Anatomy-based reconstruction of FDG-PET images with implicit partial volume correction improves detection of hypometabolic regions in patients with epilepsy due to focal cortical dysplasia diagnosed on MRI

Epilepsy surgery in children with operculoinsular epilepsy: Results of a large unicentric cohort

OBJECTIVE

Epilepsy surgery in the operculoinsular cortex is challenging due to the difficult delineation of the epileptogenic zone and the high risk of postoperative deficits.

METHODS

Pre- and postsurgical data from 30 pediatric patients who underwent operculoinsular cortex surgery at the Motol Epilepsy Center Prague from 2010 to 2022 were analyzed.

RESULTS

Focal cortical dysplasia (FCD; n = 15, 50%) was the predominant cause of epilepsy, followed by epilepsy-associated tumors (n = 5, 17%) and tuberous sclerosis complex (n = 2, 7%). In eight patients where FCD was the most likely etiology, the histology was negative. Seven patients (23%) displayed normal magnetic resonance imaging results. Seizures exhibited diverse semiology and propagation patterns (frontal, perisylvian, and temporal). The ictal and interictal electroencephalographic (EEG) findings were mostly extensive. Multimodal imaging and advanced postprocessing were frequently used. Stereo-EEG was used for localizing the epileptogenic zone and eloquent cortex in 23 patients (77%). Oblique electrodes were used as guides for better neurosurgeon orientation. The epileptogenic zone was in the dominant hemisphere in 16 patients. At the 2-year follow-up, 22 patients (73%) were completely seizure-free, and eight (27%) experienced a seizure frequency reduction of >50% (International League Against Epilepsy class 3 and 4). Fourteen patients (47%) underwent antiseizure medication tapering; treatment was completely withdrawn in two (7%). Nineteen patients (63%) remained seizure-free following the definitive outcome assessment (median = 6 years 5 months, range = 2 years to 13 years 5 months postsurgery). Six patients (20%) experienced corona radiata or basal ganglia ischemia; four (13%) improved to mild and one (3%) to moderate hemiparesis. Two patients (7%) operated on in the anterior insula along with frontotemporal resection experienced major complications: pontine ischemia and postoperative brain edema.

SIGNIFICANCE

Epilepsy surgery in the operculoinsular cortex can lead to excellent patient outcomes. A comprehensive diagnostic approach is crucial for surgical success. Rehabilitation brings a great chance for significant recovery of postoperative deficits.

Brain PET Imaging in the Presurgical Evaluation of Drug-Resistant Focal Epilepsy

Presurgical evaluation aims to localize the seizure onset zone (SOZ) for a tailored resection. Interictal [18F]fluorodeoxyglucose PET is now an established test to lateralize and/or localize the SOZ, particularly if MR imaging is negative or if the noninvasive assessment shows discrepancies. PET can show hypometabolic areas associated with SOZ and the potential altered metabolic brain networks. It is very sensitive, and this is increased if images are read coregistered to the patient's MR imaging. PET hypometabolic intensity and pattern show prognostic value.

EANM practice guidelines for an appropriate use of PET and SPECT for patients with epilepsy

Epilepsy is one of the most frequent neurological conditions with an estimated prevalence of more than 50 million people worldwide and an annual incidence of two million. Although pharmacotherapy with anti-seizure medication (ASM) is the treatment of choice, ~30% of patients with epilepsy do not respond to ASM and become drug resistant. Focal epilepsy is the most frequent form of epilepsy. In patients with drug-resistant focal epilepsy, epilepsy surgery is a treatment option depending on the localisation of the seizure focus for seizure relief or seizure freedom with consecutive improvement in quality of life. Beside examinations such as scalp video/electroencephalography (EEG) telemetry, structural, and functional magnetic resonance imaging (MRI), which are primary standard tools for the diagnostic work-up and therapy management of epilepsy patients, molecular neuroimaging using different radiopharmaceuticals with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) influences and impacts on therapy decisions. To date, there are no literature-based praxis recommendations for the use of Nuclear Medicine (NM) imaging procedures in epilepsy. The aims of these guidelines are to assist in understanding the role and challenges of radiotracer imaging for epilepsy; to provide practical information for performing different molecular imaging procedures for epilepsy; and to provide an algorithm for selecting the most appropriate imaging procedures in specific clinical situations based on current literature. These guidelines are written and authorized by the European Association of Nuclear Medicine (EANM) to promote optimal epilepsy imaging, especially in the presurgical setting in children, adolescents, and adults with focal epilepsy. They will assist NM healthcare professionals and also specialists such as Neurologists, Neurophysiologists, Neurosurgeons, Psychiatrists, Psychologists, and others involved in epilepsy management in the detection and interpretation of epileptic seizure onset zone (SOZ) for further treatment decision. The information provided should be applied according to local laws and regulations as well as the availability of various radiopharmaceuticals and imaging modalities.

Cortical surface analysis for focal cortical dysplasia diagnosis by using PET images

Focal cortical dysplasia (FCD) is a neurological disorder distinguished by faulty brain cell structure and development. Repetitive and uncontrollable seizures may be linked to FCD's aberrant cortical thickness, gyrification, and sulcal depth. Quantitative cortical surface analysis is a crucial alternative to ineffective visual inspection. This study recruited 42 subjects including 22 FCD patients who underwent surgery and 20 healthy controls (HC). For the FCD patients, T1-weighted and PET images were obtained by a PET-MRI scanner, and the confirmed epileptogenic zone (EZ) was collected from postsurgical follow-up. For the HCs, CT and PET images were obtained by a PET-CT scanner. Cortical thickness, gyrification index, and sulcal depth were calculated using a computational anatomical toolbox (CAT12). A cluster-based analysis is carried out to determine each FCD patient's aberrant cortical surface. After parcellating the cerebral cortex into 68 regions by the Desikan-Killiany atlas, a region of interest (ROI) analysis was conducted to know whether the feature in the FCD group is significantly different from that in the HC group. Finally, the features of all ROIs were utilised to train a support vector machine classifier (SVM). The classification performance is evaluated by the leave-one-out cross-validation. The cluster-based analysis can localize the EZ cluster with the highest accuracy of 54.5 % (12/22) for cortical thickness, 40.9 % (9/22) and 13.6 % (3/22) for sulcal depth and gyrification, respectively. Moderate concordance (Kappa, 0.6) is observed between the confirmed EZs and identified clusters by using the cortical thickness. Fair concordance (Kappa, 0.3) and no concordance (Kappa, 0.1) is found by using sulcal depth and gyrification. Significant differences are found in 46 of 68 regions (67.7 %) for the three measures. The trained SVM classifier achieved a prediction accuracy of 95.5 % for the cortical thickness, while the sulcal depth and the gyrification obtained 86.0 % and 81.5 %. Cortical thickness, as determined by quantitative cortical surface analysis of PET data, has a greater ability than sulcal depth and gyrification to locate aberrant EZ clusters in FCD. Surface measures might be different in many regions for FCD and HC. By integrating machine learning and cortical morphologies features, individual prediction of FCD seems to be feasible.

Distinct patterns of interictal intracranial EEG in focal cortical dysplasia type I and II

OBJECTIVE

Focal cortical dysplasia (FCD) is the most common malformation causing refractory focal epilepsy. Surgical removal of the entire dysplastic cortex is crucial for achieving a seizure-free outcome. Precise presurgical distinctions between FCD types by neuroimaging are difficult, mainly in patients with normal magnetic resonance imaging findings. However, the FCD type is important for planning the extent of surgical approach and counselling.

METHODS

This study included patients with focal drug-resistant epilepsy and definite histopathological FCD type I or II diagnoses who underwent intracranial electroencephalography (iEEG). We detected interictal epileptiform discharges (IEDs) and their recruitment into repetitive discharges (RDs) to compare electrophysiological patterns characterizing FCD types.

RESULTS

Patients with FCD type II had a significantly higher IED rate (p < 0.005), a shorter inter-discharge interval within RD episodes (p < 0.003), sleep influence on decreased RD periodicity (p < 0.036), and longer RD episode duration (p < 0.003) than patients with type I. A Bayesian classifier stratified FCD types with 82% accuracy.

CONCLUSION

Temporal characteristics of IEDs and RDs reflect the histological findings of FCD subtypes and can differentiate FCD types I and II.

SIGNIFICANCE

Presurgical prediction of FCD type can help to plan a more tailored surgical approach in patients with normal magnetic resonance findings.

A cycle-consistent adversarial network for brain PET partial volume correction without prior anatomical information

PURPOSE

Partial volume effect (PVE) is a consequence of the limited spatial resolution of PET scanners. PVE can cause the intensity values of a particular voxel to be underestimated or overestimated due to the effect of surrounding tracer uptake. We propose a novel partial volume correction (PVC) technique to overcome the adverse effects of PVE on PET images.

METHODS

Two hundred and twelve clinical brain PET scans, including 50 18F-Fluorodeoxyglucose (18F-FDG), 50 18F-Flortaucipir, 36 18F-Flutemetamol, and 76 18F-FluoroDOPA, and their corresponding T1-weighted MR images were enrolled in this study. The Iterative Yang technique was used for PVC as a reference or surrogate of the ground truth for evaluation. A cycle-consistent adversarial network (CycleGAN) was trained to directly map non-PVC PET images to PVC PET images. Quantitative analysis using various metrics, including structural similarity index (SSIM), root mean squared error (RMSE), and peak signal-to-noise ratio (PSNR), was performed. Furthermore, voxel-wise and region-wise-based correlations of activity concentration between the predicted and reference images were evaluated through joint histogram and Bland and Altman analysis. In addition, radiomic analysis was performed by calculating 20 radiomic features within 83 brain regions. Finally, a voxel-wise two-sample t-test was used to compare the predicted PVC PET images with reference PVC images for each radiotracer.

RESULTS

The Bland and Altman analysis showed the largest and smallest variance for 18F-FDG (95% CI: - 0.29, + 0.33 SUV, mean = 0.02 SUV) and 18F-Flutemetamol (95% CI: - 0.26, + 0.24 SUV, mean =  - 0.01 SUV), respectively. The PSNR was lowest (29.64 ± 1.13 dB) for 18F-FDG and highest (36.01 ± 3.26 dB) for 18F-Flutemetamol. The smallest and largest SSIM were achieved for 18F-FDG (0.93 ± 0.01) and 18F-Flutemetamol (0.97 ± 0.01), respectively. The average relative error for the kurtosis radiomic feature was 3.32%, 9.39%, 4.17%, and 4.55%, while it was 4.74%, 8.80%, 7.27%, and 6.81% for NGLDM_contrast feature for 18F-Flutemetamol, 18F-FluoroDOPA, 18F-FDG, and 18F-Flortaucipir, respectively.

CONCLUSION

An end-to-end CycleGAN PVC method was developed and evaluated. Our model generates PVC images from the original non-PVC PET images without requiring additional anatomical information, such as MRI or CT. Our model eliminates the need for accurate registration or segmentation or PET scanner system response characterization. In addition, no assumptions regarding anatomical structure size, homogeneity, boundary, or background level are required.

PET image enhancement using artificial intelligence for better characterization of epilepsy lesions

INTRODUCTION

[18F]fluorodeoxyglucose ([18F]FDG) brain PET is used clinically to detect small areas of decreased uptake associated with epileptogenic lesions, e.g., Focal Cortical Dysplasias (FCD) but its performance is limited due to spatial resolution and low contrast. We aimed to develop a deep learning-based PET image enhancement method using simulated PET to improve lesion visualization.

METHODS

We created 210 numerical brain phantoms (MRI segmented into 9 regions) and assigned 10 different plausible activity values (e.g., GM/WM ratios) resulting in 2100 ground truth high quality (GT-HQ) PET phantoms. With a validated Monte-Carlo PET simulator, we then created 2100 simulated standard quality (S-SQ) [18F]FDG scans. We trained a ResNet on 80% of this dataset (10% used for validation) to learn the mapping between S-SQ and GT-HQ PET, outputting a predicted HQ (P-HQ) PET. For the remaining 10%, we assessed Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Root Mean Squared Error (RMSE) against GT-HQ PET. For GM and WM, we computed recovery coefficients (RC) and coefficient of variation (COV). We also created lesioned GT-HQ phantoms, S-SQ PET and P-HQ PET with simulated small hypometabolic lesions characteristic of FCDs. We evaluated lesion detectability on S-SQ and P-HQ PET both visually and measuring the Relative Lesion Activity (RLA, measured activity in the reduced-activity ROI over the standard-activity ROI). Lastly, we applied our previously trained ResNet on 10 clinical epilepsy PETs to predict the corresponding HQ-PET and assessed image quality and confidence metrics.

RESULTS

Compared to S-SQ PET, P-HQ PET improved PNSR, SSIM and RMSE; significatively improved GM RCs (from 0.29 ± 0.03 to 0.79 ± 0.04) and WM RCs (from 0.49 ± 0.03 to 1 ± 0.05); mean COVs were not statistically different. Visual lesion detection improved from 38 to 75%, with average RLA decreasing from 0.83 ± 0.08 to 0.67 ± 0.14. Visual quality of P-HQ clinical PET improved as well as reader confidence.

CONCLUSION

P-HQ PET showed improved image quality compared to S-SQ PET across several objective quantitative metrics and increased detectability of simulated lesions. In addition, the model generalized to clinical data. Further evaluation is required to study generalization of our method and to assess clinical performance in larger cohorts.

Anatomy-guided PET reconstruction usingl1bowsher prior

Advances in simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI) technology have led to an active investigation of the anatomy-guided regularized PET image reconstruction algorithm based on MR images. Among the various priors proposed for anatomy-guided regularized PET image reconstruction, Bowsher's method based on second-order smoothing priors sometimes suffers from over-smoothing of detailed structures. Therefore, in this study, we propose a Bowsher prior based on thel1-norm and an iteratively reweighting scheme to overcome the limitation of the original Bowsher method. In addition, we have derived a closed solution for iterative image reconstruction based on this non-smooth prior. A comparison study between the originall2and proposedl1Bowsher priors was conducted using computer simulation and real human data. In the simulation and real data application, small lesions with abnormal PET uptake were better detected by the proposedl1Bowsher prior methods than the original Bowsher prior. The originall2Bowsher leads to a decreased PET intensity in small lesions when there is no clear separation between the lesions and surrounding tissue in the anatomical prior. However, the proposedl1Bowsher prior methods showed better contrast between the tumors and surrounding tissues owing to the intrinsic edge-preserving property of the prior which is attributed to the sparseness induced byl1-norm, especially in the iterative reweighting scheme. Besides, the proposed methods demonstrated lower bias and less hyper-parameter dependency on PET intensity estimation in the regions with matched anatomical boundaries in PET and MRI. Therefore, these methods will be useful for improving the PET image quality based on the anatomical side information.

Volumetric and Functional Activity Lateralization in Healthy Subjects and Patients with Focal Epilepsy: Initial Findings in a 7T MRI Study

BACKGROUND AND PURPOSE

In 30% of the patients with focal epilepsy, an epileptogenic lesion cannot be visually detected with structural MRI. Ultra-high field MRI may be able to identify subtle pathology related to the epileptic focus. We set out to assess 7T MRI-derived volumetric and functional activity lateralization of the hippocampus, hippocampal subfields, temporal and frontal lobe in healthy subjects and MRI-negative patients with focal epilepsy.

METHODS

Twenty controls and 10 patients with MRI-negative temporal or frontal lobe epilepsy (TLE and FLE, respectively) underwent a 7T MRI exam. T1 -weigthed imaging and resting-state fMRI was performed. T1 -weighted images were segmented to yield volumes, while from fMRI data, the fractional amplitude of low frequency fluctuations was calculated. Subsequently, volumetric and functional lateralization was calculated from left-right asymmetry.

RESULTS

In controls, volumetric lateralization was symmetric, with a slight asymmetry of the hippocampus and subiculum, while functional lateralization consistently showed symmetry. Contrarily, in epilepsy patients, regions were less symmetric. In TLE patients with known focus, volumetric lateralization in the hippocampus and hippocampal subfields was indicative of smaller ipsilateral volumes. These patients also showed clear functional lateralization, though not consistently ipsilateral or contralateral to the epileptic focus. TLE patients with unknown focus showed an obvious volumetric lateralization, facilitating the localization of the epileptic focus. Lateralization results in the FLE patients were less consistent with the epileptic focus.

CONCLUSION

MRI-derived volume and fluctuation amplitude are highly symmetric in controls, whereas in TLE, volumetric and functional lateralization effects were observed. This highlights the potential of the technique.

Structural and functional asymmetry of medial temporal subregions in unilateral temporal lobe epilepsy: A 7T MRI study

Mesial temporal lobe epilepsy (TLE) is a common neurological disorder affecting the hippocampus and surrounding medial temporal lobe (MTL). Although prior studies have analyzed whole-brain network distortions in TLE patients, the functional network architecture of the MTL at the subregion level has not been examined. In this study, we utilized high-resolution 7T T2-weighted magnetic resonance imaging (MRI) and resting-state BOLD-fMRI to characterize volumetric asymmetry and functional network asymmetry of MTL subregions in unilateral medically refractory TLE patients and healthy controls. We subdivided the TLE group into mesial temporal sclerosis patients (TLE-MTS) and MRI-negative nonlesional patients (TLE-NL). Using an automated multi-atlas segmentation pipeline, we delineated 10 MTL subregions per hemisphere for each subject. We found significantly different patterns of volumetric asymmetry between the two groups, with TLE-MTS exhibiting volumetric asymmetry corresponding to decreased volumes ipsilaterally in all hippocampal subfields, and TLE-NL exhibiting no significant volumetric asymmetries other than a mild decrease in whole-hippocampal volume ipsilaterally. We also found significantly different patterns of functional network asymmetry in the CA1 subfield and whole hippocampus, with TLE-NL patients exhibiting asymmetry corresponding to increased connectivity ipsilaterally and TLE-MTS patients exhibiting asymmetry corresponding to decreased connectivity ipsilaterally. Our findings provide initial evidence that functional neuroimaging-based network properties within the MTL can distinguish between TLE subtypes. High-resolution MRI has potential to improve localization of underlying brain network disruptions in TLE patients who are candidates for surgical resection.

Effect of PET-MR Inconsistency in the Kernel Image Reconstruction Method

Anatomically-driven image reconstruction algorithms have become very popular in positron emission tomography (PET) where they have demonstrated improved image resolution and quantification. This work, consider the effect of spatial inconsistency between MR and PET images in hot and cold regions of the PET image. We investigate these effects on the kernel method from machine learning, in particular, the hybrid kernelized expectation maximization (HKEM). These were applied to Jaszczak phantom and patient data acquired with the Biograph Siemens mMR. The results show that even a small shift can cause a significant change in activity concentration. In general, the PET-MR inconsistencies can induce the partial volume effect, more specifically the 'spill-in' of the affected cold regions and the 'spill-out' from the hot regions. The maximum change was about 100% for the cold region and 10% for the hot lesion using KEM, against the 37% and 8% obtained with HKEM. The findings of this work suggest that including PET information in the kernel enhances the flexibility of the reconstruction in case of spatial inconsistency. Nevertheless, accurate registration and choice of the appropriate MR image for the creation of the kernel is essential to avoid artifacts, blurring, and bias.

Correlation of neuropsychological and metabolic changes after epilepsy surgery in patients with left mesial temporal lobe epilepsy with hippocampal sclerosis

Background

Epilepsy surgery often causes changes in cognition and cerebral glucose metabolism. Our aim was to explore relationships between pre- and postoperative cerebral metabolism as measured with ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET) and neuropsychological test scores in patients with left mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS), who were rendered seizure-free after epilepsy surgery.

Results

Thirteen patients were included. All had neuropsychological testing and an interictal FDG-PET scan of the brain pre- and postoperative. Correlations between changes in neuropsychological test scores and metabolism were examined using statistical parametric mapping (SPM). There were no significant changes in the neuropsychological test scores pre- and postoperatively at the group level. Decreased metabolism was observed in the left mesial temporal regions and occipital lobe. Increased metabolism was observed in the bi-frontal and right parietal lobes, temporal lobes, occipital lobes, thalamus, cerebellum, and vermis. In these regions, we did not find a correlation between changes in metabolism and neuropsychological test scores. A significant negative correlation, however, was found between metabolic changes in the precuneus and Boston Naming Test (BNT) scores.

Conclusions

There are significant metabolic decreases in the left mesial temporal regions and increases in the bi-frontal lobes; right parietal, temporal, and occipital lobes; right thalamus; cerebellum; and vermis in patients with left MTLE-HS who were rendered seizure-free after epilepsy surgery. We could not confirm that these changes translate into significant cognitive changes. A significant negative correlation was found between changes in confrontation naming and changes in metabolism in the precuneus. We speculate that the precuneus may play a compensatory role in patients with postoperative naming difficulties after left TLE surgery. Understanding of these neural mechanisms may aid in designing cognitive rehabilitation strategies.

Electronic supplementary material

The online version of this article (10.1186/s13550-018-0385-5) contains supplementary material, which is available to authorized users.

Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias

OBJECTIVE

Focal cortical dysplasias (FCDs) often cause pharmacoresistant epilepsy, and surgical resection can lead to seizure-freedom. Magnetic resonance imaging (MRI) and positron emission tomography (PET) play complementary roles in FCD identification/localization; nevertheless, many FCDs are small or subtle, and difficult to find on routine radiological inspection. We aimed to automatically detect subtle or visually-unidentifiable FCDs by building a classifier based on an optimized cortical surface sampling of combined MRI and PET features.

METHODS

Cortical surfaces of 28 patients with histopathologically-proven FCDs were extracted. Morphology and intensity-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface, and fed to a 2-step (Support Vector Machine and patch-based) classifier. Classifier performance was assessed compared to manual lesion labels.

RESULTS

Our classifier using combined feature selections from MRI and PET outperformed both quantitative MRI and multimodal visual analysis in FCD detection (93% vs 82% vs 68%). No false positives were identified in the controls, whereas 3.4% of the vertices outside FCD lesions were also classified to be lesional ("extralesional clusters"). Patients with type I or IIa FCDs displayed a higher prevalence of extralesional clusters at an intermediate distance to the FCD lesions compared to type IIb FCDs (p < 0.05). The former had a correspondingly lower chance of positive surgical outcome (71% vs 91%).

CONCLUSIONS

Machine learning with multimodal feature sampling can improve FCD detection. The spread of extralesional clusters characterize different FCD subtypes, and may represent structurally or functionally abnormal tissue on a microscopic scale, with implications for surgical outcomes.

Evaluation of Parallel Level Sets and Bowsher's Method as Segmentation-Free Anatomical Priors for Time-of-Flight PET Reconstruction

In this article, we evaluate Parallel Level Sets (PLS) and Bowsher's method as segmentation-free anatomical priors for regularized brain positron emission tomography (PET) reconstruction. We derive the proximity operators for two PLS priors and use the EM-TV algorithm in combination with the first order primal-dual algorithm by Chambolle and Pock to solve the non-smooth optimization problem for PET reconstruction with PLS regularization. In addition, we compare the performance of two PLS versions against the symmetric and asymmetric Bowsher priors with quadratic and relative difference penalty function. For this aim, we first evaluate reconstructions of 30 noise realizations of simulated PET data derived from a real hybrid positron emission tomography/magnetic resonance imaging (PET/MR) acquisition in terms of regional bias and noise. Second, we evaluate reconstructions of a real brain PET/MR data set acquired on a GE Signa time-of-flight PET/MR in a similar way. The reconstructions of simulated and real 3D PET/MR data show that all priors were superior to post-smoothed maximum likelihood expectation maximization with ordered subsets (OSEM) in terms of bias-noise characteristics in different regions of interest where the PET uptake follows anatomical boundaries. Our implementation of the asymmetric Bowsher prior showed slightly superior performance compared with the two versions of PLS and the symmetric Bowsher prior. At very high regularization weights, all investigated anatomical priors suffer from the transfer of non-shared gradients.

Computational analysis in epilepsy neuroimaging: A survey of features and methods

Epilepsy affects 65 million people worldwide, a third of whom have seizures that are resistant to anti-epileptic medications. Some of these patients may be amenable to surgical therapy or treatment with implantable devices, but this usually requires delineation of discrete structural or functional lesion(s), which is challenging in a large percentage of these patients.

Advances in neuroimaging and machine learning allow semi-automated detection of malformations of cortical development (MCDs), a common cause of drug resistant epilepsy. A frequently asked question in the field is what techniques currently exist to assist radiologists in identifying these lesions, especially subtle forms of MCDs such as focal cortical dysplasia (FCD) Type I and low grade glial tumors. Below we introduce some of the common lesions encountered in patients with epilepsy and the common imaging findings that radiologists look for in these patients. We then review and discuss the computational techniques introduced over the past 10 years for quantifying and automatically detecting these imaging findings. Due to large variations in the accuracy and implementation of these studies, specific techniques are traditionally used at individual centers, often guided by local expertise, as well as selection bias introduced by the varying prevalence of specific patient populations in different epilepsy centers. We discuss the need for a multi-institutional study that combines features from different imaging modalities as well as computational techniques to definitively assess the utility of specific automated approaches to epilepsy imaging. We conclude that sharing and comparing these different computational techniques through a common data platform provides an opportunity to rigorously test and compare the accuracy of these tools across different patient populations and geographical locations. We propose that these kinds of tools, quantitative imaging analysis methods and open data platforms for aggregating and sharing data and algorithms, can play a vital role in reducing the cost of care, the risks of invasive treatments, and improve overall outcomes for patients with epilepsy.

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We introduce common epileptogenic lesions encountered in patients with drug resistant epilepsy.

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We discuss state of the art computational techniques used to detect lesions.

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There is a need for multi-institutional studies that combine these techniques.

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Clinically validated pipelines alongside the advances in imaging and electrophysiology will improve outcomes.

Voxel-based comparison of state-of-the-art reconstruction algorithms for 18F-FDG PET brain imaging using simulated and clinical data

The resolution of a PET scanner (2.5-4.5mm for brain imaging) is similar to the thickness of the cortex in the (human) brain (2.5mm on average), hampering accurate activity distribution reconstruction. Many techniques to compensate for the limited resolution during or post-reconstruction have been proposed in the past and have been shown to improve the quantitative accuracy. In this study, state-of-the-art reconstruction techniques are compared on a voxel-basis for quantification accuracy and group analysis using both simulated and measured data of healthy volunteers and patients with epilepsy.

METHODS

Maximum a posteriori (MAP) reconstructions using either a segmentation-based or a segmentation-less anatomical prior were compared to maximum likelihood expectation maximization (MLEM) reconstruction with resolution recovery. As anatomical information, a spatially aligned 3D T1-weighted magnetic resonance image was used. Firstly, the algorithms were compared using normal brain images to detect systematic bias with respect to the true activity distribution, as well as systematic differences between two methods. Secondly, it was verified whether the algorithms yielded similar results in a group comparison study.

RESULTS

Significant differences were observed between the reconstructed and the true activity, with the largest errors when using (post-smoothed) MLEM. Only 5-10% underestimation in cortical gray matter voxel activity was found for both MAP reconstructions. Higher errors were observed at GM edges. MAP with the segmentation-based prior also resulted in a significant bias in the subcortical regions due to segmentation inaccuracies, while MAP with the anatomical prior which does not need segmentation did not. Significant differences in reconstructed activity were also found between the algorithms at similar locations (mainly in gray matter edge voxels and in cerebrospinal fluid voxels) in the simulated as well as in the clinical data sets. Nevertheless, when comparing two groups, very similar regions of significant hypometabolism were detected by all algorithms.

CONCLUSION

Including anatomical a priori information during reconstruction in combination with resolution modeling yielded accurate gray matter activity estimates, and a significant improvement in quantification accuracy was found when compared to post-smoothed MLEM reconstruction with resolution modeling. AsymBowsher provided the most accurate subcortical GM activity estimates. It is also reassuring that the differences found between the algorithms did not hamper the detection of hypometabolic regions in the gray matter when performing a voxel-based group comparison. Nevertheless, the size of the detected clusters differed. More elaborated and application-specific studies are required to decide which algorithm is best for a group analysis.

Focal cortical dysplasia

Focal cortical dysplasias are among the most common causes of intractable epilepsy in children. As the neuropathology of these conditions has been better clarified, the nomenclature has undergone numerous revisions. Their recognition has grown with the use of neuroimaging, and recent advances in imaging technology will further improve detection. Clinical, electroencephalographic, and imaging findings are often diagnostic, so it is imperative for the clinician to recognize the characteristic patterns. Treatment for developmental and behavioral disability remains largely symptomatic, and epilepsy medications are often ineffective. Epilepsy surgery, however, can be successful in selected patients. The basic science underlying the development of focal cortical dysplasias may lead to novel therapeutic approaches in the future.

The impact of positron emission tomography imaging on the clinical management of patients with epilepsy

Clinical positron emission tomography (PET) imaging of human epilepsy has a 30-year history, but it is still searching for its exact role among rapidly advancing neuroimaging techniques. The vast majority of epilepsy PET studies used this technique to improve detection of epileptic foci for surgical resection. Here, we review the main trends emerging from three decades of PET research in epilepsy, with a particular emphasis on how PET imaging has impacted on the clinical management of patients with intractable epilepsy. While reviewing the latest studies, we also present an argument for a changing role of PET and molecular imaging in the future, with an increasing focus on epileptogenesis and newly discovered molecular mechanisms of epilepsy. These new applications will be facilitated by technological advances, such as the use of integrated PET/MRI systems and utilization of novel radiotracers, which may also enhance phenotype-genotype correlations and assist rational, individualized treatment strategies.

Wavelet-based resolution recovery using an anatomical prior provides quantitative recovery for human population phantom PET [¹¹C]raclopride data

The objective of this study was to evaluate a resolution recovery (RR) method using a variety of simulated human brain [¹¹C]raclopride positron emission tomography (PET) images. Simulated datasets of 15 numerical human phantoms were processed by a wavelet-based RR method using an anatomical prior. The anatomical prior was in the form of a hybrid segmented atlas, which combined an atlas for anatomical labelling and a PET image for functional labelling of each anatomical structure. We applied RR to both 60 min static and dynamic PET images. Recovery was quantified in 84 regions, comparing the typical 'true' value for the simulation, as obtained in normal subjects, simulated and RR PET images. The radioactivity concentration in the white matter, striatum and other cortical regions was successfully recovered for the 60 min static image of all 15 human phantoms; the dependence of the solution on accurate anatomical information was demonstrated by the difficulty of the technique to retrieve the subthalamic nuclei due to mismatch between the two atlases used for data simulation and recovery. Structural and functional synergy for resolution recovery (SFS-RR) improved quantification in the caudate and putamen, the main regions of interest, from -30.1% and -26.2% to -17.6% and -15.1%, respectively, for the 60 min static image and from -51.4% and -38.3% to -27.6% and -20.3% for the binding potential (BP(ND)) image, respectively. The proposed methodology proved effective in the RR of small structures from brain [¹¹C]raclopride PET images. The improvement is consistent across the anatomical variability of a simulated population as long as accurate anatomical segmentations are provided.

Evaluation of three MRI-based anatomical priors for quantitative PET brain imaging

In emission tomography, image reconstruction and therefore also tracer development and diagnosis may benefit from the use of anatomical side information obtained with other imaging modalities in the same subject, as it helps to correct for the partial volume effect. One way to implement this, is to use the anatomical image for defining the a priori distribution in a maximum-a-posteriori (MAP) reconstruction algorithm. In this contribution, we use the PET-SORTEO Monte Carlo simulator to evaluate the quantitative accuracy reached by three different anatomical priors when reconstructing positron emission tomography (PET) brain images, using volumetric magnetic resonance imaging (MRI) to provide the anatomical information. The priors are: 1) a prior especially developed for FDG PET brain imaging, which relies on a segmentation of the MR-image (Baete , 2004); 2) the joint entropy-prior (Nuyts, 2007); 3) a prior that encourages smoothness within a position dependent neighborhood, computed from the MR-image. The latter prior was recently proposed by our group in (Vunckx and Nuyts, 2010), and was based on the prior presented by Bowsher (2004). The two latter priors do not rely on an explicit segmentation, which makes them more generally applicable than a segmentation-based prior. All three priors produced a compromise between noise and bias that was clearly better than that obtained with postsmoothed maximum likelihood expectation maximization (MLEM) or MAP with a relative difference prior. The performance of the joint entropy prior was slightly worse than that of the other two priors. The performance of the segmentation-based prior is quite sensitive to the accuracy of the segmentation. In contrast to the joint entropy-prior, the Bowsher-prior is easily tuned and does not suffer from convergence problems.

Focal cortical dysplasia. Clinical-radiological-pathological associations

INTRODUCTION

The term focal cortical dysplasia (FCD) describes a particular migration disorder with a symptomatology mainly characterised by drug-resistant epileptic seizures, typical neuroradiological images, and histological characteristics, as well as a very positive response to surgical treatment in the majority of cases.

MATERIAL AND METHODS

A total of 7 patients were studied, comprising 6 children with a mean age of 34.3 months and one 25-year-old male with very persistent focal seizures and MRI images that showed FCD.

RESULTS

Three of the patients (all girls) were operated on while very young, with extirpation of the FCD and the surrounding area; with the histopathology study showed agreement between the MRI images and the macroscopic study of the slices. The histology study showed findings typical of a Taylor-type FCD (poor differentiation between the cortical grey matter and the subcortical white matter, and balloon cells). Three years after the FCD extirpation, the same 3 patients remained seizure-free with no anti-epilepsy medication. Two others have seizure control with medication, another (the adult) is on the surgical waiting list, and the remaining patient refused the operation.

CONCLUSION

Taylor-type FCD is associated with a high percentage of all drug-resistant focal seizures, and it needs to be identified and extirpated as soon as possible. Well planned and well-performed surgery that leaves no remains of dysplasia can cure the disease it in many cases.

Validation of FDG-PET/MRI coregistration in nonlesional refractory childhood epilepsy

PURPOSE

To validate the use of 18F-fluorodeoxyglucose-positron emission tomography/magnetic resonance imaging (FDG-PET/MRI) coregistration for epileptogenic zone detection in children with MRI nonlesional refractory epilepsy and to assess its ability to guide a second interpretation of the MRI studies.

METHODS

Thirty-one children with refractory epilepsy whose MRI results were nonlesional were included prospectively. All patients underwent presurgical evaluation following the standard protocol of our epilepsy unit, which included FDG-PET and FDG-PET/MRI coregistration. Cerebral areas of decreased uptake in PET and PET/MRI fusion images were compared visually and then contrasted with presumed epileptogenic zone localization, which had been obtained from other clinical data. A second interpretation of MRI studies was carried out, focusing on the exact anatomic region in which hypometabolism was located in FDG-PET/MRI fusion images.

KEY FINDINGS

Both FDG-PET and FDG-PET/MRI detected hypometabolism in 67.8% of patients, with good concordance on a subject basis and on the cerebral region involved (κ statistic = 0.83 and 0.79, respectively). Hypometabolism detected by single PET, as well as by PET/MRI fusion images, was located in the same hemisphere, as indicated by electroclinical data in 58% of patients, and at the same place in 39% of cases. Of the patients who showed hypometabolism on PET/MRI, 43% also experienced changes in the guided second MRI interpretation, from nonlesional to subtle-lesional.

SIGNIFICANCE

PET/MRI coregistration is an imaging variant that is at least as accurate as PET alone in detecting epileptogenic zone in pediatric nonlesional patients, and can guide a second look at MRI studies previously reported as nonlesional, turning a meaningful percentage into subtle-lesional.

Spatiotemporal dynamics of epileptiform propagations: imaging of human brain slices

Seizure activities often originate from a localized region of the cerebral cortex and spread across large areas of the brain. The properties of these spreading abnormal discharges may account for clinical phenotypes in epilepsy patients, although the manner of their propagation and the underlying mechanisms are not well understood. In the present study we performed flavoprotein fluorescence imaging of cortical brain slices surgically resected from patients with partial epilepsy caused by various symptomatic lesions. Elicited neural activities in the epileptogenic tissue spread horizontally over the cortex momentarily, but those in control tissue taken from patients with brain tumors who had no history of epilepsy demonstrated only localized responses. Characteristically, the epileptiform propagation comprised early and late phases. When the stimulus intensity was changed gradually, the early phase showed an all-or-none behavior, whereas the late phase showed a gradual increase in the response. Moreover, the two phases were propagated through different cortical layers, suggesting that they are derived from distinct neural circuits. Morphological investigation revealed the presence of hypertrophic neurons and loss of dendritic spines, which might participate in the aberrant activities observed by flavoprotein fluorescence imaging. These findings indicate that synchronized activities of the early phase may play a key role in spreading abnormal discharges in human cortical epilepsies.