Automated Whole-Brain Focal Cortical Dysplasia Detection Using MR Fingerprinting With Deep Learning
Abstract
Background and Objectives
Focal 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.
Methods
We 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 z-score maps for each patient were generated by subtracting the mean HC map and dividing by the SD HC map. MRF-based morphometric maps were produced in the same manner as in the morphometric analysis program (MAP), based on MRF GM and WM maps. A no-new U-Net model was trained using various input combinations, with performance evaluated through leave-one-patient-out cross-validation. We compared model performance using various input combinations from clinical MRI and MRF to assess the impact of different input types on model effectiveness.
Results
We 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.
Get full access to this article
View all available purchase options and get full access to this article.
Supplementary Materials
eFigures
- Download
- 257.93 KB
eTables
- Download
- 266.96 KB
References
1.
Walger L, Adler S, Wagstyl K, et al. Artificial intelligence for the detection of focal cortical dysplasia: challenges in translating algorithms into clinical practice. Epilepsia. 2023;64(5):1093-1112.
2.
Besson P, Andermann F, Dubeau F, Bernasconi A. Small focal cortical dysplasia lesions are located at the bottom of a deep sulcus. Brain. 2008;131(pt 12):3246-3255.
3.
Blümcke I, Thom M, Aronica E, et al. The clinicopathologic spectrum of focal cortical dysplasias: a consensus classification proposed by an ad hoc Task Force of the ILAE Diagnostic Methods Commission. Epilepsia. 2011;52(1):158-174.
4.
Urbach H, Kellner E, Kremers N, Blümcke I, Demerath T. MRI of focal cortical dysplasia. Neuroradiology. 2022;64(3):443-452.
5.
Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203-211.
6.
Gill RS, Lee HM, Caldairou B, et al. Multicenter validation of a deep learning detection algorithm for focal cortical dysplasia. Neurology. 2021;97(16):E1571-E1582.
7.
Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature. 2013;495(7440):187-192.
8.
Ma D, Jones SE, Deshmane A, et al. Development of high-resolution 3D MR fingerprinting for detection and characterization of epileptic lesions. J Magn Reson Imaging. 2019;49(5):1333-1346.
9.
Liao C, Wang K, Cao X, et al. Detection of lesions in mesial temporal lobe epilepsy by using MR fingerprinting. Radiology. 2018;288(3):804-812.
10.
Keil VC, Bakoeva SP, Jurcoane A, et al. MR fingerprinting as a diagnostic tool in patients with frontotemporal lobe degeneration: a pilot study. NMR Biomed. 2019;32(11):e4157.
11.
Keil VC, Bakoeva SP, Jurcoane A, et al. A pilot study of magnetic resonance fingerprinting in Parkinson's disease. NMR Biomed. 2020;33(11):e4389.
12.
Jaubert O, Arrieta C, Cruz G, et al. Multi-parametric liver tissue characterization using MR fingerprinting: simultaneous T1, T2, T2*, and fat fraction mapping. Magn Reson Med. 2020;84(5):2625-2635.
13.
Körzdörfer G, Jiang Y, Speier P, et al. Magnetic resonance field fingerprinting. Magn Reson Med. 2019;81(4):2347-2359.
14.
Chen Y, Fang Z, Hung SC, Chang WT, Shen D, Lin W. High-resolution 3D MR fingerprinting using parallel imaging and deep learning. Neuroimage. 2020;206:116329.
15.
Cheng F, Liu Y, Chen Y, Yap P. High-resolution 3D magnetic resonance fingerprinting with a graph convolutional network. IEEE Trans Med Imaging. 2023;42(3):674-683.
16.
Körzdörfer G, Kirsch R, Liu K, et al. Reproducibility and repeatability of MR fingerprinting relaxometry in the human brain. Radiology. 2019;292(2):429-437.
17.
Zhao W, Hu Z, Kazerooni AF, et al. Physics-informed discretization for reproducible and robust radiomic feature extraction using quantitative MRI. Invest Radiol. 2024;59(5):359-371.
18.
Chen Y, Jiang Y, Pahwa S, et al. MR fingerprinting for rapid quantitative abdominal imaging. Radiology. 2016;279(1):278-286.
19.
Su TY, Choi JY, Hu S, et al. Multiparametric characterization of focal cortical dysplasia using 3D MR fingerprinting. Ann Neurol. 2024;96(5):944-957.
20.
David B, Kröll-Seger J, Schuch F, et al. External validation of automated focal cortical dysplasia detection using morphometric analysis. Epilepsia. 2021;62(4):1005-1021.
21.
Ding Z, Ting SH, Su Y, et al. Combining magnetic resonance fingerprinting with based morphometric analysis to reduce false positives for focal cortical dysplasia detection. Epilepsia. 2024;65:1631-1643.
22.
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham.
23.
McGivney D, Deshmane A, Jiang Y, et al. Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting. Magn Reson Med. 2018;80(1):159-170.
24.
Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal. 2008;12(1):26-41.
25.
Dinov ID. Data Science and Predictive Analytics: Biomedical and Health Applications Using R. 1st ed. Springer; 2018:704.
26.
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis. 2020;128(2):336-359.
27.
Wang ZI, Jones SE, Jaisani Z, et al. Voxel-based morphometric magnetic resonance imaging (MRI) postprocessing in MRI-negative epilepsies. Ann Neurol. 2015;77(6):1060-1075.
28.
El Tahry R, Santos SF, Vrielynck P, et al. Additional clinical value of voxel-based morphometric MRI post-processing for MRI-negative epilepsies: a prospective study. Epileptic Disord. 2020;22(2):156-164.
29.
Najm I, Lal D, Alonso Vanegas M, et al. The ILAE consensus classification of focal cortical dysplasia: an update proposed by an ad hoc task force of the ILAE diagnostic methods commission. Epilepsia. 2022;63(8):1899-1919.
30.
Schurr J, Coras R, Rössler K, et al. Mild malformation of cortical development with oligodendroglial hyperplasia in frontal lobe epilepsy: a new clinico-pathological entity. Brain Pathol. 2017;27(1):26-35.
31.
Hong SJ, Kim H, Schrader D, Bernasconi N, Bernhardt BC, Bernasconi A. Automated detection of cortical dysplasia type II in MRI-negative epilepsy. Neurology. 2014;83(1):48-55.
32.
Spitzer H, Ripart M, Whitaker K, et al. Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study. Brain. 2022;145(11):3859-3871.
33.
Hong SJ, Bernhardt BC, Caldairou B, et al. Multimodal MRI profiling of focal cortical dysplasia type II. Neurology. 2017;88(8):734-742.
34.
Dastmalchian S, Kilinc O, Onyewadume L, et al. Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors. Eur J Nucl Med Mol Imaging. 2021;48(3):683-693.
35.
Tippareddy C, Onyewadume L, Sloan AE, et al. Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study. Eur Radiol. 2023;33(2):836-844.
36.
Zheng R, Chen R, Chen C, et al. Automated detection of focal cortical dysplasia based on magnetic resonance imaging and positron emission tomography. Seizure. 2024;117:126-132.
37.
Zhang S, Zhuang Y, Luo Y, Zhu F, Zhao W, Zeng H. Deep learning-based automated lesion segmentation on pediatric focal cortical dysplasia II preoperative MRI: a reliable approach. Insights Imaging. 2024;15(1):71.
38.
Thomas E, Pawan SJ, Kumar S, et al. Multi-res-attention UNet: a CNN model for the segmentation of focal cortical dysplasia lesions from magnetic resonance images. IEEE J Biomed Heal Informatics. 2021;25(5):1724-1734.
39.
Veersema TJ, Swampillai B, Ferrier CH, et al. Long-term seizure outcome after epilepsy surgery in patients with mild malformation of cortical development and focal cortical dysplasia. Epilepsia Open. 2019;4(1):170-175.
40.
Poorman ME, Martin MN, Ma D, et al. Magnetic resonance fingerprinting part 1: potential uses, current challenges, and recommendations. J Magn Reson Imaging. 2020;51(3):675-692.
41.
Piredda GF, Hilbert T, Granziera C, et al. Quantitative brain relaxation atlases for personalized detection and characterization of brain pathology. Magn Reson Med. 2020;83(1):337-351.
42.
Choi JY, Hu S, Su T, et al. Normative quantitative relaxation atlases for characterization of cortical regions using magnetic resonance fingerprinting. Cereb Cortex. 2023;33(7):3562-3574.
43.
Kaestner E, Rao J, Chang AJ, et al. Convolutional neural network algorithm to determine lateralization of seizure onset in patients with epilepsy: a proof-of-principle study. Neurology. 2023;101(3):E324-E335.
44.
Buonincontri G, Kurzawski JW, Kaggie JD, et al. Three dimensional MRF obtains highly repeatable and reproducible multi-parametric estimations in the healthy human brain at 1.5T and 3T. Neuroimage. 2021;226:117573.
45.
Fujita S, Buonincontri G, Cencini M, et al. Repeatability and reproducibility of human brain morphometry using three-dimensional magnetic resonance fingerprinting. Hum Brain Mapp. 2021;42(2):275-285.
46.
Rosenow F, Lüders H. Presurgical evaluation of epilepsy. Brain. 2001;124(pt 9):1683-1700.
47.
Fauser S, Sisodiya SM, Martinian L, et al. Multi-focal occurrence of cortical dysplasia in epilepsy patients. Brain. 2009;132(pt 8):2079-2090.
Information & Authors
Information
Published In
Copyright
© 2025 American Academy of Neurology.
Publication History
Received: October 15, 2024
Accepted: March 19, 2025
Published online: May 16, 2025
Published in issue: June 10, 2025
Disclosure
I.M. Najm is on the speakers' bureau of Eisai. S.E. Jones received travel and speaker fees from SIEMENS Healthineers. D. Ma has MRF patents licensed by SIEMENS. The other authors have no competing interests to disclose. Go to Neurology.org/N for full disclosures.
Study Funding
This study was supported by the NIH (R01 NS109439, 2R01 NS109439).
Authors
Author Contributions
Z. Ding: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data. S. Morris: major role in the acquisition of data; study concept or design; analysis or interpretation of data. S. Hu: major role in the acquisition of data. T.-Y. Su: major role in the acquisition of data; analysis or interpretation of data. J.Y. Choi: drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data. I. Blümcke: analysis or interpretation of data. X. Wang: analysis or interpretation of data. K. Sakaie: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. H. Murakami: analysis or interpretation of data. A.V. Alexopoulos: analysis or interpretation of data. S.E. Jones: analysis or interpretation of data. I.M. Najm: analysis or interpretation of data. D. Ma: major role in the acquisition of data; study concept or design. Z.I. Wang: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data.
Metrics & Citations
Metrics
Citation information is sourced from Crossref Cited-by service.
Citations
Download Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Select your manager software from the list below and click Download.
Cited By
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Personal login Institutional LoginPurchase Options
The neurology.org payment platform is currently offline. Our technical team is working as quickly as possible to restore service.
If you need immediate support or to place an order, please call or email customer service:
- 1-800-638-3030 for U.S. customers - 8:30 - 7 pm ET (M-F)
- 1-301-223-2300 for customers outside the U.S. - 8:30 - 7 pm ET (M-F)
- [email protected]
We appreciate your patience during this time and apologize for any inconvenience.