors: Ding, Zheng, Hu, Siyuan, Su, Ting‐Yu, Choi, Joon Yul, Morris, Spencer, Wang, Xiaofeng, Sakaie, Ken, Murakami, Hiroatsu, Huppertz, Hans‐Jürgen, Blümcke, Ingmar, Jones, Stephen, Najm, Imad, Ma, Dan, Wang, Zhong Irene
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.06.2024
• Subjects: Adolescent; Adult; Child; Drug Resistant Epilepsy - diagnostic imaging; Drug Resistant Epilepsy - pathology; Dysplasia; Epilepsies, Partial - diagnostic imaging; Epilepsies, Partial - pathology; Epilepsy; False Positive Reactions; Female; Fingerprinting; Focal Cortical Dysplasia; Gray Matter - diagnostic imaging; Gray Matter - pathology; Humans; Hyperplasia; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Lesions; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Malformations of Cortical Development - diagnostic imaging; Malformations of Cortical Development - pathology; Middle Aged; Morphometry; MR fingerprinting; Neural networks; Neuroimaging; Patients; pre‐surgical evaluation; quantitative MRI; relaxometry; Substantia alba; Substantia grisea; Young Adult; epilepsy; MR fingerprinting; quantitative MRI; focal cortical dysplasia; pre‐surgical evaluation; relaxometry
• ISSN: 0013-9580; 1528-1167; 1528-1167
• DOI: 10.1111/epi.17951

Objective We aim to improve focal cortical dysplasia (FCD) detection by combining high‐resolution, three‐dimensional (3D) magnetic resonance fingerprinting (MRF) with voxel‐based morphometric magnetic resonance imaging (MRI) analysis. Methods We included 37 patients with pharmacoresistant focal epilepsy and FCD (10 IIa, 15 IIb, 10 mild Malformation of Cortical Development [mMCD], and 2 mMCD with oligodendroglial hyperplasia and epilepsy [MOGHE]). Fifty‐nine healthy controls (HCs) were also included. 3D lesion labels were manually created. Whole‐brain MRF scans were obtained with 1 mm3 isotropic resolution, from which quantitative T1 and T2 maps were reconstructed. Voxel‐based MRI postprocessing, implemented with the morphometric analysis program (MAP18), was performed for FCD detection using clinical T1w images, outputting clusters with voxel‐wise lesion probabilities. Average MRF T1 and T2 were calculated in each cluster from MAP18 output for gray matter (GM) and white matter (WM) separately. Normalized MRF T1 and T2 were calculated by z‐scores using HCs. Clusters that overlapped with the lesion labels were considered true positives (TPs); clusters with no overlap were considered false positives (FPs). Two‐sample t‐tests were performed to compare MRF measures between TP/FP clusters. A neural network model was trained using MRF values and cluster volume to distinguish TP/FP clusters. Ten‐fold cross‐validation was used to evaluate model performance at the cluster level. Leave‐one‐patient‐out cross‐validation was used to evaluate performance at the patient level. Results MRF metrics were significantly higher in TP than FP clusters, including GM T1, normalized WM T1, and normalized WM T2. The neural network model with normalized MRF measures and cluster volume as input achieved mean area under the curve (AUC) of .83, sensitivity of 82.1%, and specificity of 71.7%. This model showed superior performance over direct thresholding of MAP18 FCD probability map at both the cluster and patient levels, eliminating ≥75% FP clusters in 30% of patients and ≥50% of FP clusters in 91% of patients. Significance This pilot study suggests the efficacy of MRF for reducing FPs in FCD detection, due to its quantitative values reflecting in vivo pathological changes. © 2024 International League Against Epilepsy.