Voxel-based morphometry in the detection of dysplasia and neoplasia in childhood epilepsy: Combined grey/white matter analysis augments detection (original) (raw)
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Journal of Clinical Neuroscience, 2009
The purpose of this exploratory investigation was to evaluate voxel-based morphometry (VBM) in detecting lesions underlying childhood epilepsy, and to establish the optimal image processing and statistical parameters in this context. The patients were 16 children (10 boys) aged 5.9 to 15.2 years (mean 11.3 years) with epilepsy and focal cortical dysplasia (FCD) or neoplasia. The control group comprised 24 normal children (12 boys), age matched to the patients. MRI volumes were spatially normalised to a custom template and segmented into grey matter (GM) and white matter. Using statistical parametric mapping, the GM segment from each patient was then contrasted with the mean GM segment of the control group utilising different VBM post-processing methods. Maps showing increased/decreased areas of GM concentration or volume were generated and compared with visually identified lesions. The results indicated that conservative VBM parameters of linear normalisation with no modulation produced the highest rates of lesion detection, which were identical for FCD and neoplasia at 5/8 lesions. These preliminary data suggest that VBM analysis of GM using conservative parameters can usually detect FCD and neoplasia in the MRI of children with epilepsy, but sensitivity may be inadequate for routine clinical application. Further refinement of the technique may be necessary.
Individual voxel-based analysis of gray matter in focal cortical dysplasia
NeuroImage, 2006
High-resolution MRI of the brain has made it possible to identify focal cortical dysplasia (FCD) in an increasing number of patients. There is evidence for structural abnormalities extending beyond the visually identified FCD lesion. Voxel-based morphometry (VBM) has the potential of detecting both lesions and extra-lesional abnormalities because it performs a whole brain voxel-wise comparison. However, on T1-weighted MRI, FCD lesions are characterized by a wide spectrum of signal hyperintensity that may compromise the results of the segmentation step in VBM. Our purpose was to investigate gray matter (GM) changes in individual FCD patients using voxel-based morphometry (VBM). In addition, we sought to assess the performance of this technique for FCD detection with respect to lesion intensity using an operator designed to emphasize areas of hyperintense T1 signal. We studied 27 patients with known FCD and focal epilepsy and 39 healthy controls. We compared the GM map of each subject (controls and patients) with the average GM map of all controls and obtained a GM z-score map for each individual. The protocol being designed to achieve a maximal specificity, no differences in GM concentration were found in the control group. The z-score maps showed an increase in GM that coincided with the lesion in 21/27 (78%) patients. Five of the six remaining patients whose lesions were not detected by VBM presented with a strong lesion hyperintensity, and a significant part of their lesion was misclassified as white matter. In 16/ 27 (59%) patients, there were additional areas of GM increase distant from the primary lesion. Areas of GM decrease were found in 8/27 (30%) patients. In conclusion, individual voxel-based analysis was able to detect FCD in a majority of patients. Moreover, FCD was often associated with widespread GM changes extending beyond the visible lesion. In its current form, however, individual VBM may be unable to detect lesions characterized by strong signal intensity abnormalities. D
Detection and Localization of Focal Cortical Dysplasia by Voxel-based 3-D MRI Analysis
Epilepsia, 2002
Purpose: Focal cortical dysplasia (FCD) is a frequent cause of partial epilepsy. Its diagnosis by visual evaluation of magnetic resonance images (MRIs) remains difficult. The purpose of this study was to apply a novel automated and observer-independent voxel-based technique for the analysis of 3-dimensional (3-D) MRI to detect and localize FCD.
Morphometric analysis of white matter lesions in MR images: method and validation
IEEE Transactions on Medical Imaging, 1994
The analysis of MR images is evolving from qualitatiVe to quantitative. More and more, the question asked by clinicians is how much and where, rather than a simple statement on the presence or absence of abnormalities. This paper presents a study in which the results obtained with a multispectral segmentation technique are quantitatively compared to manually delineated regions. The core of the semiautomatic image analysis System is a supervised artificial neural network classifier augmented with dedicated pre-and postprocessing algorithms, including anisotropic noise filtering and a surface-fitting method focused on the quantitation of white matter lesions in the hu-visual agreement with the expert's judgement, a number of factors, the leading of which are intra-and interslice intensity variations, reduce the accuracy and reliability of these approaches. Because of the existence of these acquisition artifacts, the development of fully automatic segmentation methods for MRI data does not currently appear to be an attainable goal. However, robust semiautomatic techniques can be developed in which intervention is reduced and for the correction of spatial intensity variations. The study was by computer environments integrating a powerful User interface with a Set Of processing and ClaSSifiCatiOn man brain. A total of 36 images from six brain volumes was analyzed twice by each of two operators, under supervision of a neuroradiologist. Both the intra-and interrater variability of the methods were studied in terms of the average tissue area detected per slice, the correlation coefficients between area measurements, and a measure of similarity derived from the kappa statistic. The results indicate that, compared to a manual method, the use of the semiautomatic technique not only facilitates the analysis of the images, but also has similar or lower intra-and interrater variabilities.
Morphometric MRI analysis improves detection of focal cortical dysplasia type II
Brain, 2011
Focal cortical dysplasias type II (FCD II) are highly epileptogenic lesions frequently causing pharmacoresistant epilepsy. Detection of these lesions on MRI is still challenging as FCDs may be very subtle in appearance and might escape conventional visual analysis. Morphometric MRI analysis is a voxel-based post-processing method based on algorithms of the statistical parametric mapping software (SPM5). It creates three dimensional feature maps highlighting brain areas with blurred grey-white matter junction and abnormal gyration, and thereby may help to detect FCD. In this study, we evaluated the potential diagnostic value of morphometric analysis as implemented in a morphometric analysis programme, compared with conventional visual analysis by an experienced neuroradiologist in 91 patients with histologically proven FCD II operated on at the University Hospital of Bonn between 2000 and 2010 (FCD IIa, n = 17; IIb, n = 74). All preoperative MRI scans were evaluated independently (i) based on conventional visual analysis by an experienced neuroradiologist and (ii) using morphometric analysis. Both evaluators had the same clinical information (electroencephalography and semiology), but were blinded to each other's results. The detection rate of FCD using morphometric analysis was superior to conventional visual analysis in the FCD IIa subgroup (82% versus 65%), while no difference was found in the FCD IIb subgroup (92% versus 91%). However, the combination of conventional visual analysis and morphometric analysis provided complementary information and detected 89 out of all 91 FCDs (98%). The combination was significantly superior to conventional visual analysis alone in both subgroups resulting in a higher diagnostic sensitivity (94% versus 65%, P = 0.031 for FCD IIa; 99% versus 91%, P = 0.016 for FCD IIb). In conclusion, the additional application of morphometric MRI analysis increases the diagnostic sensitivity for FCD II in comparison with conventional visual analysis alone. Since detection of FCDs on MRI during the presurgical evaluation markedly improves the chance of becoming seizure free postoperatively, we apply morphometric analysis in all patients who are MRI-negative after conventional visual analysis at our centre. Abbreviations: FLAIR = fluid-attenuated inversion recovery; CVA = conventional visual analysis; FCD = focal cortical dysplasia; MAP = morphometric analysis programme; MRI = magnetic resonance imaging
Epilepsy & Behavior, 2020
Focal cortical dysplasias (FCDs) are a frequent cause of epilepsy. It has been reported that up to 40% of them cannot be visualized with conventional magnetic resonance imaging (MRI). The main objective of this work was to evaluate by means of a retrospective descriptive observational study whether the automated brain segmentation is useful for detecting FCD. One hundred and fifty-five patients, who underwent surgery between the years 2009 and 2016, were reviewed. Twenty patients with FCD confirmed by histology and a preoperative segmentation study, with ages ranging from 3 to 43 years (14 men), were analyzed. Three expert neuroradiologists visually analyzed conventional and advanced MRI with automated segmentation. They were classified into positive and negative concerning visualization of FCD by consensus. Of the 20 patients evaluated with conventional MRI, 12 were positive for FCD. Of the negative studies for FCD with conventional MRI, 2 (25%) were positive when they were analyzed with automated segmentation. In 13 of the 20 patients (with positive segmentation for FCD), cortical thickening was observed in 5 (38.5%), while pseudothickening was observed in the rest of patients (8, 61.5%) in the anatomical region of the brain corresponding to the dysplasia. This work demonstrated that automated brain segmentation helps to increase detection of FCDs that are unable to be visualized in conventional MRI images.
Faculty Opinions recommendation of Voxel-based magnetic resonance image postprocessing in epilepsy
Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature, 2019
Objective: Although the general utility of voxel-based processing of structural magnetic resonance imaging (MRI) data for detecting occult lesions in focal epilepsy is established, many differences exist among studies, and it is unclear which processing method is preferable. The aim of this study was to compare the ability of commonly used methods to detect epileptogenic lesions in magnetic resonance MRI-positive and MRI-negative patients, and to estimate their diagnostic yield. Methods: We identified 144 presurgical focal epilepsy patients, 15 of whom had a histopathologically proven and MRI-visible focal cortical dysplasia; 129 patients were MRI negative with a clinical hypothesis of seizure origin, 27 of whom had resections. We applied four types of voxel-based morphometry (VBM), three based on T1 images (gray matter volume, gray matter concentration, junction map [JM]) and one based on normalized fluid-attenuated inversion recovery (nFSI). Specificity was derived from analysis of 50 healthy controls. Results: The four maps had different sensitivity and specificity profiles. All maps showed detection rates for focal cortical dysplasia patients (MRI positive and negative) of >30% at a strict threshold of p < 0.05 (family-wise error) and >60% with a liberal threshold of p < 0.0001 (uncorrected), except for gray matter volume (14% and 27% detection rate). All maps except nFSI showed poor specificity, with high rates of falsepositive findings in controls. In the MRI-negative patients, absolute detection rates were lower. A concordant nFSI finding had a significant positive odds ratio of 7.33 for a favorable postsurgical outcome in the MRI-negative group. Spatial colocalization of JM and nFSI was rare, yet showed good specificity throughout the thresholds. Significance: All VBM variants had specific diagnostic properties that need to be considered for an adequate interpretation of the results. Overall, structural postprocessing can be a useful tool in presurgical diagnostics, but the low specificity of some maps has to be taken into consideration.
VBM lesion detection depends on the normalization template: a study using simulated atrophy
Magnetic Resonance Imaging, 2007
Structural neuroimaging studies are of great interest for neuroscientists, which are reflected in the rising number of papers using voxelbased morphometry (VBM). One major step in VBM is the transformation of images to a standard template, a spatial normalization necessary to ensure that homologous regions are compared while interindividual characteristics are maintained. Templates can be created in different ways, and this may affect the likelihood that differences in gray/white matter density between groups are detected. However, studies investigating the interaction of normalization template and VBM accuracy are sparse. Existing work is based on patient-control group comparisons, and the emerging results are inconclusive. The present paper therefore used simulated atrophy in a simplified one-lesion model to systematically study template effects of VBM analyses implemented in SPM. This allowed us to characterize template-specific biases in reference to a set of prespecified parameters of anatomical difference. The data suggest that the likelihood of correctly detecting the prespecified lesion is modulated by the normalization template. Thereby, the relationship between template-related VBM accuracy and specific group/study characteristics is complex, and there does not appear to be one dbest template.T Our data show that template effects are critical and clearly suggest that the choice of template needs careful consideration in relation to the specific research question and study constraints. D
Epilepsy & Behavior, 2018
The aim of this study was to automatically detect focal cortical dysplasia (FCD) lesions in patients with extratemporal lobe epilepsy by relying on diffusion tensor imaging (DTI) and T2-weighted magnetic resonance imaging (MRI) data. We implemented an automated classifier using voxel-based multimodal features to identify gray and white matter abnormalities of FCD in patient cohorts. In addition to the commonly used T2-weighted image intensity feature, DTI-based features were also utilized. A Gaussian processes for machine learning (GPML) classifier was tested on 12 patients with FCD (8 with histologically confirmed FCD) scanned at 1.5 T and cross-validated using a leave-one-out strategy. Moreover, we compared the multimodal GPML paradigm's performance with that of single modal GPML and classical support vector machine (SVM). Our results demonstrated that the GPML performance on DTI-based features (mean AUC = 0.63) matches with the GPML performance on T2-weighted image intensity feature (mean AUC = 0.64). More promisingly, GPML yielded significantly improved performance (mean AUC = 0.76) when applying DTI-based features to multimodal paradigm. Based on the results, it can also be clearly stated that the proposed GPML strategy performed better and is robust to unbalanced dataset contrary to SVM that performed poorly (AUC = 0.69). Therefore, the GPML paradigm using multimodal MRI data containing DTI modality has promising result towards detection of the FCD lesions and provides an effective direction for future researches.
Neuroimage, 2005
This paper presents a method for fully automated detection and localization of Focal Cortical Dysplastic (FCD) lesions from anatomical magnetic resonance (MR) images of the human brain. Model-based tissue classification of the image under study was applied first such that a gray matter (GM) segmentation map is obtained of which we demonstrate that it also includes possible FCD lesions. Cortical thickness was estimated at each voxel using an appropriate distance transform applied to the binarized GM object and an FCD specific feature map was constructed by computing the ratio of cortical thickness over absolute image intensity gradient at each voxel. In absence of any prior anatomical hypothesis on the spatial location of the lesion, a statistical parameter map was constructed by evaluating the evidence for each gray matter voxel against the null hypothesis of no difference in the feature map of the patient versus similar maps obtained for a group of normal controls. Voxel clusters for which the null hypothesis was found to be improbable at optimally selected thresholds for cluster height and extent were reported as lesions. The method was applied to a surgical series of 17 FCD patient images that were compared against a group of 64 neurologically normal controls. The method correctly detected and located the FCD lesion in 9 out of 17 FCD cases (53%) using a threshold that minimized false positives and 12 of 17 (71%) using a threshold that allowed more false positive results. The detected lesions had a median volume of 7.2 cm 3 versus 2.9 cm 3 for the non-detected lesions. The detected lesions more often had an increased cortical thickness on T1 than the non-detected lesions (P = 0.015, Fisher's exact test). Due to a high variance of the feature maps in the temporal lobes and insula, detection of FCD lesions in these regions appeared more difficult than in other brain regions with lower variance. D