Search

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.

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.

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.

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.

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.

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.

Purpose To create a benchmark for the comparison of 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 The Edited-MRS reconstruction challenge had three tracks with the purpose of evaluating machine learning models trained to reconstruct simulated (Track 1), homogeneous in vivo (Track 2), and heterogeneous in vivo (Track 3) GABA-edited MRS data. Four quantitative metrics were used to evaluate the results: mean squared error (MSE), signal-to-noise ratio (SNR), linewidth, and a shape score metric that we proposed. Challenge participants were given three months to create, train and submit their models. Challenge organizers provided open access to a baseline U-NET model for initial comparison, as well as simulated data, in vivo data, and tutorials and guides for adding synthetic noise to the simulations.Results The most successful approach for Track 1 simulated data was a covariance matrix convolutional neural network model, while for Track 2 and Track 3 in vivo data, a vision transformer model operating on a spectrogram representation of the data achieved the most success. Deep learning (DL) based reconstructions with reduced transients achieved equivalent or better SNR, linewidth and fit error as conventional reconstructions with the full amount of transients. However, some DL models also showed the ability to optimize the linewidth and SNR values without actually improving overall spectral quality, pointing to the need for more robust metrics.Conclusion The edited-MRS reconstruction challenge showed that the top performing DL based edited-MRS reconstruction pipelines can obtain with a reduced number of transients equivalent metrics to conventional reconstruction pipelines using the full amount of transients. The proposed metric shape score was positively correlated with challenge track outcome indicating that it i

Background: Though mediotemporal lobe volume changes are well-known features of Alzheimer's disease (AD), grey matter volume changes may be distributed throughout the brain. These distributed changes are not independent due to the underlying network structure and can be described in terms of a structural covariance network (SCN). Objective: To investigate how the cortical brain organization is altered in AD we studied the mutual connectivity of hubs in the SCN, i.e., the rich-club. Methods: To construct the SCNs, cortical thickness was obtained from structural MRI for 97 participants (normal cognition, n = 37; mild cognitive impairment, n = 41; Alzheimer-type dementia, n = 19). Subsequently, rich-club coefficients were calculated from the SCN, and related to memory performance and hippocampal volume using linear regression. Results: Lower rich-club connectivity was related to lower memory performance as well as lower hippocampal volume. Conclusion: Therefore, this study provides novel evidence of reduced connectivity in hub areas in relation to AD-related cognitive impairments and atrophy.

Background: Disruptions in sleep are associated with Alzheimer’s disease (AD) progression. As cerebral waste clearance is most active during sleep, these reductions in sleep quality could induce impaired clearance function. Waste products are washed through the parenchyma in the interstitial fluid (ISF), its volume being regulated by the sleep-wake cycle. A smaller ISF-volume is found in awake states and after sleep deprivation. IntraVoxel Incoherent Motion (IVIM) MRI can detect the diffusion of cerebral ISF (Dint) and its volume fraction (fint). Using IVIM, the current exploratory study investigates whether alterations in a proxy of ISF-volume (fint) are related to hours of sleep or self-reported sleep quality. Method: Twenty neurotypical elderly subjects (Table 1) underwent ultra-high field MRI (7T research system, Siemens Healthcare GmbH, Erlangen, Germany) (Table 2). The intermediate diffusion components (Dint) in the range 1.5*10-3

BACKGROUND: Though mediotemporal lobe volume changes are well-known features of Alzheimer's disease (AD), grey matter volume changes may be distributed throughout the brain. These distributed changes are not independent due to the underlying network structure and can be described in terms of a structural covariance network (SCN).OBJECTIVE: To investigate how the cortical brain organization is altered in AD we studied the mutual connectivity of hubs in the SCN, i.e., the rich-club.METHODS: To construct the SCNs, cortical thickness was obtained from structural MRI for 97 participants (normal cognition, n = 37; mild cognitive impairment, n = 41; Alzheimer-type dementia, n = 19). Subsequently, rich-club coefficients were calculated from the SCN, and related to memory performance and hippocampal volume using linear regression.RESULTS: Lower rich-club connectivity was related to lower memory performance as well as lower hippocampal volume.CONCLUSION: Therefore, this study provides novel evidence of reduced connectivity in hub areas in relation to AD-related cognitive impairments and atrophy.

Epilepsy is a neurological disorder in which the network of nerve cells in the brain has been altered, and possibly even affected. The white matter fibers that connect brain areas play an important role in this. Briefly, in this thesis existing methods have been further developed to quickly and accurately measure myelin levels in the brain using magnetic resonance imaging (MRI). Myelin is an important element of the white matter that ensures efficient and fast communication between the different brain areas. This method was applied to children with epilepsy and it was shown that epilepsy involves abnormalities in the myelin level.

BACKGROUND AND PURPOSE: The process of myelination starts in utero around 20 weeks of gestation and continues through adulthood. We first set out to characterize the maturation of the tract-specific myelin content in healthy subjects from childhood (7-12 years) into adulthood (18-32 years). Second, we apply the resulting development graph to children with childhood absence epilepsy (CAE), a pediatric epilepsy that was previously characterized by changes in myelin content. METHODS: In a prospective cross-sectional study, 15 healthy children (7-12 years), 14 healthy adult participants (18-32 years) and 17 children with a clinical diagnosis of CAE (6-12 years) were included. For each participant, diffusion weighted images were acquired to reconstruct bundles of white matter tracts and multi-echo multi-slice GRASE images were acquired for myelin-water estimation. Subsequently, a tract-specific myelin development graph was constructed using the percentual difference in myelin-water content from childhood (12 year) to adulthood (25 year). RESULTS: The graph revealed myelination patterns, where tracts in the central regions myelinate prior to peripheral tracts and intra-hemispheric tracts as well as tracts in the left hemisphere myelinate prior to inter-hemispheric tracts and tracts in the right hemisphere, respectively. No significant differences were found in myelin-water content between children with CAE and healthy children for neither the early developing tracts, nor the tracts that develop in a later stage. However, the difference between the myelin-water of late and early developing tracts is significantly smaller in the children with CAE. CONCLUSION: These results indicate that CAE is associated with widespread neurodevelopmental myelin differences.

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 f int 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 f int in the WMHs, BG, and CSO, respectively. f int 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). f int 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 cS

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.

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.