Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time.

PURPOSE: Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan. METHODS: There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data. RESULTS: Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics. CONCLUSION: DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.

Comparing the efficacy of data-driven denoising methods for a multi-echo fMRI acquisition at 7T.

In functional magnetic resonance imaging (fMRI) of the brain the measured signal is corrupted by several (e.g. physiological, motion, and thermal) noise sources and depends on the image acquisition. Imaging at ultrahigh field strength is becoming increasingly popular as it offers increased spatial accuracy. The latter is of particular benefit in brainstem neuroimaging given the small cross-sectional area of most nuclei. However, physiological noise scales with field strength in fMRI acquisitions. Although this problem is in part solved by decreasing voxel size, it is clear that adequate physiological denoising is of utmost importance in brainstem-focused fMRI experiments. Multi-echo sequences have been reported to facilitate highly effective denoising through TE-dependence of Blood Oxygen Level Dependent (BOLD) signals, in a denoising method referred to as multi-echo independent component analysis (ME-ICA). It has not been explored previously how ME-ICA compares to other data-driven denoising approaches at ultrahigh field strength. In the current study, we compared the efficacy of several denoising methods, including anatomical component based correction (aCompCor), Automatic Removal of Motion Artifacts (ICA-AROMA) aggressive and non-aggressive options, ME-ICA, and a combination of ME-ICA and aCompCor. We assessed several data quality metrics, including temporal signal-to-noise ratio (tSNR), delta variation signal (DVARS), spectral density of the global signal, functional connectivity and Shannon spectral entropy. Moreover, we looked at the ability of each method to uncouple the global signal and respiration. In line with previous reports at lower field strengths, we demonstrate that after applying ME-ICA, the data is best post-processed in order to remove spatially diffuse noise with a method such as aCompCor. Our findings indicate that ME-ICA combined with aCompCor and the aggressive option of ICA-AROMA are highly effective denoising approaches for multi-echo data acquired at 7T. ME-ICA combined with aCompCor potentially preserves more signal-of-interest as compared to the aggressive option of ICA-AROMA.

Advances in Image Processing for Epileptogenic Zone Detection with MRI.

Focal epilepsy is a common and severe neurologic disorder. Neuroimaging aims to identify the epileptogenic zone (EZ), preferably as a macroscopic structural lesion. For approximately a third of patients with chronic drug-resistant focal epilepsy, the EZ cannot be precisely identified using standard 3.0-T MRI. This may be due to either the EZ being undetectable at imaging or the seizure activity being caused by a physiologic abnormality rather than a structural lesion. Computational image processing has recently been shown to aid radiologic assessments and increase the success rate of uncovering suspicious regions by enhancing their visual conspicuity. While structural image analysis is at the forefront of EZ detection, physiologic image analysis has also been shown to provide valuable information about EZ location. This narrative review summarizes and explains the current state-of-the-art computational approaches for image analysis and presents their potential for EZ detection. Current limitations of the methods and possible future directions to augment EZ detection are discussed.

An optimized b-value sampling for the quantification of interstitial fluid using diffusion-weighted MRI, a genetic algorithm approach.

PURPOSE: Multi-b-value diffusion-weighted MRI techniques can simultaneously measure the parenchymal diffusivity, microvascular perfusion, and a third, intermediate diffusion component. This component is related to the interstitial fluid in the brain parenchyma. However, simultaneously estimating three diffusion components from multi-b-value data is difficult and has strong dependence on SNR and chosen b-values. As the number of acquired b-values is limited due to scanning time, it is important to know which b-values are most effective to be included. Therefore, this study evaluates an optimized b-value sampling for interstitial fluid estimation. METHOD: The optimized b-value sampling scheme is determined using a genetic algorithm. Subsequently, the performance of this optimized sampling is assessed by comparing it with a linear, logarithmic, and previously proposed sampling scheme, in terms of the RMS error (RMSE) for the intermediate component estimation. The in vivo performance of the optimized sampling is assessed using 7T data with 101 equally spaced b-values ranging from 0 to 1000 s/mm2 . In this case, the RMSE was determined by comparing the fit that includes all b-values. RESULTS: The optimized b-value sampling for estimating the intermediate component was reported to be [0, 30, 90, 210, 280, 350, 580, 620, 660, 680, 720, 760, 980, 990, 1000] s/mm2 . For computer simulations, the optimized sampling had a lower RMSE, compared with the other samplings for varying levels of SNR. For the in vivo data, the voxel-wise RMSE of the optimized sampling was lower compared with other sampling schemes. CONCLUSION: The genetic algorithm-optimized b-value scheme improves the quantification of the diffusion component related to interstitial fluid in terms of a lower RMSE.

Improving microstructural integrity, interstitial fluid, and blood microcirculation images from multi-b-value diffusion MRI using physics-informed neural networks in cerebrovascular disease.

PURPOSE: To obtain better microstructural integrity, interstitial fluid, and microvascular images from multi-b-value diffusion MRI data by using a physics-informed neural network (PINN) fitting approach. METHODS: Test-retest whole-brain inversion recovery diffusion-weighted images with multiple b-values (IVIM: intravoxel incoherent motion) were acquired on separate days for 16 patients with cerebrovascular disease on a 3.0T MRI system. The performance of the PINN three-component IVIM (3C-IVIM) model fitting approach was compared with conventional fitting approaches (i.e., non-negative least squares and two-step least squares) in terms of (1) parameter map quality, (2) test-retest repeatability, and (3) voxel-wise accuracy. Using the in vivo data, the parameter map quality was assessed by the parameter contrast-to-noise ratio (PCNR) between normal-appearing white matter and white matter hyperintensities, and test-retest repeatability was expressed by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). The voxel-wise accuracy of the 3C-IVIM parameters was determined by 10,000 computer simulations mimicking our in vivo data. Differences in PCNR and CV values obtained with the PINN approach versus conventional fitting approaches were assessed using paired Wilcoxon signed-rank tests. RESULTS: The PINN-derived 3C-IVIM parameter maps were of higher quality and more repeatable than those of conventional fitting approaches, while also achieving higher voxel-wise accuracy. CONCLUSION: Physics-informed neural networks enable robust voxel-wise estimation of three diffusion components from the diffusion-weighted signal. The repeatable and high-quality biological parameter maps generated with PINNs allow for visual evaluation of pathophysiological processes in cerebrovascular disease.

Imaging Interstitial Fluid With MRI: A Narrative Review on the Associations of Altered Interstitial Fluid With Vascular and Neurodegenerative Abnormalities.

Interstitial fluid (ISF) refers to the fluid between the parenchymal cells and along the perivascular spaces (PVS). ISF plays a crucial role in delivering nutrients and clearing waste products from the brain. This narrative review focuses on the use of MRI techniques to measure various ISF characteristics in humans. The complementary value of contrast-enhanced and noncontrast-enhanced techniques is highlighted. While contrast-enhanced MRI methods allow measurement of ISF transport and flow, they lack quantitative assessment of ISF properties. Noninvasive MRI techniques, including multi-b-value diffusion imaging, free-water-imaging, T2 -decay imaging, and DTI along the PVS, offer promising alternatives to derive ISF measures, such as ISF volume and diffusivity. The emerging role of these MRI techniques in investigating ISF alterations in neurodegenerative diseases (eg, Alzheimer's disease and Parkinson's disease) and cerebrovascular diseases (eg, cerebral small vessel disease and stroke) is discussed. This review also emphasizes current challenges of ISF imaging, such as the microscopic scale at which ISF has to be measured, and discusses potential focus points for future research to overcome these challenges, for example, the use of high-resolution imaging techniques. Noninvasive MRI methods for measuring ISF characteristics hold significant potential and may have a high clinical impact in understanding the pathophysiology of neurodegenerative and cerebrovascular disorders, as well as in evaluating the efficacy of ISF-targeted therapies in clinical trials. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Estimating myelin-water content from anatomical and diffusion images using spatially undersampled myelin-water imaging through machine learning.

Myelin is vital for healthy neuronal development, and can therefore provide valuable information regarding neuronal maturation. Anatomical and diffusion weighted images (DWI) possess information related to the myelin content and the current study investigates whether quantitative myelin markers can be extracted from anatomical and DWI using neural networks. Thirteen volunteers (mean age 29y) are included, and for each subject, a residual neural network was trained using spatially undersampled reference myelin-water markers. The network is trained on a voxel-by-voxel basis, resulting in a large amount of training data for each volunteer. The inputs used are the anatomical contrasts (cT1w, cT2w), the standardized T1w/T2w ratio, estimates of the relaxation times (T1, T2) and their ratio (T1/T2), and common DWI metrics (FA, RD, MD, λ1, λ2, λ3). Furthermore, to estimate the added value of the DWI metrics, neural networks were trained using either the combined set (DWI, T1w and T2w) or only the anatomical (T1w and T2w) images. The reconstructed myelin-water maps are in good agreement with the reference myelin-water content in terms of the coefficient of variation (CoV) and the intraclass correlation coefficient (ICC). A 6-fold undersampling using both anatomical and DWI metrics resulted in ICC = 0.68 and CoV = 5.9%. Moreover, using twice the training data (3-fold undersampling) resulted in an ICC that is comparable to the reproducibility of the myelin-water imaging itself (CoV = 5.5% vs. CoV = 6.7% and ICC = 0.74 vs ICC = 0.80). To achieve this, beside the T1w, T2w images, DWI is required. This preliminary study shows the potential of machine learning approaches to extract specific myelin-content from anatomical and diffusion-weighted scans.

Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht study.

Quality control of brain segmentation is a fundamental step to ensure data quality. Manual quality control strategies are the current gold standard, although these may be unfeasible for large neuroimaging samples. Several options for automated quality control have been proposed, providing potential time efficient and reproducible alternatives. However, those have never been compared side to side, which prevents consensus in the appropriate quality control strategy to use. This study aimed to elucidate the changes manual editing of brain segmentations produce in morphological estimates, and to analyze and compare the effects of different quality control strategies on the reduction of the measurement error. Structural brain MRI from 259 participants of The Maastricht Study were used. Morphological estimates were automatically extracted using FreeSurfer 6.0. Segmentations with inaccuracies were manually edited, and morphological estimates were compared before and after editing. In parallel, 12 quality control strategies were applied to the full sample. Those included: two manual strategies, in which images were visually inspected and either excluded or manually edited; five automated strategies, where outliers were excluded based on the tools "MRIQC" and "Qoala-T", and the metrics "morphological global measures", "Euler numbers" and "Contrast-to-Noise ratio"; and five semi-automated strategies, where the outliers detected through the mentioned tools and metrics were not excluded, but visually inspected and manually edited. In order to quantify the effects of each quality control strategy, the proportion of unexplained variance relative to the total variance was extracted after the application of each strategy, and the resulting differences compared. Manually editing brain surfaces produced particularly large changes in subcortical brain volumes and moderate changes in cortical surface area, thickness and hippocampal volumes. The performance of the quality control strategies depended on the morphological measure of interest. Overall, manual quality control strategies yielded the largest reduction in relative unexplained variance. The best performing automated alternatives were those based on Euler numbers and MRIQC scores. The exclusion of outliers based on global morphological measures produced an increase of relative unexplained variance. Manual quality control strategies are the most reliable solution for quality control of brain segmentation and parcellation. However, measures must be taken to prevent the subjectivity associated with these strategies. The detection of inaccurate segmentations based on Euler numbers or MRIQC provides a time efficient and reproducible alternative. The exclusion of outliers based on global morphological estimates must be avoided.

Predictive value of functional MRI and EEG in epilepsy diagnosis after a first seizure.

It is often difficult to predict seizure recurrence in subjects who have suffered a first-ever epileptic seizure. In this study, the predictive value of physiological signals measured using Electroencephalography (EEG) and functional MRI (fMRI) is assessed. In particular those patients developing epilepsy (i.e. a second unprovoked seizure) that were initially evaluated as having a low risk of seizure recurrence are of interest. In total, 26 epilepsy patients, of which 8 were initially evaluated as having a low risk of seizure recurrence (i.e. converters), and 17 subjects with only a single seizure were included. All subjects underwent routine EEG as well as fMRI measurements. For diagnostic classification, features related to the temporal dynamics were determined for both the processed EEG and fMRI data. Subsequently, a logistic regression classifier was trained on epilepsy and first-seizure subjects. The trained model was tested using the clinically relevant converters group. The sensitivity, specificity, and AUC (mean ±â€¯SD) of the regression model including metrics from both modalities were 74 ±â€¯19%, 82 ±â€¯18%, and 0.75 ±â€¯0.12, respectively. Positive and negative predictive values (mean ±â€¯SD) of the regression model with both EEG and fMRI features are 84 ±â€¯14% and 78 ±â€¯12%. Moreover, this EEG/fMRI model showed significant improvements compared to the clinical diagnosis, whereas the models using metrics from either EEG or fMRI do not reach significance (p > 0.05). Temporal metrics computationally derived from EEG and fMRI time signals may clinically aid and synergistically improve the predictive value in a first-seizure sample.

Spectral Diffusion Analysis of Intravoxel Incoherent Motion MRI in Cerebral Small Vessel Disease.

BACKGROUND: Cerebral intravoxel incoherent motion (IVIM) imaging assumes two components. However, more compartments are likely present in pathologic tissue. We hypothesized that spectral analysis using a nonnegative least-squares (NNLS) approach can detect an additional, intermediate diffusion component, distinct from the parenchymal and microvascular components, in lesion-prone regions. PURPOSE: To investigate the presence of this intermediate diffusion component and its relation with cerebral small vessel disease (cSVD)-related lesions. STUDY TYPE: Prospective cross-sectional study. POPULATION: Patients with cSVD (n = 69, median age 69.8) and controls (n = 39, median age 68.9). FIELD STRENGTH/SEQUENCE: Whole-brain inversion recovery IVIM acquisition at 3.0T. ASSESSMENT: Enlarged perivascular spaces (PVS) were rated by three raters. White matter hyperintensities (WMH) were identified on a fluid attenuated inversion recovery (FLAIR) image using a semiautomated algorithm. STATISTICAL TESTS: Relations between IVIM measures and cSVD-related lesions were studied using the Spearman's rank order correlation. RESULTS: NNLS yielded diffusion spectra from which the intermediate volume fraction fint was apparent between parenchymal diffusion and microvasular pseudodiffusion. WMH volume and the extent of MRI-visible enlarged PVS in the basal ganglia (BG) and centrum semiovale (CSO) were correlated with fint in the WMHs, BG, and CSO, respectively. fint was 4.2 ± 1.7%, 7.0 ± 4.1% and 13.6 ± 7.7% in BG and 3.9 ± 1.3%, 4.4 ± 1.4% and 4.5 ± 1.2% in CSO for the groups with low, moderate, and high number of enlarged PVS, respectively, and increased with the extent of enlarged PVS (BG: r = 0.49, P < 0.01; CSO: r = 0.23, P = 0.02). fint in the WMHs was 27.1 ± 13.1%, and increased with the WMH volume (r = 0.57, P < 0.01). DATA CONCLUSION: We revealed the presence of an intermediate diffusion component in lesion-prone regions of cSVD and demonstrated its relation with enlarged PVS and WMHs. In tissue with these lesions, tissue degeneration or perivascular edema can lead to more freely diffusing interstitial fluid contributing to fint . LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1170-1180.

Applicability and reproducibility of 2D multi-slice GRASE myelin water fraction with varying acquisition acceleration.

Non-invasive quantification of the in vivo myelin content may provide valuable information regarding healthy maturation of the brain, as well as insights into demyelination of several neurological disorders. However, these scans are often long thereby limiting acquisition of large brain parts in clinically feasible acquisition times. Therefore, fast acquisition of whole brain myelin content is important. To avoid errors related to slice-selective pulses, most of the previous whole brain studies on myelin content relied on a 3D acquisition. However, multi-slice (2D) acquisition methods are often faster, and less susceptible to motion artifacts. Therefore, multi-slice approaches can be beneficial in a clinical setting. We investigated the applicability and reproducibility of whole brain multi-slice GRASE myelin-water imaging with post-acquisition slice-profile correction in healthy volunteers (aged 25-32y). The applicability was evaluated using the agreement between the multi-slice GRASE and the reference method for myelin-water imaging, single-slice multi spin-echo (MSE) acquisition. Additionally, we assessed the effect of varying acquisition acceleration using parallel imaging on the reproducibility values. First, the multi-slice myelin-water maps showed good agreement with the single-slice reference method, with a bias of at most 1.2% in absolute MWF values. Second, we found an average within-subject coefficient of variation (CoV) of 5.9% and an average intra-class correlation coefficient (ICC) of 0.90 for myelin-water estimation using a multi-slice GRASE sequence without parallel acceleration (scan time 14:06 min), while acquisition with a parallel acceleration factor of 2 resulted in a slightly worse average within-subject CoV of 6.4% and an average ICC of 0.83 at half the scan time. Hence, a multi-slice GRASE acquisition with parallel acceleration factor 2 and a scan time of 7:30 min still provides an excellent reproducibility.

A new analysis approach for T2 relaxometry myelin water quantification: Orthogonal Matching Pursuit.

PURPOSE: In vivo myelin quantification can provide valuable noninvasive information on neuronal maturation and development, as well as insights into neurological disorders. Multiexponential analysis of multiecho T2 relaxation is a powerful and widely applied method for the quantification of the myelin water fraction (MWF). In recent literature, the MWF is most commonly estimated using a regularized nonnegative least squares algorithm. METHODS: The orthogonal matching pursuit algorithm is proposed as an alternative method for the estimation of the MWF. The orthogonal matching pursuit is a greedy sparse reconstruction algorithm with a low computation complexity. For validation, both methods are compared to a ground truth using numerical simulations and a phantom model using comparable computation times. The numerical simulations were used to measure the theoretical errors, as well as the effects of varying the SNR, strength of the regularization, and resolution of the basis set. Additionally, a phantom model was used to estimate the performance of the 2 methods while including errors occurring due to the MR measurement. Lastly, 4 healthy subjects were scanned to evaluate the in vivo performance. RESULTS: The results in simulations and phantoms demonstrate that the MWFs determined with the orthogonal matching pursuit are 1.7 times more accurate as compared to the nonnegative least squares, with a comparable precision. The remaining bias of the MWF is shown to be related to the regularization of the nonnegative least squares algorithm and the Rician noise present in magnitude MR images. CONCLUSION: The orthogonal matching pursuit algorithm provides a more accurate alternative for T2 relaxometry myelin water quantification.

Lower myelin-water content of the frontal lobe in childhood absence epilepsy.

OBJECTIVE: The frontal lobe in childhood absence epilepsy (CAE) might be affected due to the suggested involvement of the frontal lobe during absence seizures and reports on attentional deficits. Previously, subtle white matter abnormalities have been reported in CAE. However, the impact of one of the most characteristic components of the white matter, the myelin content, remains underdetermined. Therefore, this study investigated whether the myelin content in frontal areas is adversely affected in CAE compared to controls. METHODS: Seventeen children with childhood absence epilepsy (mean age ± standard deviation [SD], 9.2 ± 2.1 years) and 15 age- and sex-matched controls (mean age ± SD, 9.8 ± 1.8 years) underwent neuropsychological assessment and a magnetic resonance imaging (MRI) examination. T2 relaxometry scans were used to distinguish myelin-water from tissue water and to determine the myelin-water fraction (MWF) in the frontal, temporal, parietal, occipital, and insular lobes. A linear regression model including age and sex as covariates was used to investigate group differences. Furthermore, the relationship of MWF with cognitive performance and epilepsy characteristics was determined. RESULTS: The frontal lobe revealed a significantly lower myelin-water content in children with CAE compared to controls over the developmental age range of 6-12 years (5.7 ± 1.0% vs 6.6 ± 1.1%, P = 0.02). This association was not found for any of the other four lobes (P > 0.10). No significant relation was found between myelin-water content and cognitive performance or epilepsy characteristics. SIGNIFICANCE: The lower frontal myelin-water content of children with CAE in comparison with healthy controls probably reflects an altered neurodevelopmental aspect in CAE, of which the underlying mechanisms still need to be unraveled.

Peripheral Pain Captured Centrally: Altered Brain Morphology on MRI in Small Fiber Neuropathy Patients With and Without an SCN9A Gene Variant.

The current study aims to characterize brain morphology of pain as reported by small fiber neuropathy (SFN) patients with or without a gain-of-function variant involving the SCN9A gene and compare these with findings in healthy controls without pain. The Neuropathic Pain Scale was used in patients with idiopathic SFN (N = 20) and SCN9A-associated SFN (N = 12) to capture pain phenotype. T1-weighted, structural magnetic resonance imaging (MRI) data were collected in patients and healthy controls (N = 21) to 1) compare cortical thickness and subcortical volumes and 2) quantify the association between severity, quality, and duration of pain with morphological properties. SCN9A-associated SFN patients showed significant (P < .017, Bonferroni corrected) higher cortical thickness in sensorimotor regions, compared to idiopathic SFN patients, while lower cortical thickness was found in more functionally diverse regions (eg, posterior cingulate cortex). SFN patient groups combined demonstrated a significant (Spearman's ρ = .44-.55, P = .005-.049) correlation among itch sensations (Neuropathic Pain Scale-7) and thickness of the left precentral gyrus, and midcingulate cortices. Significant associations were found between thalamic volumes and duration of pain (left: ρ = -.37, P = .043; right: ρ = -.40, P = .025). No associations were found between morphological properties and other pain qualities. In conclusion, in SCN9A-associated SFN, profound morphological alterations anchored within the pain matrix are present. The association between itch sensations of pain and sensorimotor and midcingulate structures provides a novel basis for further examining neurobiological underpinnings of itch in SFN. PERSPECTIVE: Cortical thickness and subcortical volume alterations in SFN patients were found in pain hubs, more profound in SCN9A-associated neuropathy, and correlated with itch and durations of pain. These findings contribute to our understanding of the pathophysiological pathways underlying chronic neuropathic pain and symptoms of itch in SFN.

DeepFLAIR: A neural network approach to mitigate signal and contrast loss in temporal lobes at 7 Tesla FLAIR images.

BACKGROUND AND PURPOSE: Higher magnetic field strength introduces stronger magnetic field inhomogeneities in the brain, especially within temporal lobes, leading to image artifacts. Particularly, T2-weighted fluid-attenuated inversion recovery (FLAIR) images can be affected by these artifacts. Here, we aimed to improve the FLAIR image quality in temporal lobe regions through image processing of multiple contrast images via machine learning using a neural network. METHODS: Thirteen drug-resistant MR-negative epilepsy patients (age 29.2 ± 9.4y, 5 females) were scanned on a 7 T MRI scanner. Magnetization-prepared (MP2RAGE) and saturation-prepared with 2 rapid gradient echoes, multi-echo gradient echo with four echo times, and the FLAIR sequence were acquired. A voxel-wise neural network was trained on extratemporal-lobe voxels from the acquired structural scans to generate a new FLAIR-like image (i.e., deepFLAIR) with reduced temporal lobe inhomogeneities. The deepFLAIR was evaluated in temporal lobes through signal-to-noise (SNR), contrast-to-noise (CNR) ratio, the sharpness of the gray-white matter boundary and joint-histogram analysis. Saliency mapping demonstrated the importance of each input image per voxel. RESULTS: SNR and CNR in both gray and white matter were significantly increased (p < 0.05) in the deepFLAIR's temporal ROIs, compared to the FLAIR. The gray-white matter boundary sharpness was either preserved or improved in 10/13 right-sided temporal regions and was found significantly increased in the ROIs. Multiple image contrasts were influential for the deepFLAIR reconstruction with the MP2RAGE second inversion image being the most important. CONCLUSIONS: The deepFLAIR network showed promise to restore the FLAIR signal and reduce contrast attenuation in temporal lobe areas. This may yield a valuable tool, especially when artifact-free FLAIR images are not available.

Assessment of the clinical feasibility of detecting subtle blood-brain barrier leakage in cerebral small vessel disease using dynamic susceptibility contrast MRI.

PURPOSE: Cerebral small vessel disease (cSVD) involves several pathologies affecting the small vessels, including blood-brain barrier (BBB) impairment. Dynamic susceptibility contrast (DSC) MRI is sensitive to both blood perfusion and BBB leakage, and correction methods may be crucial for obtaining reliable perfusion measures. These methods might also be applicable to detect BBB leakage itself. This study investigated to what extent DSC-MRI can measure subtle BBB leakage in a clinical feasibility setting. METHODS: In vivo DCE and DSC data were collected from fifteen cSVD patients (71 (±10) years, 6F/9M) and twelve elderly controls (71 (±10) years, 4F/8M). DSC-derived leakage fractions were obtained using the Boxerman-Schmainda-Weisskoff method (K2). K2 was compared with the DCE-derived leakage rate Ki, obtained from Patlak analysis. Subsequently, differences were assessed between white matter hyperintensities (WMH), cortical gray matter (CGM), and normal-appearing white matter (NAWM). Additionally, computer simulations were performed to assess the sensitivity of DSC-MRI to BBB leakage. RESULTS: K2 showed significant differences between tissue regions (P < 0.001 for CGM-NAWM and CGM-WMH, and P = 0.001 for NAWM-WMH). Conversely, according to the computer simulations the DSC sensitivity was insufficient to measure subtle BBB leakage, as the K2 values were below the derived limit of quantification (4∙10-3 min-1). As expected, Ki was elevated in the WMH compared to CGM and NAWM (P < 0.001). CONCLUSIONS: Although clinical DSC-MRI seems capable to detect subtle BBB leakage differences between WMH and normal-appearing brain tissue it is not recommended. K2 as a direct measure for subtle BBB leakage remains ambiguous as its signal effects are due to mixed T1- and T2∗-weighting. Further research is warranted to better disentangle perfusion from leakage effects.

Exploring the core network of the structural covariance network in childhood absence epilepsy.

Childhood absence epilepsy (CAE) is a generalized pediatric epilepsy, which is generally considered to be a benign condition since most children become seizure-free before reaching adulthood. However, cognitive deficits and changes of brain morphological have been previously reported in CAE. These morphological changes, even if they might be very subtle, are not independent due to the underlying network structure and can be captured by the structural covariance network (SCN). In this study, SCNs were used to quantify the structural brain network for children with CAE as well as controls. Seventeen children with CAE (6-12y) and fifteen controls (6-12y) were included. To estimate the SCN, T1-weighted images were acquired and parcellated into 68 cortical regions. Graph measures characterizing the core network architecture, i.e. the assortativity and rich-club coefficient, were calculated for all individuals. Multivariable linear regression models, including age and sex as covariates, were used to assess differences between children with CAE and controls. Additionally, potential relations between the core network and cognitive performance was investigated. A lower assortativity (i.e. less efficiently organized core network organization) was found for children with CAE compared to controls. Moreover, better cognitive performance was found to relate to stronger assortative mixing pattern (i.e. more efficient core network structure). Rich-club coefficients did not differ between groups, nor relate to cognitions. The core network organization of the SCN in children with CAE tend to be less efficient organized compared to controls, and relates to cognitive performance, and therefore this study provides novel insights into the SCN organization in relation to CAE and cognition.