T2 Fluid-attenuated Inversion Recovery Images Research Articles

Ultra-High Contrast MRI: The Whiteout Sign Shown with Divided Subtracted Inversion Recovery (dSIR) Sequences in Post-Insult Leukoencephalopathy Syndromes (PILS).

Ultra-high contrast (UHC) MRI describes forms of MRI in which little or no contrast is seen on conventional MRI images but very high contrast is seen with UHC techniques. One of these techniques uses the divided subtracted inversion recovery (dSIR) sequence, which, in modelling studies, can produce ten times the contrast of conventional inversion recovery (IR) sequences. When used in cases of mild traumatic brain injury (mTBI), the dSIR sequence frequently shows extensive abnormalities in white matter that appears normal when imaged with conventional T2-fluid-attenuated IR (T2-FLAIR) sequences. The changes are bilateral and symmetrical in white matter of the cerebral and cerebellar hemispheres. They partially spare the anterior and posterior central corpus callosum and peripheral white matter of the cerebral hemispheres and are described as the whiteout sign. In addition to mTBI, the whiteout sign has also been seen in methamphetamine use disorder and Grinker's myelinopathy (delayed post-hypoxic leukoencephalopathy) in the absence of abnormalities on T2-FLAIR images, and is a central component of post-insult leukoencephalopathy syndromes. This paper describes the concept of ultra-high contrast MRI, the whiteout sign, the theory underlying the use of dSIR sequences and post-insult leukoencephalopathy syndromes.

Abstract WMP20: Deep Learning Radiomics Identifies White Matter Hyperintensity-Related Cognitive Decline Based on T2-FLAIR

Introduction: Early identification of white matter hyperintensity-related cognitive impairment (WMH-CI) through normal MRI images is of great significance for early clinical intervention to reduce the occurrence of dementia. The aim of this study was to develop a generalizable and interpretable deep learning model for screening WMH-related CI using radiomic features (RFs) from T2 fluid-attenuated inversion recovery (T2-FLAIR) images. Methods: A total of 783 subjects were enrolled from three medical centres. RFs for WMH lesions were extracted from T2-FLAIR images in each subject. A deep learning model with a hierarchical transformer architecture was used to leverage all extracted RFs, instead of feature selection, to develop and cross-validate the diagnostic model for cognitive dysfunction. Unsupervised domain adaptation was used to address the cross-centre data inconsistencies without labelling the outer centre data. A gradient-weighted class activation mapping approach was adopted to identify the RFs that contributed most to the diagnosis. Subgroup analysis, correlation analysis and mediation analysis were performed to confirm the clinical relevance of the model. Besides, we also verify the clinical importance of the model by microstructural pathology of WMH detected by diffusion tensor imaging. Results: The deep learning model showed robust diagnostic power for WMH related CI, with an area under the receiver operating curve (AUC) of 0.841±0.016 in the development cohort. The prediction accuracy, sensitivity, specificity, precision and recall were 0.793 ± 0.108, 0.798 ± 0.021, 0.800 ± 0.065, 0.716±0.055 and 0.793 ± 0.108, respectively. The model generalized well across different subgroups. It also performed well in other two external verification cohorts, with an AUC of 0.859 and 0.749, respectively. The visual representation showed that the most important features were textural features, which were also significantly correlated with clinical assessment scale and diffusion parameters. Conclusions: This study presents a non-invasive imaging biomarker that can identify WMH-CI patients and is applicable to all levels of hospitals since only conventional MRI images are needed.

Predicting cerebral glioma enhancement pattern using a machine learning-based magnetic resonance imaging radiomics model

To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery (T2-FLAIR) images for optimizing the workflow of magnetic resonance imaging (MRI) examinations of glioma patients. We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma, who were divided into enhancing and non-enhancing groups according to the enhancement pattern. Predictive radiomics models were established using Gaussian Process, Linear Regression, Linear Regression-Least absolute shrinkage and selection operator, Support Vector Machine, Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort (n=201)and tested both in the internal (n=85) and external validation cohorts (n=99). The receiver-operating characteristic curve was used to assess the predictive performance of the models. The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort, with areas under the curve (AUC) of 0.88 (95% CI: 0.81-0.94) and 0.80 (95% CI: 0.71-0.88), respectively. In the external validation cohort, the model showed an AUC of 0.81 (95% CI: 0.71-0.90) with sensitivity, specificity, positive predictive value and negative predictive value of 0.98, 0.61, 0.76 and 0.96, respectively. The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Association between impaired dynamic cerebral autoregulation and BBB disruption in reversible cerebral vasoconstriction syndrome

BackgroundHalf of the sufferers of reversible cerebral vasoconstriction syndrome (RCVS) exhibit imaging-proven blood-brain barrier disruption. The pathogenesis of blood-brain barrier disruption in RCVS remains unclear and mechanism-specific intervention is lacking. We speculated that cerebrovascular dysregulation might be associated with blood-brain barrier disruption in RCVS. Hence, we aimed to evaluate whether the dynamic cerebral autoregulation is altered in patients with RCVS and could be associated with blood-brain barrier disruption.MethodsA cross-sectional study was conducted from 2019 to 2021 at headache clinics of a national tertiary medical center. Dynamic cerebral autoregulation was evaluated in all participants. The capacity of the dynamic cerebral autoregulation to damp the systemic hemodynamic changes, i.e., phase shift and gain between the cerebral blood flow and blood pressure waveforms in the very-low- and low-frequency bands were calculated by transfer function analysis. The mean flow correlation index was also calculated. Patients with RCVS received 3-dimensional isotropic contrast-enhanced T2 fluid-attenuated inversion recovery imaging to visualize blood-brain barrier disruption.ResultsForty-five patients with RCVS (41.9 ± 9.8 years old, 29 females) and 45 matched healthy controls (41.4 ± 12.5 years old, 29 females) completed the study. Nineteen of the patients had blood-brain barrier disruption. Compared to healthy controls, patients with RCVS had poorer dynamic cerebral autoregulation, indicated by higher gain in very-low-frequency band (left: 1.6 ± 0.7, p = 0.001; right: 1.5 ± 0.7, p = 0.003; healthy controls: 1.1 ± 0.4) and higher mean flow correlation index (left: 0.39 ± 0.20, p = 0.040; right: 0.40 ± 0.18, p = 0.017; healthy controls: 0.31 ± 0.17). Moreover, patients with RCVS with blood-brain barrier disruption had worse dynamic cerebral autoregulation, as compared to those without blood-brain barrier disruption, by having less phase shift in very-low- and low-frequency bands, and higher mean flow correlation index.ConclusionsDysfunctional dynamic cerebral autoregulation was observed in patients with RCVS, particularly in those with blood-brain barrier disruption. These findings suggest that impaired cerebral autoregulation plays a pivotal role in RCVS pathophysiology and may be relevant to complications associated with blood-brain barrier disruption by impaired capacity of maintaining stable cerebral blood flow under fluctuating blood pressure.Graphical

Deep learning-based grading of white matter hyperintensities enables identification of potential markers in multi-sequence MRI data

BackgroundWhite matter hyperintensities (WMHs) are widely-seen in the aging population, which are associated with cerebrovascular risk factors and age-related cognitive decline. At present, structural atrophy and functional alterations coexisted with WMHs lacks comprehensive investigation. This study developed a WMHs risk prediction model to evaluate WHMs according to Fazekas scales, and to locate potential regions with high risks across the entire brain. MethodsWe developed a WMHs risk prediction model, which consisted of the following steps: T2 fluid attenuated inversion recovery (T2-FLAIR) image of each participant was firstly segmented into 1000 tiles with the size of 32 × 32 × 1, features from the tiles were extracted using the ResNet18-based feature extractor, and then a 1D convolutional neural network (CNN) was used to score all tiles based on the extracted features. Finally, a multi-layer perceptron (MLP) was constructed to predict the Fazekas scales based on the tile scores. The proposed model was trained using T2-FLAIR images, we selected tiles with abnormal scores in the test set after prediction, and evaluated their corresponding gray matter (GM) volume, white matter (WM) volume, fractional anisotropy (FA), mean diffusivity (MD), and cerebral blood flow (CBF) via longitudinal and multi-sequence Magnetic Resonance Imaging (MRI) data analysis. ResultsThe proposed WMHs risk prediction model could accurately predict the Fazekas ratings based on the tile scores from T2-FLAIR MRI images with accuracy of 0.656, 0.621 in training data set and test set, respectively. The longitudinal MRI validation revealed that most of the high-risk tiles predicted by the WMHs risk prediction model in the baseline images had WMHs in the corresponding positions in the longitudinal images. The validation on multi-sequence MRI demonstrated that WMHs were associated with GM and WM atrophies, WM micro-structural and perfusion alterations in high-risk tiles, and multi-modal MRI measures of most high-risk tiles showed significant associations with Mini Mental State Examination (MMSE) score. ConclusionOur proposed WMHs risk prediction model can not only accurately evaluate WMH severities according to Fazekas scales, but can also uncover potential markers of WMHs across modalities. The WMHs risk prediction model has the potential to be used for the early detection of WMH-related alterations in the entire brain and WMH-induced cognitive decline.

T1 contrast versus fluid-attenuated inversion recovery contrast technique for evaluation of cerebral tuberculosis

Objectives: T2-weighted (T2W) fluid-attenuated inversion recovery (FLAIR), a traditional magnetic resonance imaging (MRI) technique, is regarded as one of the most effective methods for examining brain disorders. The identification of brain pathologies can be significantly aided by T2 FLAIR and T1 post-contrast imaging. However, lesion enhancement is occasionally inconspicuous on T1-weighted image. Hence, for the evaluation of brain disorders, a MRI approach known as FLAIR contrast would combine the benefits of T2W FLAIR with contrast-enhancement on a single picture. Material and Methods: After obtaining clearance from the Institution’s Ethics Committee, contrast-enhanced MRI (CE-MRI) brain of 20 patients was taken for the study. CE-MRI was performed using 1.5 Tesla Intera PHILIPS with intravenous administration of gadolinium at approximately 0.1 mL/kg/body at a rate of 10 mL/15 s. Results: This study showed a better appreciation of lesion conspicuity when compared to prior T1 contrast images. However, there was no significant difference in the number of lesions, lesion extension, and degree of contrast-enhancement on comparison with T1 contrast images. Conclusion: T1 FLAIR contrast shows a better appreciation of lesion conspicuity when compared to T1 contrast images. Post-contrast FLAIR imaging is a valuable adjunct to post-contrast T1W imaging and effectively delineates cerebral tubercular pathologies. However, T1 FLAIR cannot be used as a replacement for conventional T1 fat saturated post-contrast imaging.

Volume mismatch indicates tumors in paramedial bithalamic diseases: a retrospective study.

This study aimed to investigate the diagnostic performance of volume mismatch sign on discriminating paramedial bithalamic tumors from non-tumors. In this study, we recruited patients with tumors or non-tumors of the paramedial bithalamus. We confirmed the diagnosis by pathology, laboratory tests documented in medical records, medical imaging at the baseline, or through at least 1 year of follow-up. Cases with paramedial thalamic lesions on only one side or purely midbrain illnesses were excluded. Additionally, any case without involvement of the medial thalami (i.e., those with one or both-sided anterior, lateral, or posterior thalamic lesions) was excluded. Two neuroradiologists were trained independently to evaluate volume mismatch sign on magnetic resonance T2-weighted images or T2 fluid-attenuated inversion recovery images. A positive volume mismatch sign means that the ratio of the larger-sided lesion volume to the smaller-sided lesion volume is >150%. The volume of each lesion was calculated by multiplying the anteroposterior diameter by the left-right diameter and by the height of the lesion and then dividing by 2. The kappa value was calculated to show the consistency between the two observers. The chi-square test was used to evaluate differences in volume mismatch sign between the bilthalamic midline tumor and non-tumor groups. The positive (PPV) and negative (NPV) predictive values, sensitivity, and specificity were calculated to evaluate the ability of volume mismatch sign to differentiate paramedial bilateral thalamus tumors from non-tumors. A two-tailed P ≤ 0.05 was considered to be statistically significant. The analyses were performed using the statistical software SPSS version 26. A total of 96 patients were enrolled in this study between March 2012 and October 2022. A high agreement between the two observers on the volume mismatch sign of bilateral paramedian thalamic diseases was found, and the Kappa value was 0.828. A statistically significant difference was observed for the volume mismatch sign between the paramedial bithalamic tumor and the non-tumorous groups (χ2 = 35.465, P < 0.001). The presence of volume mismatch sign in paramedial bithalamic illnesses predicted the presence of tumors with a sensitivity and specificity of 69.2% and 90.9%, respectively, and PPV and NPV were 90.0% and 71.4%. Volume mismatch sign may indicate tumors in paramedian bithalamic diseases.

Introducing radiomics model to predict active plaque in multiple sclerosis patients using magnetic resonance images

Multiple Sclerosis (MS) is the most common non-traumatic disabling disease in young people. The prediction active plaque has the potential to offer new biomarkers for assessing the activity of MS disease. Consequently it supports patient management in the clinical setting and trials. This study aims to investigate the predictive capability of radiomics features for identifying active plaques in these patients using T2 FLAIR (Fluid Attenuated Inversion Recovery) images. For this purpose, a dataset images from 82 patients with 122 lesions was analyzed. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) method. Six different classifier algorithms, namely K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), were employed for modeling. The models were evaluated using 5-fold cross-validation, and performance metrics including sensitivity, specificity, accuracy, area under the curve (AUC), and mean squared error were computed. A total of 107 radiomics features were extracted for each lesion, and 11 robust features were identified through the feature selection process. These features consisted of four shape features (elongation, flatness, major axis length, mesh volume), one first-order feature (energy), one Gray Level Co-occurrence Matrix feature (correlation), two Gray Level Run Length Matrix features (gray level non-uniformity, gray level non-uniformity normalized), and three Gray Level Size Zone Matrix features (low gray level zone emphasis, size zone non-uniformity, small area low gray level emphasis). The NB classifier demonstrated the best performance with an AUC, sensitivity, and specificity of 0.85, 0.82, and 0.66, respectively. The findings indicate the potential of radiomics features in predicting active MS plaques in T2 FLAIR images.

Reducing Gadolinium Exposure in Patients Undergoing Monitoring for Meningiomas.

Background Due to the non-malignant and slow-growing nature of many meningiomas, surveillance with serial magnetic resonance imaging (MRI) serves as an acceptable management plan. However, repeated imaging with gold-standard contrast-based studies may lead to contrast-associated adverse effects. Non-gadolinium T2 sequences may serve as a suitable alternative without the risk of adverse effects of contrast. Thus, this study sought to investigate the agreement between post-contrast T1 and non-gadolinium T2 MRI sequences in the measurement of meningioma growth. Methodology The Virginia Commonwealth University School of Medicine (VCU SOM) brain tumor database was used to create a cohort of meningioma patients and determine the number of patients who had T1 post-contrast imaging accompanied by readily measurable imaging from either T2 fast spin echo (FSE) or T2 fluid-attenuated inversion recovery (FLAIR) sequences. Measurements of the largest axial and perpendicular diameters of each tumor were conducted by two independent observers using T1 post-contrast, T2 FSE, and T2 FLAIR imaging series.Lin's concordance correlation coefficient (CCC) was calculated to assess inter-rater reliability between observers and agreement between measurements of tumor diameter among the different imaging sequences. Results In total, 33 patients (average age = 72.1 ± 12.9 years, 90% female) with meningiomas were extracted from our database, with 22 (66.7%) undergoing T1 post-contrast imaging accompanied with readily measurable imaging from T2 FSE and/or T2 FLAIR sequences. The inter-rater reliability between the measurements of T1 axial and perpendicular diameters was 0.96 (95% confidence interval (CI) = 0.92-0.98) and 0.92 (95% CI = 0.83-0.97), respectively. The inter-rater reliability between the measurements of T2 axial perpendicular diameters was 0.93 (95% = CI 0.92-0.97) and 0.89 (95% CI = 0.74-0.95), respectively. The agreements between the measurement of T1 and T2 FSE axial diameter by each observer were 0.97 (95% CI = 0.93-0.98) and 0.92 (95% CI = 0.81-0.97). The agreements between the measurements of T1 and T2 FSE perpendicular diameter measurements by each observer were 0.98 (95% CI = 0.95-0.99) and 0.88 (95% CI = 0.73-0.95). Conclusions Two-thirds of our patients had meningiomas that were readily measurable on either T2 FSE or T2 FLAIR sequences. Additionally, there was excellent inter-rater reliability between the observers in our study as well as an agreement between individual measurements of T1 post-contrast and T2 FSE tumor diameters. These findings suggest that T2 FSE may serve as a safe and similarly effective surveillance method for the long-term management of meningioma patients.

Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters.

To construct a machine learning model based on radiomics of multiparametric magnetic resonance imaging (MRI) combined with clinical parameters for predicting Sonic Hedgehog (SHH) and Group 4 (G4) molecular subtypes of pediatric medulloblastoma (MB). The preoperative MRI images and clinical data of 95 patients with MB were retrospectively analyzed, including 47 cases of SHH subtype and 48 cases of G4 subtype. Radiomic features were extracted from T1-weighted imaging (T1), contrast-enhanced T1 weighted imaging (T1c), T2-weighted imaging (T2), T2 fluid-attenuated inversion recovery imaging (T2FLAIR), and apparent diffusion coefficient (ADC) maps, using variance thresholding, SelectKBest, and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms. The optimal features were filtered using LASSO regression, and a logistic regression (LR) algorithm was used to build a machine learning model. The receiver operator characteristic (ROC) curve was plotted to evaluate the prediction accuracy, and verified by its calibration, decision and nomogram. The Delong test was used to compare the differences between different models. A total of 17 optimal features, with non-redundancy and high correlation, were selected from 7,045 radiomics features, and used to build an LR model. The model showed a classification accuracy with an under the curve (AUC) of 0.960 (95% CI: 0.871-1.000) in the training cohort and 0.751 (95% CI: 0.587-0.915) in the testing cohort, respectively. The location of the tumor, pathological type, and hydrocephalus status of the two subtypes of patients differed significantly (p < 0.05). When combining radiomics features and clinical parameters to construct the combined prediction model, the AUC improved to 0.965 (95% CI: 0.898-1.000) in the training cohort and 0.849 (95% CI: 0.695-1.000) in the testing cohort, respectively. There was a significant difference in the prediction accuracy, as measured by AUC, between the testing cohorts of the two prediction models, which was confirmed by Delong's test (p = 0.0144). Decision curves and nomogram further validate that the combined model can achieve net benefits in clinical work. The combined prediction model, constructed based on radiomics of multiparametric MRI and clinical parameters can potentially provide a non-invasive clinical approach to predict SHH and G4 molecular subtypes of MB preoperatively.

Impaired peri-olfactory cerebrospinal fluid clearance is associated with ageing, cognitive decline and dyssomnia.

Animal experiments have demonstrated the dependency of cerebrospinal fluid clearance function on age and sleep, which partially underlay the cognitive decline in the elderly. However, human evidence is lacking, which could be mainly attributed to the limited methods of cerebrospinal fluid clearance function assessment. Serial T1-weighted and T2-fluid attenuated inversion recovery imaging were performed in 92 patients before and at multiple time points including 4.5h, 15h and 39h after intrathecal injection of contrast agent to visualize the putative meningeal lymphatic pathway, peri-olfactory nerve pathway, and peri-optic nerve pathway. We defined the clearance function as the percentage change in signal unit ratio of critical locations in these pathways from baseline to 39h after intrathecal injection, and further analysed their relationships with age, sleep, and cognitive function. Cerebrospinal fluid clearance through the putative meningeal lymphatic and perineural pathways were clearly visualized. The clearance function of putative meningeal lymphatic and perineural pathways were impaired with ageing (all P<0.05). The clearance function through peri-olfactory nerve pathway in inferior turbinate was positively correlated with sleep quality and cognitive function (both P<0.05), and mediated the association of sleep quality with cognitive function (percent change in β [bootstrap 95% CI]: 33% [-0.220,-0.007]). The impaired clearance through putative peri-olfactory nerve pathway may explain the cognitive decline in patients with sleep disturbance. The study shows a promising method to assess cerebrospinal fluid clearance function of putative peri-neural pathways via dynamic magnetic resonance imaging with intrathecal injection of contrast agent. This work was supported by the National Natural Science Foundation of China (81971101, 82171276 and 82101365).

Brain white matter hyperintensity lesion characterization in 3D T2 fluid-attenuated inversion recovery magnetic resonance images: Shape, texture, and their correlations with potential growth.

Analyses of age-related white matter hyperintensity (WMH) lesions manifested in T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have been mostly on understanding the size and location of the WMH lesions and rarely on the morphological characterization of the lesions. This work extends our prior analyses of the morphological characteristics and texture of WMH from 2D to 3D based on 3D T2 FLAIR images. 3D Zernike transformation was used to characterize WMH shape; a fuzzy logic method was used to characterize the lesion texture. We then clustered 3D WMH lesions into groups based on their 3D shape and texture features. A potential growth index (PGI) to assess dynamic changes in WMH lesions was developed based on the image texture features of the WMH lesion penumbra. WMH lesions with various sizes were segmented from brain images of 32 cognitively normal older adults. The WMH lesions were divided into two groups based on their size. Analyses of Variance (ANOVAs) showed significant differences in PGI among WMH shape clusters (P = 1.57 × 10-3 for small lesions; P = 3.14 × 10-2 for large lesions). Significant differences in PGI were also found among WMH texture group clusters (P = 1.79 × 10-6). In conclusion, we presented a novel approach to characterize the morphology of 3D WMH lesions and explored the potential to assess the dynamic morphological changes of WMH lesions using PGI.

White Matter Hyperintensities and Cerebral Microbleeds in Ataxia-Telangiectasia.

Background and ObjectivesTo systematically assess the occurrence of cerebral microbleeds (CMBs) and white matter hyperintensities (WMHs) in the largest published cohort of adults with ataxia-telangiectasia (AT).MethodsWe assessed 38 adults with AT (age range 18–55 years) including 15 classic and 23 variant AT, evaluated by two independent assessors. WMHs were quantified on T2-fluid attenuated inversion recovery images using the semiquantitative modified Scheltens and Fazekas scales and CMB on susceptibility-weighted imaging and T2*-weighted gradient echo sequences using the Brain Observer MicroBleed Scale.ResultsCMBs were more frequently found in classic AT compared with variant AT (66.7% vs 5.9%) predominantly in cortical and subcortical regions. WMHs were seen in 25 (73.5%) probands and CMBs in 9 (31.0%). The burden of WMHs increased with age, and WMHs were focused in periventricular and deep white matter regions. WMHs were more frequently seen in variant than classic AT.DiscussionThis cohort study confirms that WMHs and CMBs are a frequent finding in AT. Further longitudinal studies are required to understand how WMHs and CMBs relate to the neurodegeneration that occurs in AT and the predisposition to cerebral hemorrhage.

Radiomics for the Prediction of Epilepsy in Patients With Frontal Glioma.

ObjectiveThis study was conducted in order to investigate the association between radiomics features and frontal glioma-associated epilepsy (GAE) and propose a reliable radiomics-based model to predict frontal GAE.MethodsThis retrospective study consecutively enrolled 166 adult patients with frontal glioma (111 in the training cohort and 55 in the testing cohort). A total 1,130 features were extracted from T2 fluid-attenuated inversion recovery images, including first-order statistics, 3D shape, texture, and wavelet features. Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. Pearson correlation coefficient, 10-fold cross-validation, area under curve (AUC) analysis, and support vector machine were adopted to select the most relevant features to build a clinical model, a radiomics model, and a clinical–radiomics model for GAE. The receiver operating characteristic curve (ROC) and AUC were used to evaluate the classification performance of the models in each cohort, and DeLong’s test was used to compare the performance of the models. A two-sided t-test and Fisher’s exact test were used to compare the clinical variables. Statistical analysis was performed using SPSS software (version 22.0; IBM, Armonk, New York), and p <0.05 was set as the threshold for significance.ResultsThe classification accuracy of seven scout models, except the wavelet first-order model (0.793) and the wavelet texture model (0.784), was <0.75 in cross-validation. The clinical–radiomics model, including 17 magnetic resonance imaging-based features selected among the 1,130 radiomics features and two clinical features (patient age and tumor grade), achieved better discriminative performance for GAE prediction in both the training [AUC = 0.886, 95% confidence interval (CI) = 0.819–0.940] and testing cohorts (AUC = 0.836, 95% CI = 0.707–0.937) than the radiomics model (p = 0.008) with 82.0% and 78.2% accuracy, respectively.ConclusionRadiomics analysis can non-invasively predict GAE, thus allowing adequate treatment of frontal glioma. The clinical–radiomics model may enable a more precise prediction of frontal GAE. Furthermore, age and pathology grade are important risk factors for GAE.

Blood-Brain Barrier Permeability in Patients With Reversible Cerebral Vasoconstriction Syndrome Assessed With Dynamic Contrast-Enhanced MRI.

Blood-brain barrier (BBB) disruption has been proposed to be important in the pathogenesis of reversible cerebral vasoconstriction syndrome (RCVS), but not all patients present an identifiable macroscopic BBB disruption; that is, visible contrast leakage on contrast-enhanced T2 fluid-attenuated inversion recovery imaging. This study aimed to evaluate microscopic BBB permeability and its dynamic change in patients with RCVS. This prospective cohort implemented 3T dynamic contrast-enhanced MRI. We measured microscopic BBB permeability by determining the whole-brain and white matter hyperintensity (WMH) Ktrans values and evaluated the correlation of whole-brain Ktrans permeability with clinical and vascular measures in transcranial color-coded sonography. In total, 176 patients (363 scans) were analyzed and separated into acute (≦30 days) and remission (≧90 days) groups based on the onset-to-examination time. Whole-brain Ktrans values were similar between patients with and without macroscopic BBB disruption in either acute or remission stage. The whole-brain Ktrans was significantly decreased (p < 0.001) from acute to remission stages. The WMH Ktrans was significantly higher than mirror references and decreased from acute to remission stages (p < 0.001). Whole-brain Ktrans correlated with mean pulsatility index (r s = 0.5, p = 0.029), mean resistance index (r s = 0.662, p = 0.002), and distal-to-proximal ratio of resistance index (r s = 0.801, p < 0.001) of M1 segment of middle cerebral arteries at around 10-15 days after onset. The time-trend curve of whole-brain Ktrans depicted dynamic changes during disease course, similar to temporal trends of vasoconstrictions and WMH. Patients with RCVS presented increased microscopic brain permeability during acute stage, even without discernible macroscopic BBB disruption. The dynamic changes in BBB permeability may be related to impaired cerebral microvascular compliance and WMH formation.

MRI-Based Investigation of Association Between Cerebrovascular Structural Alteration and White Matter Hyperintensity Induced by High Blood Pressure.

High blood pressure (BP) is a common risk factor for cerebral small vessel disease including white matter hyperintensity (WMH). Whether increased BP exacerbates WMH by impacting cerebral vascular morphologies remains poorly studied. To determine the relationships among high BP, cerebrovascular morphologies, and WMH in elderly individuals. Cohort. Eight hundred sixty-three participants (54.2% female) from the Taizhou Imaging Study without clinical evidence of neurologic disorders were included in the analyses. 3.0 T; time-of-flight magnetic resonance angiography (TOF MRA); T2 fluid-attenuated inversion recovery (FLAIR); T1 magnetization-prepared rapid gradient-echo; gradient echo T2*-weighted; diffusion tensor imaging; pulsed arterial spin labeling. Cerebrovascular morphologic measurements were quantified based on the TOF MRA images, including vessel density, radius, tortuosity, and branch number. WMH lesion volumes (WMHV) and WMH lesion counts (WMHC) were calculated automatically based on the T2 FLAIR images. Multivariable linear regression analysis and path analysis with a linear single-mediator model were employed. A P value <0.05 was considered statistically significant. Higher BP, especially diastolic BP, was significantly correlated with lower cerebrovascular density (β=-104) and lower branch numbers (β=-0.02). Although decreased tortuosity (β=-1.25) and increased radius (β=93.8) were correlated with BP, no significant relationship of tortuosity (β=-4.6 × 10-4 , P=0.58) or radius (β=0.03, P=0.08) with BP in small vessels was found. The proportion of small vessels decreased as BP increased (SBP: β=-6.6 × 10-4 ; DBP: β=-9.0 × 10-4 ). Similarly, increased WMHV and WMHC were associated with decreased vessel density (volumes: β=-24, counts: β=-127), decreased tortuosity (volumes: β=-0.08, counts: β=-0.53), and increased radius (volumes: β=12.6, counts: β=86.6). Path analyses suggested an association between high BP and WMHs that were mediated by cerebrovascular morphologic changes. Structural alterations of cerebral vessels induced by high BP are correlated with WMH. This result suggested that elevated BP might be one of the pathophysiological mechanisms involving in the co-occurrence of cerebrovascular alteration and small vessel disease. 1 Technical Efficacy Stage: 1.

Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study.

PurposeWe propose three support vector machine (SVM) classifiers, using pre-and post-contrast T2 fluid-attenuated inversion recovery (FLAIR) subtraction and/or pre-and post-contrast T1WI subtraction, to differentiate treatment-related effects (TRE) from glioma recurrence.Materials and MethodsFifty-six postoperative high-grade glioma patients with suspicious progression after radiotherapy and chemotherapy from two centers were studied. Pre-and post-contrast T1WI and T2 FLAIR were collected. Each pre-contrast image was voxel-wise subtracted from the co-registered post-contrast image. Dataset was randomly split into training, and testing on a 7:3 ratio, accordingly subjected to a five fold cross validation. Best feature subsets were selected by Pearson correlation coefficient and recursive feature elimination, whereupon a radiomics classifier was built with SVM. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).ResultsIn all, 186 features were extracted on each subtraction map. Top nine T1WI subtraction features, top thirteen T2 FLAIR subtraction features and top thirteen combination features were selected to build optimal SVM classifiers accordingly. The accuracies/AUCs/sensitivity/specificity/PPV/NPV of SVM based on sole T1WI subtraction were 80.00%/80.00% (CI: 0.5370–1.0000)/100%/70.00%/62.50%/100%. Those results of SVM based on sole T2 FLAIR subtraction were 86.67%/84.00% (CI: 0.5962–1.0000)/100%/80%/71.43%/100%. Those results of SVM based on both T1WI subtraction and T2 FLAIR subtraction were 93.33%/94.00% (CI: 0.7778–1.0000)/100%/90%/83.33%/100%, respectively.ConclusionPre- and post-contrast T2 FLAIR subtraction provided added value for diagnosis between recurrence and TRE. SVM based on a combination of T1WI and T2 FLAIR subtraction maps was superior to the sole use of T1WI or T2 FLAIR for differentiating TRE from recurrence. The SVM classifier based on combination of pre-and post-contrast subtraction T2 FLAIR and T1WI imaging allowed for the accurate differential diagnosis of TRE from recurrence, which is of paramount importance for treatment management of postoperative glioma patients after radiation therapy.

Impairment of the Glymphatic Pathway and Putative Meningeal Lymphatic Vessels in the Aging Human.

Aging is a major risk factor for numerous neurological disorders, and the mechanisms underlying brain aging remain elusive. Recent animal studies demonstrated a tight relationship between impairment of the glymphatic pathway, meningeal lymphatic vessels, and aging. However, the relationship in the human brain remains uncertain. In this observational cohort study, patients underwent magnetic resonance imaging before and at multiple time points after intrathecal administration of a contrast agent. Head T1-weighted imaging was performed to assess the function of the glymphatic pathway and head high-resolution T2-fluid attenuated inversion recovery imaging to visualize putative meningeal lymphatic vessels (pMLVs). We measured the signal unit ratio (SUR) of 6 locations in the glymphatic pathway and pMLVs, defined the percentage change in SUR from baseline to 39 hours as the clearance of the glymphatic pathway and pMLVs, and then analyzed their relationships with aging. In all patients (N = 35), the SUR of the glymphatic pathway and pMLVs changed significantly after intrathecal injection of the contrast agent. The clearance of both the glymphatic pathway and pMLVs was related to aging (all p < 0.05). The clearance of pMLVs was significantly related to the clearance of the glymphatic pathway (all p < 0.05), and the clearance of the glymphatic pathway was significantly faster in patients with early filling of pMLVs than those with late filling (all p < 0.05). We revealed that both the glymphatic pathway and pMLVs might be impaired in the aging human brain through the novel, clinically available method to simultaneously visualize their clearance. Our findings also support that in humans, pMLVs are the downstream of the glymphatic pathway. Ann Neurol 2020;87:357-369.

Effect of MR Field Strength on the Texture Features of Cerebral T2-FLAIR Images: A Pilot Study

Objective To investigate effect of MR field strength on texture features of cerebral T2 fluid attenuated inversion recovery (T2-FLAIR) images. Methods We acquired cerebral 3D T2-FLAIR images of thirty patients who were diagnosed with ischemic white matter lesion (WML) with MR-1.5T and MR-3.0T scanners. Histogram texture features which included mean signal intensity (Mean), Skewness and Kurtosis, and gray level co-occurrence matrix (GLCM) texture features which included angular second moment (ASM), Contrast, Correlation, Inverse difference moment (IDM) and Entropy, of regions of interest located in the area of WML and normal white matter (NWM) were measured by ImageJ software. The texture parameters acquired with MR-1.5T scanning were compared with MR-3.0T scanning. Results The Mean of both WML and NWM obtained with MR-1.5T scanning was significantly lower than that acquired with MR-3.0T (P<0.001), while Skewness and Kurtosis between MR-1.5T and MR-3.0T scanning showed no significant difference (P>0.05). ASM, Correlation and IDM of both WML and NWM acquired with MR-1.5T revealed significantly lower values than those with MR-3.0T (P<0.001), while Contrast and Entropy acquired with MR-1.5T showed significantly higher values than those with MR-3.0T (P<0.001). Conclusion MR field strength showed no significant effect on histogram textures, while had significant effect on GLCM texture features of cerebral T2-FLAIR images, which indicated that it should be cautious to explain the texture results acquired based on the different MR field strength.

Brain White Matter Hyperintensity Lesion Characterization in T2 Fluid-Attenuated Inversion Recovery Magnetic Resonance Images: Shape, Texture, and Potential Growth.

Prior methods in characterizing age-related white matter hyperintensity (WMH) lesions on T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have mainly been limited to understanding the sizes of, and occasionally the locations of WMH lesions. Systematic morphological characterization has been missing. In this work, we proposed innovative methods to fill this knowledge gap. We developed an innovative and proof-of-concept method to characterize and quantify the shape (based on Zernike transformation) and texture (based on fuzzy logic) of WMH lesions. We have also developed a multi-dimension feature vector approach to cluster WMH lesions into distinctive groups based on their shape and then texture features. We then developed an approach to calculate the potential growth index (PGI) of WMH lesions based on the image intensity distributions at the edge of the WMH lesions using a region-growing algorithm. High-quality T2 FLAIR images containing clearly identifiable WMH lesions with various sizes from six cognitively normal older adults were used in our method development Analyses of Variance (ANOVAs) showed significant differences in PGI among WMH group clusters in terms of either the shape (P = 1.06 × 10−2) or the texture (P < 1 × 10−20) features. In conclusion, we propose a systematic framework on which the shape and texture features of WMH lesions can be quantified and may be used to predict lesion growth in older adults.