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Jiménez-Murillo D, Castro-Ospina AE, Duque-Muñoz L, Martínez-Vargas JD, Suárez-Revelo JX, Vélez-Arango JM, de la Iglesia-Vayá M. Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7072. [PMID: 37631608 PMCID: PMC10458261 DOI: 10.3390/s23167072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/02/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023]
Abstract
Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)-one of the most commonly used non-invasive neuroimaging methods for evaluating the structure of the brain-is often implemented along with automatic methods to diagnose FCD. In this review, we define three categories for FCD identification based on MRI: visual, semi-automatic, and fully automatic methods. By conducting a systematic review following the PRISMA statement, we identified 65 relevant papers that have contributed to our understanding of automatic FCD identification techniques. The results of this review present a comprehensive overview of the current state-of-the-art in the field of automatic FCD identification and highlight the progress made and challenges ahead in developing reliable, efficient methods for automatic FCD diagnosis using MRI images. Future developments in this area will most likely lead to the integration of these automatic identification tools into medical image-viewing software, providing neurologists and radiologists with enhanced diagnostic capabilities. Moreover, new MRI sequences and higher-field-strength scanners will offer improved resolution and anatomical detail for precise FCD characterization. This review summarizes the current state of automatic FCD identification, thereby contributing to a deeper understanding and the advancement of FCD diagnosis and management.
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Affiliation(s)
- David Jiménez-Murillo
- Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia; (D.J.-M.); (L.D.-M.)
| | - Andrés Eduardo Castro-Ospina
- Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia; (D.J.-M.); (L.D.-M.)
| | - Leonardo Duque-Muñoz
- Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia; (D.J.-M.); (L.D.-M.)
| | | | - Jazmín Ximena Suárez-Revelo
- Grupo de Investigación en Imágenes Médicas SURA, Ayudas Diagnósticas SURA, Carrera 48 # 26-50, Piso 2, Medellín 050021, Colombia; (J.X.S.-R.); (J.M.V.-A.)
| | - Jorge Mario Vélez-Arango
- Grupo de Investigación en Imágenes Médicas SURA, Ayudas Diagnósticas SURA, Carrera 48 # 26-50, Piso 2, Medellín 050021, Colombia; (J.X.S.-R.); (J.M.V.-A.)
| | - Maria de la Iglesia-Vayá
- Biomedical Imaging Unit FISABIO-CIPF, Foundation for the Promotion of the Research in Healthcare and Biomedicine (FISABIO), Avda. de Catalunya, 21, 46020 Valencia, Spain;
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM-G23), 28029 Madrid, Spain
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Gennari AG, Cserpan D, Stefanos-Yakoub I, Kottke R, O’Gorman Tuura R, Ramantani G. Diffusion tensor imaging discriminates focal cortical dysplasia from normal brain parenchyma and differentiates between focal cortical dysplasia types. Insights Imaging 2023; 14:36. [PMID: 36826756 PMCID: PMC9958211 DOI: 10.1186/s13244-023-01368-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 12/29/2022] [Indexed: 02/25/2023] Open
Abstract
OBJECTIVES Although diffusion tensor imaging (DTI) may facilitate the identification of cytoarchitectural changes associated with focal cortical dysplasia (FCD), the predominant aetiology of paediatric structural epilepsy, its potential has thus far remained unexplored in this population. Here, we investigated whether DTI indices can differentiate FCD from contralateral brain parenchyma (CBP) and whether clinical features affect these indices. METHODS In this single-centre, retrospective study, we considered children and adolescents with FCD-associated epilepsy who underwent brain magnetic resonance (MRI), including DTI. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity, and radial diffusivity, were calculated in both FCD and CBP. The DTI indices best discriminating between FCD and CBP were subsequently used to assess the link between DTI and selected clinical and lesion-related parameters. RESULTS We enrolled 32 patients (20 male; median age at MRI 4 years), including 15 with histologically confirmed FCD. FA values were lower (p = 0.03), whereas MD values were higher in FCD than in CBP (p = 0.04). The difference in FA values between FCD and CBP was more pronounced for a positive vs. negative history of status epilepticus (p = 0.004). Among histologically confirmed cases, the difference in FA values between FCD and CBP was more pronounced for type IIb versus type I FCD (p = 0.03). CONCLUSIONS FA and MD discriminate between FCD and CBP, while FA differentiates between FCD types. Status epilepticus increases differences in FA, potentially reflecting changes induced in the brain. Our findings support the potential of DTI to serve as a non-invasive biomarker to characterise FCD in the paediatric population.
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Affiliation(s)
- Antonio Giulio Gennari
- Department of Neuropediatrics, University Children's Hospital Zurich, Steinwiesstrasse 75, 8032, Zurich, Switzerland. .,MR-Research Centre, University Children's Hospital Zurich, Zurich, Switzerland.
| | - Dorottya Cserpan
- grid.412341.10000 0001 0726 4330Department of Neuropediatrics, University Children’s Hospital Zurich, Steinwiesstrasse 75, 8032 Zurich, Switzerland
| | - Ilona Stefanos-Yakoub
- grid.412341.10000 0001 0726 4330Department of Neuropediatrics, University Children’s Hospital Zurich, Steinwiesstrasse 75, 8032 Zurich, Switzerland
| | - Raimund Kottke
- grid.412341.10000 0001 0726 4330Department of Diagnostic Imaging, University Children’s Hospital Zurich, Zurich, Switzerland
| | - Ruth O’Gorman Tuura
- grid.412341.10000 0001 0726 4330MR-Research Centre, University Children’s Hospital Zurich, Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland ,grid.412341.10000 0001 0726 4330Children’s Research Centre, University Children’s Hospital Zurich, Zurich, Switzerland
| | - Georgia Ramantani
- grid.412341.10000 0001 0726 4330Department of Neuropediatrics, University Children’s Hospital Zurich, Steinwiesstrasse 75, 8032 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland ,grid.412341.10000 0001 0726 4330Children’s Research Centre, University Children’s Hospital Zurich, Zurich, Switzerland
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Nandakumar N, Hsu D, Ahmed R, Venkataraman A. DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization from Resting-State fMRI Connectivity. IEEE Trans Biomed Eng 2022; 70:216-227. [PMID: 35776823 PMCID: PMC9841829 DOI: 10.1109/tbme.2022.3187942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVE Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up and therapeutic planning in medication refractory epilepsy. In this paper, we present the first deep learning approach to localize the EZ based on resting-state fMRI (rs-fMRI) data. METHODS Our network, called DeepEZ, uses a cascade of graph convolutions that emphasize signal propagation along expected anatomical pathways. We also integrate domain-specific information, such as an asymmetry term on the predicted EZ and a learned subject-specific bias to mitigate environmental confounds. RESULTS We validate DeepEZ on rs-fMRI collected from 14 patients with focal epilepsy at the University of Wisconsin Madison. Using cross validation, we demonstrate that DeepEZ achieves consistently high EZ localization performance (Accuracy: 0.88 ± 0.03; AUC: 0.73 ± 0.03) that far outstripped any of the baseline methods. This performance is notable given the variability in EZ locations and scanner type across the cohort. CONCLUSION Our results highlight the promise of using DeepEZ as an accurate and noninvasive therapeutic planning tool for medication refractory epilepsy. SIGNIFICANCE While prior work in EZ localization focused on identifying localized aberrant signatures, there is growing evidence that epileptic seizures affect inter-regional connectivity in the brain. DeepEZ allows clinicians to harness this information from noninvasive imaging that can easily be integrated into the existing clinical workflow.
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Affiliation(s)
- Naresh Nandakumar
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - David Hsu
- Department of Neurology, University of Wisconsin, USA
| | - Raheel Ahmed
- Department of Neurosurgery, University of Wisconsin, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218 USA
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Sollee J, Tang L, Igiraneza AB, Xiao B, Bai HX, Yang L. Artificial Intelligence for Medical Image Analysis in Epilepsy. Epilepsy Res 2022; 182:106861. [DOI: 10.1016/j.eplepsyres.2022.106861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 11/18/2021] [Accepted: 01/16/2022] [Indexed: 11/16/2022]
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Isen J, Perera-Ortega A, Vos SB, Rodionov R, Kanber B, Chowdhury FA, Duncan JS, Mousavi P, Winston GP. Non-parametric combination of multimodal MRI for lesion detection in focal epilepsy. NEUROIMAGE-CLINICAL 2021; 32:102837. [PMID: 34619650 PMCID: PMC8503566 DOI: 10.1016/j.nicl.2021.102837] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 09/10/2021] [Accepted: 09/20/2021] [Indexed: 12/21/2022]
Abstract
Multivariate voxel-based analysis useful for lesion detection in focal epilepsy. Non-parametric combination algorithm used to combine data from various MR sequences. Successful lesion detection demonstrated in MRI-positive and MRI-negative patients. Multimodal analysis detected abnormalities from diverse epileptogenic pathologies. Sensitivity of multivariate analysis notably higher than univariate analyses.
One third of patients with medically refractory focal epilepsy have normal-appearing MRI scans. This poses a problem as identification of the epileptogenic region is required for surgical treatment. This study performs a multimodal voxel-based analysis (VBA) to identify brain abnormalities in MRI-negative focal epilepsy. Data was collected from 69 focal epilepsy patients (42 with discrete lesions on MRI scans, 27 with no visible findings on scans), and 62 healthy controls. MR images comprised T1-weighted, fluid-attenuated inversion recovery (FLAIR), fractional anisotropy (FA) and mean diffusivity (MD) from diffusion tensor imaging, and neurite density index (NDI) from neurite orientation dispersion and density imaging. These multimodal images were coregistered to T1-weighted scans, normalized to a standard space, and smoothed with 8 mm FWHM. Initial analysis performed voxel-wise one-tailed t-tests separately on grey matter concentration (GMC), FLAIR, FA, MD, and NDI, comparing patients with epilepsy to controls. A multimodal non-parametric combination (NPC) analysis was also performed simultaneously on FLAIR, FA, MD, and NDI. Resulting p-maps were family-wise error rate corrected, threshold-free cluster enhanced, and thresholded at p < 0.05. Sensitivity was established through visual comparison of results to manually drawn lesion masks or seizure onset zone (SOZ) from stereoelectroencephalography. A leave-one-out cross-validation with the same analysis protocols was performed on controls to determine specificity. NDI was the best performing individual modality, detecting focal abnormalities in 38% of patients with normal MRI and conclusive SOZ. GMC demonstrated the lowest sensitivity at 19%. NPC provided superior performance to univariate analyses with 50% sensitivity. Specificity in controls ranged between 96 and 100% for all analyses. This study demonstrated the utility of a multimodal VBA utilizing NPC for detecting epileptogenic lesions in MRI-negative focal epilepsy. Future work will apply this approach to datasets from other centres and will experiment with different combinations of MR sequences.
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Affiliation(s)
- Jonah Isen
- School of Computing, Queen's University, Kingston, Canada
| | | | - Sjoerd B Vos
- Centre for Medical Image Computing, University College London, London, UK; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St Peter, UK; National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK; Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Roman Rodionov
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St Peter, UK
| | - Baris Kanber
- Centre for Medical Image Computing, University College London, London, UK; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St Peter, UK; National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK
| | - Fahmida A Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St Peter, UK; National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK
| | - Parvin Mousavi
- School of Computing, Queen's University, Kingston, Canada
| | - Gavin P Winston
- School of Computing, Queen's University, Kingston, Canada; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St Peter, UK; National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK; Department of Medicine, Division of Neurology & Centre for Neuroscience Studies, Queen's University, Kingston, Canada.
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Niyas S, Chethana Vaisali S, Show I, Chandrika T, Vinayagamani S, Kesavadas C, Rajan J. Segmentation of focal cortical dysplasia lesions from magnetic resonance images using 3D convolutional neural networks. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Bartoňová M, Bartoň M, Říha P, Vojtíšek L, Brázdil M, Rektor I. The benefit of the diffusion kurtosis imaging in presurgical evaluation in patients with focal MR-negative epilepsy. Sci Rep 2021; 11:14208. [PMID: 34244544 PMCID: PMC8270902 DOI: 10.1038/s41598-021-92804-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 06/15/2021] [Indexed: 02/06/2023] Open
Abstract
The effectivity of diffusion-weighted MRI methods in detecting the epileptogenic zone (EZ) was tested. Patients with refractory epilepsy (N=25) who subsequently underwent resective surgery were recruited. First, the extent of white matter (WM) asymmetry from mean kurtosis (MK) was calculated in order to detect the lobe with the strongest impairment. Second, a newly developed metric was used, reflecting a selection of brain areas with concurrently increased mean Diffusivity, reduced fractional Anisotropy, and reduced mean Kurtosis (iDrArK). A two-step EZ detection was performed as (1) lobe-specific detection, (2) iDrArK voxel-wise detection (with a possible lobe-specific restriction if the result of the first step was significant in a given subject). The method results were compared with the surgery resection zones. From the whole cohort (N=25), the numbers of patients with significant results were: 10 patients in lobe detection and 9 patients in EZ detection. From these subsets of patients with significant results, the impaired lobe was successfully detected with 100% accuracy; the EZ was successfully detected with 89% accuracy. The detection of the EZ using iDrArK was substantially more successful when compared with solo diffusional parameters (or their pairwise combinations). For a subgroup with significant results from step one (N=10), iDrArK without lobe restriction achieved 37.5% accuracy; lobe-restricted iDrArK achieved 100% accuracy. The study shows the plausibility of MK for detecting widespread WM changes and the benefit of combining different diffusional voxel-wise parameters.
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Affiliation(s)
- Michaela Bartoňová
- grid.10267.320000 0001 2194 0956Central European Institute of Technology (CEITEC), Multimodal and Functional Neuroimaging Research Group, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic ,grid.10267.320000 0001 2194 0956Brno Epilepsy Center, Full member of the European Reference Network (ERN) EpiCARE, First Department of Neurology, St. Anne′s University Hospital, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Marek Bartoň
- grid.10267.320000 0001 2194 0956Central European Institute of Technology (CEITEC), Multimodal and Functional Neuroimaging Research Group, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
| | - Pavel Říha
- grid.10267.320000 0001 2194 0956Central European Institute of Technology (CEITEC), Multimodal and Functional Neuroimaging Research Group, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic ,grid.10267.320000 0001 2194 0956Brno Epilepsy Center, Full member of the European Reference Network (ERN) EpiCARE, First Department of Neurology, St. Anne′s University Hospital, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Lubomír Vojtíšek
- grid.10267.320000 0001 2194 0956Central European Institute of Technology (CEITEC), Multimodal and Functional Neuroimaging Research Group, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
| | - Milan Brázdil
- grid.10267.320000 0001 2194 0956Central European Institute of Technology (CEITEC), Multimodal and Functional Neuroimaging Research Group, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic ,grid.10267.320000 0001 2194 0956Brno Epilepsy Center, Full member of the European Reference Network (ERN) EpiCARE, First Department of Neurology, St. Anne′s University Hospital, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Ivan Rektor
- grid.10267.320000 0001 2194 0956Central European Institute of Technology (CEITEC), Multimodal and Functional Neuroimaging Research Group, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic ,grid.10267.320000 0001 2194 0956Brno Epilepsy Center, Full member of the European Reference Network (ERN) EpiCARE, First Department of Neurology, St. Anne′s University Hospital, Faculty of Medicine, Masaryk University, Brno, Czech Republic
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Sone D, Beheshti I. Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review. Front Neurosci 2021; 15:684825. [PMID: 34239413 PMCID: PMC8258163 DOI: 10.3389/fnins.2021.684825] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/31/2021] [Indexed: 12/13/2022] Open
Abstract
Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan.,Department of Clinical and Experimental Epilepsy, University College London Institute of Neurology, London, United Kingdom
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
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Gryska E, Schneiderman J, Björkman-Burtscher I, Heckemann RA. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ Open 2021; 11:e042660. [PMID: 33514580 PMCID: PMC7849889 DOI: 10.1136/bmjopen-2020-042660] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 01/09/2021] [Accepted: 01/12/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field. DESIGN Scoping review. SETTING Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison. RESULTS Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity. CONCLUSIONS The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.
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Affiliation(s)
- Emilia Gryska
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
| | - Justin Schneiderman
- Sektionen för klinisk neurovetenskap, Goteborgs Universitet Institutionen for Neurovetenskap och fysiologi, Goteborg, Sweden
| | | | - Rolf A Heckemann
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
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Lorio S, Adler S, Gunny R, D'Arco F, Kaden E, Wagstyl K, Jacques TS, Clark CA, Cross JH, Baldeweg T, Carmichael DW. MRI profiling of focal cortical dysplasia using multi-compartment diffusion models. Epilepsia 2020; 61:433-444. [PMID: 32065673 PMCID: PMC7154549 DOI: 10.1111/epi.16451] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 01/26/2020] [Accepted: 01/27/2020] [Indexed: 12/11/2022]
Abstract
Objective Focal cortical dysplasia (FCD) lesion detection and subtyping remain challenging on conventional MRI. New diffusion models such as the spherical mean technique (SMT) and neurite orientation dispersion and density imaging (NODDI) provide measurements that potentially produce more specific maps of abnormal tissue microstructure. This study aims to assess the SMT and NODDI maps for computational and radiological lesion characterization compared to standard fractional anisotropy (FA) and mean diffusivity (MD). Methods SMT, NODDI, FA, and MD maps were calculated for 33 pediatric patients with suspected FCD (18 histologically confirmed). Two neuroradiologists scored lesion visibility on clinical images and diffusion maps. Signal profile changes within lesions and homologous regions were quantified using a surface‐based approach. Diffusion parameter changes at multiple cortical depths were statistically compared between FCD type IIa and type IIb. Results Compared to fluid‐attenuated inversion recovery (FLAIR) or T1‐weighted imaging, lesions conspicuity on NODDI intracellular volume fraction (ICVF) maps was better/equal/worse in 5/14/14 patients, respectively, while on SMT intra‐neurite volume fraction (INVF) in 3/3/27. Compared to FA or MD, lesion conspicuity on the ICVF was better/equal/worse in 27/4/2, while on the INVF in 20/7/6. Quantitative signal profiling demonstrated significant ICVF and INVF reductions in the lesions, whereas SMT microscopic mean, radial, and axial diffusivities were significantly increased. FCD type IIb exhibited greater changes than FCD type IIa. No changes were detected on FA or MD profiles. Significance FCD lesion‐specific signal changes were found in ICVF and INVF but not in FA and MD maps. ICVF and INVF showed greater contrast than FLAIR in some cases and had consistent signal changes specific to FCD, suggesting that they could improve current presurgical pediatric epilepsy imaging protocols and can provide features useful for automated lesion detection.
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Affiliation(s)
- Sara Lorio
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK.,School of Biomedical Engineering & Imaging Sciences, St Thomas' Hospital, King's College London, London, UK
| | - Sophie Adler
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | | | | | - Enrico Kaden
- Centre for Medical Image Computing, University College London, London, UK
| | - Konrad Wagstyl
- Brain Mapping Unit, Institute of Psychiatry, University of Cambridge, Cambridge, UK
| | - Thomas S Jacques
- Developmental Biology and Cancer Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.,Department of Histopathology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Chris A Clark
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Judith Helen Cross
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Torsten Baldeweg
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - David W Carmichael
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK.,School of Biomedical Engineering & Imaging Sciences, St Thomas' Hospital, King's College London, London, UK
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Abstract
Epilepsy in infants and children is one of the most common and devastating neurological disorders. In the past, we had a limited understanding of the causes of epilepsy in pediatric patients, so we treated pediatric epilepsy according to seizure type. Now with new tools and tests, we are entering the age of precision medicine in pediatric epilepsy. In this review, we use the new etiological classification system proposed by the International League Against Epilepsy to review the advances in the diagnosis of pediatric epilepsy, describe new tools to identify seizure foci for epilepsy surgery, and define treatable epilepsy syndromes.
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Affiliation(s)
- Priya Sharma
- Department of Neurology, University of North Carolina School of Medicine, Physicians Office Building, Chapel Hill, NC, 27599-7025, USA
| | - Ammar Hussain
- Department of Neurology, University of North Carolina School of Medicine, Physicians Office Building, Chapel Hill, NC, 27599-7025, USA
| | - Robert Greenwood
- Department of Neurology & Pediatrics, University of North Carolina School of Medicine, 2141 Physicians Office Building, Chapel Hill, NC, 27599-7025, USA
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