Improved detection of focal cortical dysplasia using a novel 3D imaging sequence: Edge-Enhancing Gradient Echo (3D-EDGE) MRI

Integrating standard epilepsy protocol, ASL-perfusion, MP2RAGE/EDGE and the MELD-FCD classifier in the detection of subtle epileptogenic lesions: a 3 Tesla MRI pilot study

BACKGROUND

Malformations of cortical development (MCDs) in children with focal epilepsy pose significant diagnostic challenges, and a precise radiological diagnosis is crucial for surgical planning. New MRI sequences and the use of artificial intelligence (AI) algorithms are considered very promising in this regard, yet studies evaluating the relative contribution of each diagnostic technique are lacking.

METHODS

The study was conducted using a dedicated "EPI-MCD MR protocol" with a 3 Tesla MRI scanner in patients with focal epilepsy and previously negative MRI. MRI sequences evaluated included 3D FLAIR, 3D T1 MPRAGE, T2 Turbo Spin Echo (TSE), 3D T1 MP2RAGE, and Arterial Spin Labelling (ASL). Two paediatric neuroradiologists scored each sequence for localisation and extension of the lesion. The MELD-FCD AI classifier's performance in identifying pathological findings was also assessed. We only included patients where a diagnosis of MCD was subsequently confirmed on histology and/or sEEG.

RESULTS

The 3D FLAIR sequence showed the highest yield in detecting epileptogenic lesions, with 3D T1 MPRAGE, T2 TSE, and 3D T1 MP2RAGE sequences showing moderate to low yield. ASL was the least useful. The MELD-FCD classifier achieved a 69.2% true positive rate. In one case, MELD identified a subtle area of cortical dysplasia overlooked by the neuroradiologists, changing the management of the patient.

CONCLUSIONS

The 3D FLAIR sequence is the most effective in the MRI-based diagnosis of subtle epileptogenic lesions, outperforming other sequences in localisation and extension. This pilot study emphasizes the importance of careful assessment of the value of additional sequences.

Advances in magnetic resonance imaging for the assessment of paediatric focal epilepsy: a narrative review

Background and Objective

Epilepsy affects approximately 50 million people worldwide, with 30-40% of patients not responding to medication, necessitating alternative therapies such as surgical intervention. However, the accurate localization of epileptogenic lesions, particularly in pediatric magnetic resonance imaging (MRI)-negative drug-resistant epilepsy, remains a challenge. This paper reviews advanced neuroimaging techniques aimed at improving the detection of such lesions to enhance surgical outcomes.

Methods

A comprehensive literature search was conducted using PubMed, focusing on advanced MRI sequences, focal epilepsy, and the integration of artificial intelligence (AI) in the diagnostic process.

Key Content and Findings

New MRI sequences, including magnetization prepared 2 rapid gradient echo (MP2RAGE), edge-enhancing gradient echo (EDGE), and fluid and white matter suppression (FLAWS), have demonstrated enhanced capabilities in detecting subtle epileptogenic lesions. Quantitative MRI techniques, notably magnetic resonance fingerprinting (MRF), alongside innovative post-processing methods, are emphasized for their effectiveness in delineating cortical malformations, whether used alone or in combination with ultra-high field MRI systems. Furthermore, the integration of AI in radiology is progressing, providing significant support in accurately localizing lesions, and potentially optimizing pre-surgical planning.

Conclusions

While advanced neuroimaging and AI offer significant improvements in the diagnostic process for epilepsy, some challenges remain. These include long acquisition times, the need for extensive data analysis, and a lack of large, standardized datasets for AI validation. However, the future holds promise as research continues to integrate these technologies into clinical practice. These efforts will improve the clinical applicability and effectiveness of these advanced techniques in epilepsy management, paving the way for more accurate diagnoses and better patient outcomes.

A radiomics nomogram based on multiparametric MRI for diagnosing focal cortical dysplasia and initially identifying laterality

BACKGROUND

Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation. The diagnosis of FCD is challenging. We generated a radiomics nomogram based on multiparametric magnetic resonance imaging (MRI) to diagnose FCD and identify laterality early.

METHODS

Forty-three patients treated between July 2017 and May 2022 with histopathologically confirmed FCD were retrospectively enrolled. The contralateral unaffected hemispheres were included as the control group. Therefore, 86 ROIs were finally included. Using January 2021 as the time cutoff, those admitted after January 2021 were included in the hold-out set (n = 20). The remaining patients were separated randomly (8:2 ratio) into training (n = 55) and validation (n = 11) sets. All preoperative and postoperative MR images, including T1-weighted (T1w), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and combined (T1w + T2w + FLAIR) images, were included. The least absolute shrinkage and selection operator (LASSO) was used to select features. Multivariable logistic regression analysis was used to develop the diagnosis model. The performance of the radiomic nomogram was evaluated with an area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration and clinical utility.

RESULTS

The model-based radiomics features that were selected from combined sequences (T1w + T2w + FLAIR) had the highest performances in all models and showed better diagnostic performance than inexperienced radiologists in the training (AUCs: 0.847 VS. 0.664, p = 0.008), validation (AUC: 0.857 VS. 0.521, p = 0.155), and hold-out sets (AUCs: 0.828 VS. 0.571, p = 0.080). The positive values of NRI (0.402, 0.607, 0.424) and IDI (0.158, 0.264, 0.264) in the three sets indicated that the diagnostic performance of Model-Combined improved significantly. The radiomics nomogram fit well in calibration curves (p > 0.05), and decision curve analysis further confirmed the clinical usefulness of the nomogram. Additionally, the contrast (the radiomics feature) of the FCD lesions not only played a crucial role in the classifier but also had a significant correlation (r = -0.319, p < 0.05) with the duration of FCD.

CONCLUSION

The radiomics nomogram generated by logistic regression model-based multiparametric MRI represents an important advancement in FCD diagnosis and treatment.

Temporal pole blurring in temporal lobe epilepsy revealed by 3D Edge-Enhancing Gradient Echo MRI

While abnormalities of the hippocampus have been well characterized in temporal lobe epilepsy, various additional temporal lobe abnormalities have also been described. One poorly understood entity, the so-called temporal pole blurring (TPB), is one of the more frequently described neocortical abnormalities in TLE and is thought to represent dysmyelination and axonal loss due to chronic electrical perturbations in early age-onset temporal lobe epilepsy. In this study, we describe the first reported cases of TPB diagnosed by a recently described MRI sequence known as 3D Edge-Enhancing Gradient Echo (3D-EDGE), which has an effective "myelin weighting" making it exquisitely sensitive to this temporal pole dysmyelination. The value of detection of TPB lies in lateralizing seizure onset, as well as predicting a lower baseline neuropsychological performance compared to temporal lobe epilepsy without TPB. Additionally, it is critical to not mistake TPB for alternative diagnoses, such as focal cortical dysplasia or neoplasm.

Development and prospective clinical validation of a convolutional neural network for automated detection and segmentation of focal cortical dysplasias

PURPOSE

Focal cortical dysplasias (FCDs) are a leading cause of drug-resistant epilepsy. Early detection and resection of FCDs have favorable prognostic implications for postoperative seizure freedom. Despite advancements in imaging methods, FCD detection remains challenging. House et al. (2021) introduced a convolutional neural network (CNN) for automated FCD detection and segmentation, achieving a sensitivity of 77.8%. However, its clinical applicability was limited due to a low specificity of 5.5%. The objective of this study was to improve the CNN's performance through data-driven training and algorithm optimization, followed by a prospective validation on daily-routine MRIs.

MATERIAL AND METHODS

A dataset of 300 3 T MRIs from daily clinical practice, including 3D T1 and FLAIR sequences, was prospectively compiled. The MRIs were visually evaluated by two neuroradiologists and underwent morphometric assessment by two epileptologists. The dataset included 30 FCD cases (11 female, mean age: 28.1 ± 10.1 years) and a control group of 150 normal cases (97 female, mean age: 32.8 ± 14.9 years), along with 120 non-FCD pathological cases (64 female, mean age: 38.4 ± 18.4 years). The dataset was divided into three subsets, each analyzed by the CNN. Subsequently, the CNN underwent a two-phase-training process, incorporating subset MRIs and expert-labeled FCD maps. This training employed both classical and continual learning techniques. The CNN's performance was validated by comparing the baseline model with the trained models at two training levels.

RESULTS

In prospective validation, the best model trained using continual learning achieved a sensitivity of 90.0%, specificity of 70.0%, and accuracy of 72.0%, with an average of 0.41 false positive clusters detected per MRI. For FCD segmentation, an average Dice coefficient of 0.56 was attained. The model's performance improved in each training phase while maintaining a high level of sensitivity. Continual learning outperformed classical learning in this regard.

CONCLUSIONS

Our study presents a promising CNN for FCD detection and segmentation, exhibiting both high sensitivity and specificity. Furthermore, the model demonstrates continuous improvement with the inclusion of more clinical MRI data. We consider our CNN a valuable tool for automated, examiner-independent FCD detection in daily clinical practice, potentially addressing the underutilization of epilepsy surgery in drug-resistant focal epilepsy and thereby improving patient outcomes.

Pushing MP2RAGE boundaries: Ultimate time-efficient parameterization combined with exhaustive T1 synthetic contrasts

PURPOSE

MP2RAGE parameter optimization is redefined to allow more time-efficient MR acquisitions, whereas the T1 -based synthetic imaging framework is used to obtain on-demand T1 -weighted contrasts. Our aim was to validate this concept on healthy volunteers and patients with multiple sclerosis, using plug-and-play parallel-transmission brain imaging at 7 T.

METHODS

A "time-efficient" MP2RAGE sequence was designed with optimized parameters including TI and TR set as small as possible. Extended phase graph formalism was used to set flip-angle values to maximize the gray-to-white-matter contrast-to-noise ratio (CNR). Several synthetic contrasts (UNI, EDGE, FGATIR, FLAWSMIN , FLAWSHCO ) were generated online based on the acquired T1 maps. Experimental validation was performed on 4 healthy volunteers at various spatial resolutions. Clinical applicability was evaluated on 6 patients with multiple sclerosis, scanned with both time-efficient and conventional MP2RAGE parameterizations.

RESULTS

The proposed time-efficient MP2RAGE protocols reduced acquisition time by 40%, 30%, and 19% for brain imaging at (1 mm)3 , (0.80 mm)3 and (0.65 mm)3 , respectively, when compared with conventional parameterizations. They also provided all synthetic contrasts and comparable contrast-to-noise ratio on UNI images. The flexibility in parameter selection allowed us to obtain a whole-brain (0.45 mm)3 acquisition in 19 min 56 s. On patients with multiple sclerosis, a (0.67 mm)3 time-efficient acquisition enhanced cortical lesion visualization compared with a conventional (0.80 mm)3 protocol, while decreasing the scan time by 15%.

CONCLUSION

The proposed optimization, associated with T1 -based synthetic contrasts, enabled substantial decrease of the acquisition time or higher spatial resolution scans for a given time budget, while generating all typical brain contrasts derived from MP2RAGE.

Optimal control in a magnetization-prepared rapid acquisition gradient-echo sequence

This article proposes a numerical framework to determine the optimal magnetization preparation in a three-dimensional magnetization-prepared rapid gradient-echo (MP-RAGE) sequence to obtain the best achievable contrast between target tissues based on differences in their relaxation times. The benefit lies in the adaptation of the algorithm of optimal control, GRAdient Ascent Pulse Engineering (GRAPE), to the optimization of magnetization preparation in a cyclic sequence without full recovery between each cycle. This numerical approach optimizes magnetization preparation of an arbitrary number of radio frequency pulses to enhance contrast, taking into account the establishment of a steady state in the longitudinal component of the magnetization. The optimal control preparation offers an optimized mixed T 1 / T 2 contrast in this traditional T 1 -weighted sequence. To show the versatility of the proposed method, numerical and in vitro results are described. Examples of contrasts acquired on brain regions of a healthy volunteer are presented for potential applications at 3 T.

Diagnostic Accuracy of Epilepsy-dedicated MRI with Post-processing

PURPOSE

To evaluate the diagnostic accuracy of epilepsy-dedicated 3 Tesla MRI including post-processing by correlating MRI, histopathology, and postsurgical seizure outcomes.

METHODS

3 Tesla-MRI including a magnetization-prepared two rapid acquisition gradient echo (MP2RAGE) sequence for post-processing using the morphometric analysis program MAP was acquired in 116 consecutive patients with drug-resistant focal epilepsy undergoing resection surgery. The MRI, histopathology reports and postsurgical seizure outcomes were recorded from the patient's charts.

RESULTS

The MRI and histopathology were concordant in 101 and discordant in 15 patients, 3 no hippocampal sclerosis/gliosis only lesions were missed on MRI and 1 of 28 focal cortical dysplasia (FCD) type II associated with a glial scar was considered a glial scar only on MRI. In another five patients, MRI was suggestive of FCD, the histopathology was uneventful but patients were seizure-free following surgery. The MRI and histopathology were concordant in 20 of 21 glioneuronal tumors, 6 cavernomas, and 7 glial scars. Histopathology was negative in 10 patients with temporal lobe epilepsy, 4 of them had anteroinferior meningoencephaloceles. Engel class IA outcome was reached in 71% of patients.

CONCLUSION

The proposed MRI protocol is highly accurate. No hippocampal sclerosis/gliosis only lesions are typically MRI negative. Small MRI positive FCD can be histopathologically missed, most likely due to sampling errors resulting from insufficient harvesting of tissue.

Synthetic Inversion Image Generation using MP2RAGE T1 Mapping for Surgical Targeting in Deep Brain Stimulation and Lesioning

BACKGROUND

Advances in MRI technology have increased interest in direct targeting for deep brain stimulation (DBS). Various imaging sequences have been shown to provide increased contrast of numerous common DBS targets, such as T1-weighted, Fast Gray Matter Acquisition T1 Inversion Recovery (FGATIR), gray matter nulled, and Edge-Enhancing Gradient Echo (EDGE); however, the continual increase in the number of necessary sequences has led to an increase in imaging time, which is undesirable. Additionally, carefully timed inversion pulses can often lead to less-than-ideal contrast in some subjects, particularly in ultra-high field MRI, where B1+ field inhomogeneity can lead to substantial contrast variation.

OBJECTIVES

This study proposes using 3D MP2RAGE-based T1 maps to retrospectively synthesize images of any desired inversion time, including T1-weighted, FGATIR, and EDGE contrasts, to visualize specific DBS targets at both 3T and 7T.

METHOD

First, a systematic sequence optimization framework was applied to optimize MP2RAGE T1 mapping sequence parameters for the purpose of DBS planning. Next, we show that synthetic inversion-time images can be generated through a mathematical transformation of the T1 maps. The sequence was then applied to patients undergoing preoperative planning for DBS at 3T and 7T to generate synthetic contrasts used in surgical planning.

RESULTS

We show that synthetic image contrasts can be generated across a full range of inversion times at 3T and 7T, including commonly used sequences for DBS targeting, such as T1-weighted, FGATIR, and EDGE. Acquisition through a single sequence shortens scan time compared to acquiring the sequences independently without affecting image quality or contrast.

CONCLUSIONS

The generation of synthetic images for DBS targeting allows faster acquisition of many key sequences, as well as the ability to optimize contrast properties post-acquisition to account for the variable B1+ effects present in ultra-high field MRI. The proposed approach has the potential to reduce imaging time and improve the accuracy of DBS targeting at 1.5T, 3T, and 7T.

Automatic segmentation of white matter hyperintensities and correlation analysis for cerebral small vessel disease

Objective

Cerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes in white matter regions. In this paper, we propose a framework for automatic 3D segmentation of brain white matter hyperintensity based on MRI images to address the problems of low accuracy and segmentation inhomogeneity in 3D segmentation. We explored correlation analyses of cognitive assessment parameters and multiple comparison analyses to investigate differences in brain white matter hyperintensity volume among three cognitive states, Dementia, MCI and NCI. The study explored the correlation between cognitive assessment coefficients and brain white matter hyperintensity volume.

Methods

This paper proposes an automatic 3D segmentation framework for white matter hyperintensity using a deep multi-mapping encoder-decoder structure. The method introduces a 3D residual mapping structure for the encoder and decoder. Multi-layer Cross-connected Residual Mapping Module (MCRCM) is proposed in the encoding stage to enhance the expressiveness of model and perception of detailed features. Spatial Attention Weighted Enhanced Supervision Module (SAWESM) is proposed in the decoding stage to adjust the supervision strategy through a spatial attention weighting mechanism. This helps guide the decoder to perform feature reconstruction and detail recovery more effectively.

Result

Experimental data was obtained from a privately owned independent brain white matter dataset. The results of the automatic 3D segmentation framework showed a higher segmentation accuracy compared to nnunet and nnunet-resnet, with a p-value of <0.001 for the two cognitive assessment parameters MMSE and MoCA. This indicates that larger brain white matter are associated with lower scores of MMSE and MoCA, which in turn indicates poorer cognitive function. The order of volume size of white matter hyperintensity in the three groups of cognitive states is dementia, MCI and NCI, respectively.

Conclusion

The paper proposes an automatic 3D segmentation framework for brain white matter that achieves high-precision segmentation. The experimental results show that larger volumes of segmented regions have a negative correlation with lower scoring coefficients of MMSE and MoCA. This correlation analysis provides promising treatment prospects for the treatment of cerebral small vessel diseases in the brain through 3D segmentation analysis of brain white matter. The differences in the volume of white matter hyperintensity regions in subjects with three different cognitive states can help to better understand the mechanism of cognitive decline in clinical research.

Pearls & Oy-sters: Harnessing New Diagnostic and Therapeutic Approaches to Treat a Patient With Genetic Drug-Resistant Focal Epilepsy

Focal cortical dysplasia (FCD) is a congenital developmental malformation and is one of the leading causes of drug-resistant focal epilepsy (DRFE). Although focal epilepsies traditionally have been regarded as acquired disorders, increasing evidence suggests a substantial genetic contribution to the pathogenesis of focal structural epilepsies, including FCDs. Variations in the Dishevelled, Egl-10, and domain-containing protein 5 (DEPDC5) have recently emerged as a causative gene mutation in familial focal epilepsies associated with FCD type 2a, including bottom-of-sulcus dysplasia (BOSD). We present the case of a 20-year-old man with DRFE, positive for DEPDC5 c.1555C>T (p.GIn519*) heterozygous pathogenic variant. Initial 3T brain MRI was unrevealing, but subsequent 7T MRI including 7T edge-enhancing gradient echo revealed a left superior frontal sulcus BOSD concordant with the electroclinical data. The patient underwent treatment with MR-guided laser interstitial thermal ablation of the left frontal BOSD without intracranial EEG monitoring (skipped candidate), resulting in a seizure-free outcome of 9 months since the last follow-up. Our case highlights the real-world application of summative information obtained through advancements in epilepsy genetic testing, minimally invasive surgeries, and ultra-high field MRI, allowing us to provide a safe and effective treatment for a patient with a genetic DRFE.

Optimization of MP2RAGE T1 mapping with radial view-ordering for deep brain stimulation targeting at 7 T MRI

PURPOSE

Deep brain stimulation (DBS) is an effective treatment of various neurological disorders. Due to higher intrinsic signal, 7 T MRI can potentially improve delineation of DBS targets. However, the severe RF transmit field (B1+) inhomogeneity at 7 T can compromise the image contrast of traditional single-contrast sequences for DBS targeting, leading to sub-optimal target visualization. The Magnetization Prepared 2 Rapid Acquisition Gradient Echo (MP2RAGE)-based T1 mapping provides an alternative to the traditional single-contrast techniques by allowing retrospective synthesis of images at arbitrary inversion times to aid in visualization of various DBS targets. With this approach, optimization of sequence parameters to create T1 maps with low noise and low quantification bias is critical, as these characteristics directly affect the noise and uniformity of the synthetic images. In this work, we perform sequence optimization for MP2RAGE-based T1 mapping using a radial view-ordering technique to improve image quality, and demonstrate the clinical utility of T1 mapping approach for DBS targeting.

METHODS

We first introduce a systematic sequence optimization framework for 7 T MP2RAGE T1 mapping by formulating it into a constrained, multi-dimensional optimization process considering the effect of B1+ inhomogeneity on image noise, T1 quantification bias, and image blurring. With this framework, we investigate the use of radial view-order approach for T1 mapping, in lieu of the conventional linear view-ordering. Bloch's equation-based simulations were performed to compare the T1 maps generated using different approaches. Images of healthy volunteer and patients were acquired on a clinical 7 T MRI scanner for validation and to demonstrate the utility of T1 mapping for DBS targeting.

RESULTS

Numerical experiments demonstrated that the proposed framework allowed optimization of image SNR in T1 maps while controlling the quantification bias and image blurring, therefore facilitating the selection of optimal sequence parameters for visualizing DBS targets. The optimized sequence using radial view-ordering offered 40-60% noise reduction compared to the linear view-ordering. The improvement of SNR was confirmed in the in vivo examples. Clinical images showed that the synthetic images generated from the optimized T1 maps allowed clear visualization of DBS targets.

CONCLUSION

We demonstrated the optimization of MP2RAGE T1 mapping with radial view-ordering technique for DBS targeting at 7 T and showed that the optimized sequence allows retrospective generation of synthetic inversion time images commonly utilized in DBS targeting, such as fast gray matter acquisition T1 inversion recovery (FGATIR) and edge-enhancing gradient echo (EDGE) sequences.

Edge-Enhancing Gradient-Echo MP2RAGE for Clinical Epilepsy Imaging at 7T

The 3D edge-enhancing gradient-echo (EDGE) MR imaging sequence offers superior contrast-to-noise ratio in the detection of focal cortical dysplasia. EDGE could benefit from 7T MR imaging but also faces challenges such as image inhomogeneity and low acquisition efficiency. We propose an EDGE-MP2RAGE sequence that can provide both EDGE and T1-weighted contrast, simultaneously, improving data-acquisition efficiency. We demonstrate that with sequence optimization, EDGE images with sufficient uniformity and T1-weighted images with high gray-to-white matter contrast can be achieved.

The Role of the Three-Dimensional Edge-Enhancing Gradient Echo Sequence at 3T MRI in the Detection of Focal Cortical Dysplasia: A Technical Case Report and Literature Review

INTRODUCTION

Focal cortical dysplasia (FCD) is a most common cause of intractable focal epilepsy in children. Surgery is considered as a radical option for such patients with the prerequisite of lesion detection. Magnetic resonance imaging (MRI) plays a significant role in detection of FCDs in epilepsy patients; however, the detection of FCDs even in epilepsy dedicated MRI sequence shows relatively low positive rate. Last year, Middlebrooks et al introduced the novel three-dimensional Edge-Enhancing Gradient Echo (3D-EDGE) MRI sequence and using this sequence successfully identified five cases of FCDs which indicates its potential role in those epilepsy patients who may have FCDs.

CASE PRESENTATION

We present a 14-year-old, right-handed, male patient who has suffered from drug-resistant epilepsy over the past 3 years. It was unable to localize the lesion of the seizure, even using the series of epilepsy dedicated MRI sequences. Inspired by the previous report, the lesion of the seizure was successfully targeted by 3D-EDGE sequence. Combined with intraoperative navigation and precisely removed the lesion. He was uneventfully recovered with no signs of cerebral dysfunction and no seizure recurrence 8 months after surgery.

CONCLUSION

The 3D-EDGE sequences show a higher sensitivity for FCD detection in epilepsy patients compared with a series of epilepsy-dedicated MRI protocols. We confirmed that the study by Middlebrooks et al is of great clinical value. If the findings on routine MRI sequences or even epilepsy-dedicated MRI sequences were reported as negative, however, the semiology, video-electroencephalography, and fluorodeoxyglucose-positron emission tomography results suggest a local abnormality, and the results are concordant with each other, a 3D-EDGE sequence may be a good option.

Imaging in medically refractory epilepsy at 3 Tesla: a 13-year tertiary adult epilepsy center experience

Objectives

MRI negative epilepsy has evolved through increased usage of 3 T and insights from surgically correlated studies. The goal of this study is to describe dedicated 3 T epilepsy MRI findings in medically refractory epilepsy (MRE) patients at a tertiary epilepsy center to familiarize radiologists with an updated spectrum and frequency of potential imaging findings in the adult MRE population.

Methods

Included were all patients with MRE admitted to the epilepsy monitoring unit who were discussed at weekly interdisciplinary imaging conferences at Toronto Western Hospital with MRI studies (3 T with dedicated epilepsy protocol) performed between January 2008 and January 2021. Lesion characterization was performed by two readers based on most likely imaging diagnosis in consensus. Lobes involved per case were recorded.

Results

A total of 738 patients (386 female; mean age 35 years, range 15–77) were included. A total of 262 patients (35.5%) were MRI negative. The most common imaging finding was mesial temporal sclerosis, seen in 132 patients (17.9%), followed by encephalomalacia and gliosis, either posttraumatic, postoperative, postischemic, or postinfectious in nature, in 79 patients (10.7%). The most common lobar involvement (either partially or uniquely) was temporal (341 cases, 58.6%). MRE patients not candidates for surgical resection were included in the study, as were newly described pathologies from surgically correlated studies revealing findings seen retrospectively on reported MRI negative exams (isolated enlargement of the amygdala, temporal pole white matter abnormality, temporal encephalocele).

Conclusion

This study provides an updated description of the spectrum of 3 T MRI findings in adult MRE patients from a tertiary epilepsy center.

A Noise-robust and Overshoot-free Alternative to Unsharp Masking for Enhancing the Acuity of MR Images

Poor acutance of images (unsharpness) is one of the major concerns in magnetic resonance imaging (MRI). MRI-based diagnosis and clinical interventions become difficult due to the vague textural information and weak morphological margins on images. A novel image sharpening algorithm named as maximum local variation-based unsharp masking (MLVUM) to address the issue of 'unsharpness' in MRI is proposed in this paper. In the MLVUM, the sharpened image is the algebraic sum of the input image and the product of the user-defined scale and the difference between the output of a newly designed nonlinear spatial filter named maximum local variation-controlled edge smoothing Gaussian filter (MLVESGF) and the input image, weighted by the normalised MLV. The MLVESGF is a locally adaptive 2D Gaussian edge smoothing kernel whose standard deviation is directly proportional to the local value of the normalized MLV. The values of the acutance-to-noise ratio (ANR) and absolute mean brightness error (AMBE) shown by the MLVUM on 100 MRI slices are 0.6463 ± 0.1852 and 0.3323 ± 0.2200, respectively. Compared to 17 state-of-the-art image sharpening algorithms, the MLVUM exhibited a higher ANR and lower AMBE. The MLVUM selectively enhances the sharpness of edges in the MR images without amplifying the background noise without altering the mean brightness level.

MRI of focal cortical dysplasia

Focal cortical dysplasia (FCD) are histopathologically categorized in ILAE type I to III. Mild malformations of cortical development (mMCD) including those with oligodendroglial hyperplasia (MOGHE) are to be integrated into this classification yet. Only FCD type II have distinctive MRI and molecular genetics alterations so far. Subtle FCD including FCD type II located in the depth of a sulcus are often overlooked requiring the use of dedicated sequences (MP2RAGE, FLAWS, EDGE) and/or voxel (VBM)- or surface-based (SBM) postprocessing. The added value of 7 Tesla MRI has to be proven yet.

Optimization of fast gray matter acquisition T1 inversion recovery (FGATIR) on 7T MRI for deep brain stimulation targeting

Deep brain stimulation (DBS) is an increasingly utilized treatment for multiple neurological disorders. Continued improvements in DBS outcome are, in part, related to increasing ability to directly visualize stimulation targets by MRI. However, it is challenging to image DBS targets with conventional MRI techniques due to limited contrast. Fast Gray Matter Acquisition T1 Inversion Recovery (FGATIR) is a commonly used MRI sequence that improves visualization of several key DBS targets by suppressing white matter (WM) signal to better reveal deep-brain gray matter (GM) structures. Due to increased signal level at high field strength, application of FGATIR on 7T MRI may allow higher spatial resolution and better DBS targeting accuracy. However, successful utilization of FGATIR requires meticulous sequence optimization involving multiple parameters to maximize GM signal while suppressing WM. This is further complicated by the transmit RF field (B1+) inhomogeneity on 7T, which can cause severe contrast degradation. In this work, we introduce a systematic approach to optimize FGATIR and to improve visualization of thalamic DBS targets on 7T. FGATIR optimization is cast into a constrained optimization problem whose objective function and constraints are designed to maximize the GM-WM contrast-to-noise ratio (CNR) while accounting for B1+ inhomogeneity. This approach allows a systematic search for optimal parameters across the multi-dimensional parametric space while limiting the negative effect of B1+ variation. Bloch equation simulations were performed to solve the proposed optimization problem and to compare the sequence derived from this method against the sequence optimized without considering B1+ inhomogeneity. The results showed that this approach can improve GM-WM CNR in the presence of B1+ inhomogeneity, especially in some high relative B1+ areas where several key thalamic DBS targets are located. Additionally, in vivo images were acquired on a clinical 7T MRI to further validate this approach. Severe contrast degradation in the thalamus was observed when B1+ effect was not considered in sequence optimization, while the proposed approach yielded improved image contrast in the thalamus with key DBS targets well-defined. These results demonstrated that the proposed method allowed optimization of FGATIR on 7T to better visualize thalamic DBS targets, which may lead to improved DBS targeting accuracy as well as treatment outcome.

Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging

This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process. Examples include machine learning applications in stroke, aneurysms, multiple sclerosis, neuro-oncology, neurodegeneration, and epilepsy.

Improving surgical outcome with electric source imaging and high field magnetic resonance imaging

While most patients with focal epilepsy present with clear structural abnormalities on standard, 1.5 or 3 T MRI, some patients are MRI-negative. For those, quantitative MRI techniques, such as volumetry, voxel-based morphometry, and relaxation time measurements can aid in finding the epileptogenic focus. High-field MRI, just recently approved for clinical use by the FDA, increases the resolution and, in several publications, was shown to improve the detection of focal cortical dysplasias and mild cortical malformations. For those cases without any tissue abnormality in neuroimaging, even at 7 T, scalp EEG alone is insufficient to delimitate the epileptogenic zone. They may benefit from the use of high-density EEG, in which the increased number of electrodes helps improve spatial sampling. The spatial resolution of even low-density EEG can benefit from electric source imaging techniques, which map the source of the recorded abnormal activity, such as interictal epileptiform discharges, focal slowing, and ictal rhythm. These EEG techniques help localize the irritative, functional deficit, and seizure-onset zone, to better estimate the epileptogenic zone. Combining those technologies allows several drug-resistant cases to be submitted to surgery, increasing the odds of seizure freedom and providing a must needed hope for patients with epilepsy.

Making the Invisible Visible: Advanced Neuroimaging Techniques in Focal Epilepsy

It has been a clinically important, long-standing challenge to accurately localize epileptogenic focus in drug-resistant focal epilepsy because more intensive intervention to the detected focus, including resection neurosurgery, can provide significant seizure reduction. In addition to neurophysiological examinations, neuroimaging plays a crucial role in the detection of focus by providing morphological and neuroanatomical information. On the other hand, epileptogenic lesions in the brain may sometimes show only subtle or even invisible abnormalities on conventional MRI sequences, and thus, efforts have been made for better visualization and improved detection of the focus lesions. Recent advance in neuroimaging has been attracting attention because of the potentials to better visualize the epileptogenic lesions as well as provide novel information about the pathophysiology of epilepsy. While the progress of newer neuroimaging techniques, including the non-Gaussian diffusion model and arterial spin labeling, could non-invasively detect decreased neurite parameters or hypoperfusion within the focus lesions, advances in analytic technology may also provide usefulness for both focus detection and understanding of epilepsy. There has been an increasing number of clinical and experimental applications of machine learning and network analysis in the field of epilepsy. This review article will shed light on recent advances in neuroimaging for focal epilepsy, including both technical progress of images and newer analytical methodologies and discuss about the potential usefulness in clinical practice.

Edge-enhancing gradient echo with multi-image co-registration and averaging (EDGE-MICRA) for targeting thalamic centromedian and parafascicular nuclei

BACKGROUND AND PURPOSE

Deep brain stimulation of the thalamus is an effective treatment for multiple neurological disorders. The centromedian and parafascicular nuclei are recently emerging targets for multiple conditions, such as epilepsy and Tourette syndrome; however, their limited visibility on conventional magnetic resonance imaging sequences has been a major obstacle. The goal of this study was to demonstrate the feasibility of a high-resolution and high-contrast targeting sequence for centromedian-parafascicular deep brain stimulation using a recently described magnetic resonance imaging sequence, three-dimensional edge-enhancing gradient echo.

METHODS

The three-dimensional edge-enhancing gradient echo sequence was performed on a normal volunteer for a total of six acquisitions. Multi-image co-registration and averaging was performed by first co-registering each of the six scans and then averaging to produce an edge-enhancing gradient echo-multi-image co-registration and averaging scan. The averaging was also performed for two, three, four and five scans to assess the change in the signal-to-noise ratio and identify the ideal balance of image quality and scan time.

RESULTS

The edge-enhancing gradient echo-multi-image co-registration and averaging scan allowed clear boundary delineation of the centromedian and parafascicular nuclei. The signal-to-noise ratio increased as a function of increasing scan number, but the added gain was small beyond four scans for the imaging parameters used in this study.

CONCLUSIONS

The recently described three-dimensional edge-enhancing gradient echo sequence provides an easily implementable approach, using widely available magnetic resonance imaging technology without complex post-processing techniques, to delineate centromedian and parafascicular nuclei for deep brain stimulation targeting.