Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach

Focal cortical dysplasia (FCD) is a leading cause of drug-resistant focal epilepsy, which can be cured by surgery. These lesions are extremely subtle and often missed even by expert neuroradiologists. "Ground truth" manual lesion masks are therefore expensive, limited and have large inter-rater variability. Existing FCD detection methods are limited by high numbers of false positive predictions, primarily due to vertex- or patch-based approaches that lack whole-brain context. Here, we propose to approach the problem as semantic segmentation using graph convolutional networks (GCN), which allows our model to learn spatial relationships between brain regions. To address the specific challenges of FCD identification, our proposed model includes an auxiliary loss to predict distance from the lesion to reduce false positives and a weak supervision classification loss to facilitate learning from uncertain lesion masks. On a multi-centre dataset of 1015 participants with surface-based features and manual lesion masks from structural MRI data, the proposed GCN achieved an AUC of 0.74, a significant improvement against a previously used vertex-wise multi-layer perceptron (MLP) classifier (AUC 0.64). With sensitivity thresholded at 67%, the GCN had a specificity of 71% in comparison to 49% when using the MLP. This improvement in specificity is vital for clinical integration of lesion-detection tools into the radiological workflow, through increasing clinical confidence in the use of AI radiological adjuncts and reducing the number of areas requiring expert review.Comment: accepted at MICCAI 202

Correction d'inhomogénéités de champs pour la SWI non-cartésienne par estimation des cartes de champs

International audiencePatient-induced inhomogeneities in the magnetic field cause distortions and blurring during acquisitions with long echo times, as in susceptibility-weighted imaging. Most correction methods require collecting an additional ΔB0 field map. To avoid that, we propose a method to approximate this field map using the single echo acquisition only. The main component of the observed phase is linearly related to ΔB0 and TE, and the relative impact of non-ΔB0 terms becomes insignificant with TE>20ms at 3T. The estimated 3D field maps, produced at 0.6 mm isotropic under 3 minutes, provide equivalent corrections to acquired ones.Les inhomogénéités de champs induites par les patients sont à l'origine de distorsions et de floutages durant les acquisitions à temps d'écho longs, comme pour l'imagerie pondérée en susceptibilité. La plupart des méthodes de correction nécessitent d'acquérir une carte de champ ΔB0 additionnelle. Pour éviter cela, nous proposons une méthode pour approximer cette carte de champs en utilisant seulement l'acquisition à écho unique. La composante principale de la phase observée est linéairement liée au ΔB0 et au TE, et l'impact relatif des termes indépendants du ΔB0 deviennent négligeables pour TE>20ms à 3T. Les cartes 3D estimées, produites à 0.6 mm isotrope en moins de 3 minutes, permettent d'obtenir une correction équivalente aux cartes acquises

Extent of piriform cortex resection in children with temporal lobe epilepsy

OBJECTIVE: A greater extent of resection of the temporal portion of the piriform cortex (PC) has been shown to be associated with higher likelihood of seizure freedom in adults undergoing anterior temporal lobe resection (ATLR) for drug-resistant temporal lobe epilepsy (TLE). There have been no such studies in children, therefore this study aimed to investigate this association in a pediatric cohort. METHODS: A retrospective, neuroimaging cohort study of children with TLE who underwent ATLR between 2012 and 2021 was undertaken. The PC, hippocampal and amygdala volumes were measured on the preoperative and postoperative T1-weighted MRI. Using these volumes, the extent of resection per region was compared between the seizure-free and not seizure-free groups. RESULTS: In 50 children (median age 9.5 years) there was no significant difference between the extent of resection of the temporal PC in the seizure-free (median = 50%, n = 33/50) versus not seizure-free (median = 40%, n = 17/50) groups (p = 0.26). In a sub-group of 19 with ipsilateral hippocampal atrophy (quantitatively defined by ipsilateral-to-contralateral asymmetry), the median extent of temporal PC resection was greater in children who were seizure-free (53%) versus those not seizure-free (19%) (p = 0.009). INTERPRETATION: This is the first study demonstrating that, in children with TLE and hippocampal atrophy, more extensive temporal PC resection is associated with a greater chance of seizure freedom-compatible with an adult series in which 85% of patients had hippocampal sclerosis. In a combined group of children with and without hippocampal atrophy, the extent of PC resection was not associated with seizure outcome, suggesting different epileptogenic networks within this cohort

Interpretable surface-based detection of focal cortical dysplasias:a Multi-centre Epilepsy Lesion Detection study

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy

Correction d'inhomogénéités de champs pour la SWI non-cartésienne par estimation des cartes de champs

International audiencePatient-induced inhomogeneities in the magnetic field cause distortions and blurring during acquisitions with long echo times, as in susceptibility-weighted imaging. Most correction methods require collecting an additional ΔB0 field map. To avoid that, we propose a method to approximate this field map using the single echo acquisition only. The main component of the observed phase is linearly related to ΔB0 and TE, and the relative impact of non-ΔB0 terms becomes insignificant with TE>20ms at 3T. The estimated 3D field maps, produced at 0.6 mm isotropic under 3 minutes, provide equivalent corrections to acquired ones.Les inhomogénéités de champs induites par les patients sont à l'origine de distorsions et de floutages durant les acquisitions à temps d'écho longs, comme pour l'imagerie pondérée en susceptibilité. La plupart des méthodes de correction nécessitent d'acquérir une carte de champ ΔB0 additionnelle. Pour éviter cela, nous proposons une méthode pour approximer cette carte de champs en utilisant seulement l'acquisition à écho unique. La composante principale de la phase observée est linéairement liée au ΔB0 et au TE, et l'impact relatif des termes indépendants du ΔB0 deviennent négligeables pour TE>20ms à 3T. Les cartes 3D estimées, produites à 0.6 mm isotrope en moins de 3 minutes, permettent d'obtenir une correction équivalente aux cartes acquises

Simultaneous proton density, T1 , T2 , and flip‐angle mapping of the brain at 7 T using multiparametric 3D SSFP imaging and parallel‐transmission universal pulses

International audiencePurpose: Performing simultaneous quantitative MRI at ultra-high field is challenging, as B0 and B1 + heterogeneities as well as Specific Absorption Rate increase. Too large deviations of flip angle from the target can induce biases and impair signal-to-noise ratio in the quantification process. In this work, we use calibration-free parallel transmission, a dedicated pulse sequence parameter optimization and signal fitting to recover 3D proton density, flip angle, T1 and T2 maps over the whole brain, in a clinically suitable time. Methods: Eleven optimized contrasts were acquired with an unbalanced Steady-State Free Precession sequence by varying flip angle amplitude and radiofrequency phase cycling increment, at a 1.0x1.0x3.0mm 3 resolution. Acquisition time was of 16min36sec for the whole brain. Parallel transmission and Universal Pulses were used to mitigate B1 + heterogeneity to improve the results' reliability over six healthy volunteers (3 females/males, age 22.6±2.7 years-old). Quantification of the physical parameters was performed by fitting acquired contrasts to the simulated ones using the Bloch-Torrey equations with a realistic diffusion coefficient. Results: Whole-brain 3D maps of effective flip angle, PD and relaxation times were estimated. Parallel transmission improved the robustness of the results at 7T. Results were in accordance with literature and with measurements from standard methods. Conclusion: These preliminary results show robust PD, FA, T1 and T2 map retrieval. Other parameters, such as Apparent Diffusion Coefficient, could be assessed. With further optimization in the acquisition, scan time could be reduced and spatial resolution increased to bring this multi-parametric quantification method to clinical research routine at 7-tesla

Extent of piriform cortex resection in children with temporal lobe epilepsy

Abstract Objective A greater extent of resection of the temporal portion of the piriform cortex (PC) has been shown to be associated with higher likelihood of seizure freedom in adults undergoing anterior temporal lobe resection (ATLR) for drug‐resistant temporal lobe epilepsy (TLE). There have been no such studies in children, therefore this study aimed to investigate this association in a pediatric cohort. Methods A retrospective, neuroimaging cohort study of children with TLE who underwent ATLR between 2012 and 2021 was undertaken. The PC, hippocampal and amygdala volumes were measured on the preoperative and postoperative T1‐weighted MRI. Using these volumes, the extent of resection per region was compared between the seizure‐free and not seizure‐free groups. Results In 50 children (median age 9.5 years) there was no significant difference between the extent of resection of the temporal PC in the seizure‐free (median = 50%, n = 33/50) versus not seizure‐free (median = 40%, n = 17/50) groups (p = 0.26). In a sub‐group of 19 with ipsilateral hippocampal atrophy (quantitatively defined by ipsilateral‐to‐contralateral asymmetry), the median extent of temporal PC resection was greater in children who were seizure‐free (53%) versus those not seizure‐free (19%) (p = 0.009). Interpretation This is the first study demonstrating that, in children with TLE and hippocampal atrophy, more extensive temporal PC resection is associated with a greater chance of seizure freedom—compatible with an adult series in which 85% of patients had hippocampal sclerosis. In a combined group of children with and without hippocampal atrophy, the extent of PC resection was not associated with seizure outcome, suggesting different epileptogenic networks within this cohort

Predicting seizure outcome after epilepsy surgery: do we need more complex models, larger samples, or better data?

OBJECTIVE: The accurate prediction of seizure freedom after epilepsy surgery remains challenging. We investigated if 1) training more complex models, 2) recruiting larger sample sizes, or 3) using data-driven selection of clinical predictors would improve our ability to predict post-operative seizure outcome using clinical features. We also conducted the first substantial external validation of a machine learning model trained to predict post-operative seizure outcome. METHODS: We performed a retrospective cohort study of 797 children who had undergone resective or disconnective epilepsy surgery at a tertiary center. We extracted patient information from medical records and trained three models - a logistic regression, a multilayer perceptron, and an XGBoost model - to predict one-year post-operative seizure outcome on our dataset. We evaluated the performance of a recently published XGBoost model on the same patients. We further investigated the impact of sample size on model performance, using learning curve analysis to estimate performance at samples up to N=2,000. Finally, we examined the impact of predictor selection on model performance. RESULTS: Our logistic regression achieved an accuracy of 72% (95% CI=68-75%,AUC=0.72), while our multilayer perceptron and XGBoost both achieved accuracies of 71% (95% CIMLP =67-74%,AUCMLP =0.70; 95% CIXGBoost own =68-75%,AUCXGBoost own =0.70). There was no significant difference in performance between our three models (all P>0.4) and they all performed better than the external XGBoost, which achieved an accuracy of 63% (95% CI=59-67%,AUC=0.62; PLR =0.005,PMLP =0.01,PXGBoost own =0.01) on our data. All models showed improved performance with increasing sample size, but limited improvements beyond our current sample. The best model performance was achieved with data-driven feature selection. SIGNIFICANCE: We show that neither the deployment of complex machine learning models nor the assembly of thousands of patients alone is likely to generate significant improvements in our ability to predict post-operative seizure freedom. We instead propose that improved feature selection alongside collaboration, data standardization, and model sharing is required to advance the field

Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted \u27gold-standard\u27 subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy

Atlas of lesion locations and postsurgical seizure freedom in focal cortical dysplasia: A MELD study

OBJECTIVE: Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome. METHODS: The MELD (Multi-centre Epilepsy Lesion Detection) project collated a retrospective cohort of 580 patients with epilepsy attributed to FCD from 20 epilepsy centers worldwide. Magnetic resonance imaging-based maps of individual FCDs with accompanying demographic, clinical, and surgical information were collected. We mapped the distribution of FCDs, examined for associations between clinical factors and lesion location, and developed a predictive model of postsurgical seizure freedom. RESULTS: FCDs were nonuniformly distributed, concentrating in the superior frontal sulcus, frontal pole, and temporal pole. Epilepsy onset was typically before the age of 10 years. Earlier epilepsy onset was associated with lesions in primary sensory areas, whereas later epilepsy onset was associated with lesions in association cortices. Lesions in temporal and occipital lobes tended to be larger than frontal lobe lesions. Seizure freedom rates varied with FCD location, from around 30% in visual, motor, and premotor areas to 75% in superior temporal and frontal gyri. The predictive model of postsurgical seizure freedom had a positive predictive value of 70% and negative predictive value of 61%. SIGNIFICANCE: FCD location is an important determinant of its size, the age at epilepsy onset, and the likelihood of seizure freedom postsurgery. Our atlas of lesion locations can be used to guide the radiological search for subtle lesions in individual patients. Our atlas of regional seizure freedom rates and associated predictive model can be used to estimate individual likelihoods of postsurgical seizure freedom. Data-driven atlases and predictive models are essential for evidence-based, precision medicine and risk counseling in epilepsy