T2-weighted Fluid-attenuated Inversion Recovery Images Research Articles

2 Higher White Matter Hyperintensity Load Adversely Affects Pre-Post Proximal Cognitive Training Performance in Healthy Older Adults

Objective:Cognitive training has shown promise for improving cognition in older adults. Aging involves a variety of neuroanatomical changes that may affect response to cognitive training. White matter hyperintensities (WMH) are one common age-related brain change, as evidenced by T2-weighted and Fluid Attenuated Inversion Recovery (FLAIR) MRI. WMH are associated with older age, suggestive of cerebral small vessel disease, and reflect decreased white matter integrity. Higher WMH load associates with reduced threshold for clinical expression of cognitive impairment and dementia. The effects of WMH on response to cognitive training interventions are relatively unknown. The current study assessed (a) proximal cognitive training performance following a 3-month randomized control trial and (b) the contribution of baseline whole-brain WMH load, defined as total lesion volume (TLV), on pre-post proximal training change.Participants and Methods:Sixty-two healthy older adults ages 65-84 completed either adaptive cognitive training (CT; n=31) or educational training control (ET; n=31) interventions. Participants assigned to CT completed 20 hours of attention/processing speed training and 20 hours of working memory training delivered through commercially-available Posit Science BrainHQ. ET participants completed 40 hours of educational videos. All participants also underwent sham or active transcranial direct current stimulation (tDCS) as an adjunctive intervention, although not a variable of interest in the current study. Multimodal MRI scans were acquired during the baseline visit. T1- and T2-weighted FLAIR images were processed using the Lesion Segmentation Tool (LST) for SPM12. The Lesion Prediction Algorithm of LST automatically segmented brain tissue and calculated lesion maps. A lesion threshold of 0.30 was applied to calculate TLV. A log transformation was applied to TLV to normalize the distribution of WMH. Repeated-measures analysis of covariance (RM-ANCOVA) assessed pre/post change in proximal composite (Total Training Composite) and sub-composite (Processing Speed Training Composite, Working Memory Training Composite) measures in the CT group compared to their ET counterparts, controlling for age, sex, years of education and tDCS group. Linear regression assessed the effect of TLV on post-intervention proximal composite and sub-composite, controlling for baseline performance, intervention assignment, age, sex, years of education, multisite scanner differences, estimated total intracranial volume, and binarized cardiovascular disease risk.Results:RM-ANCOVA revealed two-way group*time interactions such that those assigned cognitive training demonstrated greater improvement on proximal composite (Total Training Composite) and sub-composite (Processing Speed Training Composite, Working Memory Training Composite) measures compared to their ET counterparts. Multiple linear regression showed higher baseline TLV associated with lower pre-post change on Processing Speed Training sub-composite (ß = -0.19, p = 0.04) but not other composite measures.Conclusions:These findings demonstrate the utility of cognitive training for improving postintervention proximal performance in older adults. Additionally, pre-post proximal processing speed training change appear to be particularly sensitive to white matter hyperintensity load versus working memory training change. These data suggest that TLV may serve as an important factor for consideration when planning processing speed-based cognitive training interventions for remediation of cognitive decline in older adults.

Associations of Alzheimer's Disease Pathology and Small Vessel Disease With Cerebral White Matter Degeneration: A Tract-Based MR Diffusion Imaging Study.

White matter (WM) degeneration is a key feature of Alzheimer's disease (AD). However, the underlying mechanism remains unclear. To investigate how amyloid-β (Aβ), tau, and small vascular disease (SVD) jointly affect WM degeneration in subjects along AD continuum. Retrospective. 152 non-demented participants (age: 55.8-91.6, male/female: 66/86) from the ADNI database were included, classified into three groups using the A (Aβ)/T (tau)/N pathological scheme (Group 1: A-T-; Group 2: A+T-; Group 3: A+T+) based on positron emission tomography data. 3T; T1-weighted images, T2-weighted fluid-attenuated inversion recovery images, T2*-weighted images, diffusion-weighted spin-echo echo-planar imaging sequence (54 diffusion directions). Free-water diffusion model (generated parameters: free water, FW; tissue fractional anisotropy, FAt; tissue mean diffusivity, MDt); SVD total score; Neuropsychological tests. Linear regression analysis was performed to investigate the independent contribution of AD (Aβ and tau) and SVD pathologies to diffusion parameters in each fiber tract, first in the entire population and then in each subgroup. We also investigated associations between diffusion parameters and cognitive functions. The level of statistical significance was set at p < 0.05 (false discovery rate corrected). In the entire population, we found that: 1) Increased FW was significantly associated with SVD and tau, while FAt and MDt were significantly associated with Aβ and tau; 2) The spatial pattern of fiber tracts related to a certain pathological marker is consistent with the known distribution of that pathology; 3) Subgroup analysis showed that Group 2 and 3 had more alterations of FAt and MDt associated with Aβ and tau; 4) Diffusion imaging indices showed significant associations with cognitive score in all domains except memory. WM microstructural injury was associated with both AD and SVD pathologies, showing compartment-specific, tract-specific, and stage-specific WM patterns. 1 TECHNICAL EFFICACY: Stage 3.

Spatial heterogeneity of edema region uncovers survival-relevant habitat of Glioblastoma

ObjectivesThe composition and extent of edema (ED) region can reflect the aggressive degree of glioblastoma (GBM). Investigating its heterogeneity pattern and identifying the high-risk habitat may provide important prognostic information. MethodsWe prospectively collected 122 GBM patients from the Cancer Imaging Archive (TCIA) and 65 GBM patients from the local institution for the training cohort and external test cohort respectively. The signal intensities of ED region from T1-weighted contrast-enhanced images (T1CE) and T2-weighted fluid-attenuated inversion recovery images (FLAIR) were pooled together from each patient as a global matrix. Then, K-means clustering was applied, which could segment ED regions into several habitats (i.e., subregions). A group of radiomics features were extracted and radiomics signatures (RadScores) were derived. The high-risk habitat was identified and evaluated in light of the prognostic at the intra-regional, inter-regional, and model levels. Molecular analysis was also conducted to investigate the potential of the high-risk habitat in complementing biological information. ResultsAfter three levels comparison, the high-risk habitat was determined. When combing with the RadScores of enhanced tumor (ET), the concordance index (C-index) was leveraged from 0.658 to 0.677. When combining with clinical factors and RadScores of ET, the C-index increased to 0.770. For molecular analysis, we observed a more significant difference among groups in survival prediction after uniting MGMT methylation status and the high-risk habitat signature. ConclusionsThis study demonstrates that investigating the spatial heterogeneity of ED and identifying the high-risk habitat within it may provide more references for GBM treatment and prognosis studies.

Multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis and neuropsychiatric systemic lupus erythematosus.

To develop an MRI-based multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis (RRMS) and its mimicker neuropsychiatric systemic lupus erythematosus (NPSLE). A total of 112 patients with RRMS (n = 63) or NPSLE (n = 49) were assigned to training and test sets with a ratio of 3:1. All lesions across the whole brain were manually segmented on T2-weighted fluid-attenuated inversion recovery images. For each single lesion, 371 radiomics features were extracted and trained using machine learning algorithms, producing Radiomics Index for Lesion (RIL) for each lesion and a single-lesion radiomics model. Then, for each subject, single lesions were assigned to one of two disease courts based on their distance to decision threshold, and a Radiomics Index for Subject (RIS) was calculated as the mean RIL value of lesions on the higher-weighted court. Accordingly, a subject-level discrimination model was constructed and compared with performances of two radiologists. The subject-based discrimination model satisfactorily differentiated RRMS and NPSLE in both training (AUC = 0.967, accuracy = 0.892, sensitivity = 0.917, and specificity = 0.872) and test sets (AUC = 0.962, accuracy = 0.931, sensitivity = 1.000, and specificity = 0.875), significantly better than the single-lesion radiomics method (training: p < 0.001; test: p = 0.001) Besides, the discrimination model significantly outperformed the senior radiologist in the training set (training: p = 0.018; test: p = 0.077) and the junior radiologist in both the training and test sets (training: p = 0.008; test: p = 0.023). The multi-lesion radiomics model could effectively discriminate between RRMS and NPSLE, providing a supplementary tool for accurate differential diagnosis of the two diseases. • Radiomic features of brain lesions in RRMS and NPSLE were different. • The multi-lesion radiomics model constructed using a merging strategy was comprehensively superior to the single-lesion-based model for discrimination of RRMS and NPSLE. • The RRMS-NPSLE discrimination model showed a significantly better performance or a trend toward significance than the radiologists.

Benefit of Using Early Contrast-Enhanced 2D T2-Weighted Fluid-Attenuated Inversion Recovery Image to Detect Leptomeningeal Metastasis in Lung-Cancer Staging

Benefit of Using Early Contrast-Enhanced 2D T2-Weighted Fluid-Attenuated Inversion Recovery Image to Detect Leptomeningeal Metastasis in Lung-Cancer Staging

QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps

Background and PurposeChronic active multiple sclerosis (MS) lesions are characterized by a paramagnetic rim at the edge of the lesion and are associated with increased disability in patients. Quantitative susceptibility mapping (QSM) is an MRI technique that is sensitive to chronic active lesions, termed rim + lesions on the QSM. We present QSMRim-Net, a data imbalance-aware deep neural network that fuses lesion-level radiomic and convolutional image features for automated identification of rim + lesions on QSM. MethodsQSM and T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRI of the brain were collected at 3 T for 172 MS patients. Rim + lesions were manually annotated by two human experts, followed by consensus from a third expert, for a total of 177 rim + and 3986 rim negative (rim−) lesions. Our automated rim + detection algorithm, QSMRim-Net, consists of a two-branch feature extraction network and a synthetic minority oversampling network to classify rim + lesions. The first network branch is for image feature extraction from the QSM and T2-FLAIR, and the second network branch is a fully connected network for QSM lesion-level radiomic feature extraction. The oversampling network is designed to increase classification performance with imbalanced data. ResultsOn a lesion-level, in a five-fold cross validation framework, the proposed QSMRim-Net detected rim + lesions with a partial area under the receiver operating characteristic curve (pROC AUC) of 0.760, where clinically relevant false positive rates of less than 0.1 were considered. The method attained an area under the precision recall curve (PR AUC) of 0.704. QSMRim-Net out-performed other state-of-the-art methods applied to the QSM on both pROC AUC and PR AUC. On a subject-level, comparing the predicted rim + lesion count and the human expert annotated count, QSMRim-Net achieved the lowest mean square error of 0.98 and the highest correlation of 0.89 (95% CI: 0.86, 0.92). ConclusionThis study develops a novel automated deep neural network for rim + MS lesion identification using T2-FLAIR and QSM images.

Radiomics features based on T2-weighted fluid-attenuated inversion recoveryMRI predict the expression levels of CD44 and CD133 in lower grade gliomas.

Objective: To verify the association between CD44 and CD133 expression levels and the prognosis of patients with lower grade gliomas (LGGs) and constructing radiomic models to predict those two genes'expression levels before surgery. Materials & methods: Genomic data of patients with LGGand the corresponding T2-weighted fluid-attenuated inversion recovery images were downloaded from The Cancer Genome Atlasand The Cancer Imaging Archive, which were utilized for prognosis analysis, radiomic featureextraction and modelconstruction, respectively. Results & conclusion: CD44 and CD133 expression levels in LGG can significantly affect the prognosis of patients with LGG. Based on the T2-weighted fluid-attenuated inversion recovery images, the radiomic features can effectively predict the expression levels of CD44 and CD133 before surgery.

Elevated Aortic Pulse Wave Velocity Relates to Longitudinal Gray and White Matter Changes.

To determine whether baseline aortic stiffness, measured by aortic pulse wave velocity (PWV), relates to longitudinal cerebral gray or white matter changes among older adults. Baseline cardiac magnetic resonance imaging will be used to assess aortic PWV while brain magnetic resonance imaging will be used to assess gray matter and white matter hyperintensity (WMH) volumes at baseline, 18 months, 3 years, 5 years, and 7 years. Approach and Results: Aortic PWV (m/s) was quantified from cardiac magnetic resonance. Multimodal 3T brain magnetic resonance imaging included T1-weighted imaging for quantifying gray matter volumes and T2-weighted fluid-attenuated inversion recovery imaging for quantifying WMHs. Mixed-effects regression models related baseline aortic PWV to longitudinal gray matter volumes (total, frontal, parietal, temporal, occipital, hippocampal, and inferior lateral ventricle) and WMH volumes (total, frontal, parietal, temporal, and occipital) adjusting for age, sex, race/ethnicity, education, cognitive diagnosis, Framingham stroke risk profile, APOE (apolipoprotein E)-ε4 carrier status, and intracranial volume. Two hundred seventy-eight participants (73±7 years, 58% male, 87% self-identified as non-Hispanic White, 159 with normal cognition, and 119 with mild cognitive impairment) from the Vanderbilt Memory & Aging Project (n=335) were followed on average for 4.9±1.6 years with PWV measurements occurring from September 2012 to November 2014 and longitudinal brain magnetic resonance imaging measurements occurring from September 2012 to June 2021. Higher baseline aortic PWV was related to greater decrease in hippocampal (β=-3.6 [mm3/y]/[m/s]; [95% CI, -7.2 to -0.02] P=0.049) and occipital lobe (β=-34.2 [mm3/y]/[m/s]; [95% CI, -67.8 to -0.55] P=0.046) gray matter volume over time. Higher baseline aortic PWV was related to greater increase in WMH volume over time in the temporal lobe (β=17.0 [mm3/y]/[m/s]; [95% CI, 7.2-26.9] P<0.001). All associations may be driven by outliers. In older adults, higher baseline aortic PWV related to greater decrease in gray matter volume and greater increase in WMHs over time. Because of unmet cerebral metabolic demands and microvascular remodeling, arterial stiffening may preferentially affect certain highly active brain regions like the temporal lobes. These same regions are affected early in the course of Alzheimer disease.

Urinary sodium and potassium excretion and cerebrovascular health: a multimodal imaging study.

Dietary sodium and potassium intake are associated with stroke, but the potential mechanisms are unclear. We aimed to study the association between sodium and potassium intake and subclinical cerebrovascular health in hypertensive older males using multimodal magnetic resonance imaging. A total of 189 hypertensive male subjects without previous cardiovascular or cerebrovascular disease were included. Daily urinary sodium and potassium excretion were estimated from a fasting spot urine sample using a formula approach. A dedicated cerebrovascular health imaging protocol including vessel wall imaging, angiography, arterial spin labeling imaging and T2-weighted fluid-attenuated inversion recovery imaging was performed to study intracranial atherosclerosis, vascular rarefaction (defined as fewer discernible vessels on angiography), brain perfusion and small vessel disease, respectively. The mean age was 64.9 (± 7.2) years. The average daily urinary and potassium excretion was 4.7 (± 1.4) g/L and 2.1 (± 0.5) g/L, respectively. Increased urinary sodium excretion was associated with decreased cerebral blood flow and elevated urinary potassium excretion was associated with reduced prevalence of intracranial plaque. The associations remained significant after adjusting for covariates, even including blood pressure control. Quadratic regression analysis indicated a marginally significant U-shaped association between urinary sodium intake and white matter hyperintensity, which lost significance in fully adjusted models. No significant association of urinary sodium and potassium excretion with other cerebrovascular health measures was noted. We concluded that in hypertensive older males without overt cardiovascular disease, increased sodium intake and reduced potassium intake are associated with impaired subclinical cerebrovascular health.

A Deep Learning-based Model for Detecting Abnormalities on Brain MR Images for Triaging: Preliminary Results from a Multisite Experience.

To develop a deep learning model for detecting brain abnormalities on MR images. In this retrospective study, a deep learning approach using T2-weighted fluid-attenuated inversion recovery images was developed to classify brain MRI findings as "likely normal" or "likely abnormal." A convolutional neural network model was trained on a large, heterogeneous dataset collected from two different continents and covering a broad panel of pathologic conditions, including neoplasms, hemorrhages, infarcts, and others. Three datasets were used. Dataset A consisted of 2839 patients, dataset B consisted of 6442 patients, and dataset C consisted of 1489 patients and was only used for testing. Datasets A and B were split into training, validation, and test sets. A total of three models were trained: model A (using only dataset A), model B (using only dataset B), and model A + B (using training datasets from A and B). All three models were tested on subsets from dataset A, dataset B, and dataset C separately. The evaluation was performed by using annotations based on the images, as well as labels based on the radiology reports. Model A trained on dataset A from one institution and tested on dataset C from another institution reached an F1 score of 0.72 (95% CI: 0.70, 0.74) and an area under the receiver operating characteristic curve of 0.78 (95% CI: 0.75, 0.80) when compared with findings from the radiology reports. The model shows relatively good performance for differentiating between likely normal and likely abnormal brain examination findings by using data from different institutions.Keywords: MR-Imaging, Head/Neck, Computer Applications-General (Informatics), Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms© RSNA, 2021Supplemental material is available for this article.

Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model.

Background Missing MRI sequences represent an obstacle in the development and use of deep learning (DL) models that require multiple inputs. Purpose To determine if synthesizing brain MRI scans using generative adversarial networks (GANs) allows for the use of a DL model for brain lesion segmentation that requires T1-weighted images, postcontrast T1-weighted images, fluid-attenuated inversion recovery (FLAIR) images, and T2-weighted images. Materials and Methods In this retrospective study, brain MRI scans obtained between 2011 and 2019 were collected, and scenarios were simulated in which the T1-weighted images and FLAIR images were missing. Two GANs were trained, validated, and tested using 210 glioblastomas (GBMs) (Multimodal Brain Tumor Image Segmentation Benchmark [BRATS] 2017) to generate T1-weighted images from postcontrast T1-weighted images and FLAIR images from T2-weighted images. The quality of the generated images was evaluated with mean squared error (MSE) and the structural similarity index (SSI). The segmentations obtained with the generated scans were compared with those obtained with the original MRI scans using the dice similarity coefficient (DSC). The GANs were validated on sets of GBMs and central nervous system lymphomas from the authors' institution to assess their generalizability. Statistical analysis was performed using the Mann-Whitney, Friedman, and Dunn tests. Results Two hundred ten GBMs from the BRATS data set and 46 GBMs (mean patient age, 58 years ± 11 [standard deviation]; 27 men [59%] and 19 women [41%]) and 21 central nervous system lymphomas (mean patient age, 67 years ± 13; 12 men [57%] and nine women [43%]) from the authors' institution were evaluated. The median MSE for the generated T1-weighted images ranged from 0.005 to 0.013, and the median MSE for the generated FLAIR images ranged from 0.004 to 0.103. The median SSI ranged from 0.82 to 0.92 for the generated T1-weighted images and from 0.76 to 0.92 for the generated FLAIR images. The median DSCs for the segmentation of the whole lesion, the FLAIR hyperintensities, and the contrast-enhanced areas using the generated scans were 0.82, 0.71, and 0.92, respectively, when replacing both T1-weighted and FLAIR images; 0.84, 0.74, and 0.97 when replacing only the FLAIR images; and 0.97, 0.95, and 0.92 when replacing only the T1-weighted images. Conclusion Brain MRI scans generated using generative adversarial networks can be used as deep learning model inputs in case MRI sequences are missing. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Zhong in this issue. An earlier incorrect version of this article appeared online. This article was corrected on April 12, 2021.

Radiomics Repeatability Pitfalls in a Scan-Rescan MRI Study of Glioblastoma.

To determine the influence of preprocessing on the repeatability and redundancy of radiomics features extracted using a popular open-source radiomics software package in a scan-rescan glioblastoma MRI study. In this study, a secondary analysis of T2-weighted fluid-attenuated inversion recovery (FLAIR) and T1-weighted postcontrast images from 48 patients (mean age, 56 years [range, 22-77 years]) diagnosed with glioblastoma were included from two prospective studies (ClinicalTrials.gov NCT00662506 [2009-2011] and NCT00756106 [2008-2011]). All patients underwent two baseline scans 2-6 days apart using identical imaging protocols on 3-T MRI systems. No treatment occurred between scan and rescan, and tumors were essentially unchanged visually. Radiomic features were extracted by using PyRadiomics (https://pyradiomics.readthedocs.io/) under varying conditions, including normalization strategies and intensity quantization. Subsequently, intraclass correlation coefficients were determined between feature values of the scan and rescan. Shape features showed a higher repeatability than intensity (adjusted P < .001) and texture features (adjusted P < .001) for both T2-weighted FLAIR and T1-weighted postcontrast images. Normalization improved the overlap between the region of interest intensity histograms of scan and rescan (adjusted P < .001 for both T2-weighted FLAIR and T1-weighted postcontrast images), except in scans where brain extraction fails. As such, normalization significantly improves the repeatability of intensity features from T2-weighted FLAIR scans (adjusted P = .003 [z score normalization] and adjusted P = .002 [histogram matching]). The use of a relative intensity binning strategy as opposed to default absolute intensity binning reduces correlation between gray-level co-occurrence matrix features after normalization. Both normalization and intensity quantization have an effect on the level of repeatability and redundancy of features, emphasizing the importance of both accurate reporting of methodology in radiomics articles and understanding the limitations of choices made in pipeline design. Supplemental material is available for this article. © RSNA, 2020See also the commentary by Tiwari and Verma in this issue.

An incomplete Circle of Willis is not a risk factor for white matter hyperintensities: The Tromsø Study

ObjectiveThe Circle of Willis (CoW) is often underdeveloped or incomplete, leading to suboptimal blood supply to the brain. As hypoperfusion is thought to play a role in the aetiology of white matter hyperintensities (WMH), the objective of this study was to assess whether incomplete CoW variants were associated with increased WMH volumes compared to the complete CoW. MethodsIn a cross-sectional population sample of 1751 people (age 40–84 years, 46.4% men), we used an automated method to segment WMH using T1-weighted and T2-weighted fluid-attenuated inversion recovery image obtained at 3T. CoW variants were classified from time-of-flight scans, also at 3T. WMH risk factors, including age, sex, smoking and blood pressure, were obtained from questionnaires and clinical examinations. We used linear regression to examine whether people with incomplete CoW variants had greater volumes of deep WMH (DWMH) and periventricular WMH (PWMH) compared to people with the complete CoW, correcting for WMH risk factors. ResultsParticipants with incomplete CoW variants did not have significantly higher DWMH or PWMH volumes than those with complete CoW when accounting for risk factors. Age, pack-years smoking, and systolic blood pressure were risk factors for increased DWMH and PWMH volume. Diabetes was a unique risk factor for increased PWMH volume. ConclusionIncomplete CoW variants do not appear to be risk factors for WMH in the general population.

Risk Analysis and Management of Third Ventricular Colloid Cysts

Colloid cysts (CCs) are histologically benign lesions that carry the risk of causing obstructive hydrocephalus. The indication for surgery for symptomatic cysts is unquestioned; however, the management of incidentally detected lesions has remained controversial. We independently evaluated the CC risk score (CCRS) as a useful risk stratification scheme. The demographics, imaging characteristics, and clinical presentations of 103 patients with CC during a 10-year period were correlated with symptoms and the occurrence of hydrocephalus. The discriminate capacity of the CCRS was quantified and a decision algorithm formulated. The correlates of symptoms included age <65 years, diameter ≥7 mm, anterior location, hyperintensity on T2-weighted fluid-attenuated inversion recovery (FLAIR) and/or T2-weighted images, and headache at presentation without an alternative explanation. On multivariate analysis, age <65 years, diameter ≥7 mm, and headache at presentation predicted for symptomatic cysts. The CCRS was highly effective at identifying symptomatic patients and those with obstructive hydrocephalus. All patients with cyst growth and symptomatic progression had had an initial CCRS of ≥3 plus hyperintensity (preexisting or developing) on T2-weighted FLAIR images. The CCRS performed exceptionally well in distinguishing the highest risk patients. Closer follow-up is recommended for younger asymptomatic patients withcysts near the foramen of Monro. Hyperintensity on T2-weighted FLAIR images might indicate greater growth potential for small lesions.

Quantitative Measurement of Metal Accumulation in Brain of Patients With Wilson's Disease.

Currently, no study has evaluated metal accumulation in the brains of patients with Wilson's disease by using quantitative susceptibility mapping at 3T MRI. The objectives of this study were to qualitatively and quantitatively evaluate changes in magnetic susceptibility and R2* maps in deep gray matter nuclei to discriminate Wilson's disease patients from healthy controls and to evaluate their sensitivities in diagnosing Wilson's disease. Magnetic susceptibility and R2* maps and conventional T1-weighted, T2-weighted, and T2-weighted fluid-attenuated inversion recovery images were obtained from 17 Wilson's disease patients and 14 age-matched healthy controls on a 3T MRI scanner. Differences between Wilson's disease and healthy control groups in susceptibility and R2* values in multiple deep nuclei were evaluated using a Mann-Whitney U test and receiver operating characteristic curves. The correlations of susceptibility and R2* values with Unified Wilson's Disease Rating Scale score were also performed. Magnetic susceptibility and R2* can effectively distinguish different types of signal abnormalities. Magnetic susceptibility and R2* values in multiple deep nuclei of Wilson's disease patients were significantly higher than those in healthy controls. Magnetic susceptibility value in the substantia nigra had the highest area under the curve (0.888). There were positive correlations of the Unified Wilson's Disease Rating Scale score with susceptibility values in the caudate nucleus (r = 0.757, P = 0.011), putamen (r = 0.679, P = 0.031), and red nucleus (r = 0.638, P = 0.047), as well as R2* values in the caudate nucleus (r = 0.754, P = 0.012). Quantitative susceptibility mapping at 3T could be a useful tool to evaluate metal accumulation in deep gray matter nuclei of Wilson's disease patients. © 2020 International Parkinson and Movement Disorder Society.

Texture analysis based on ADC maps and T2-FLAIR images for the assessment of the severity and prognosis of ischaemic stroke

ObjectivesTo explore the feasibility of texture analysis based on T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) images and apparent diffusion coefficient (ADC) maps in the assessment of the severity and prognosis of ischaemic stroke using the National Institutes of Health Stroke Scale (NIHSS) and modified Rankin scale (mRS) scores, respectively. MethodsOverall, 116 patients diagnosed with subacute ischaemic stroke were included in this retrospective study. Based on T2-FLAIR images and ADC maps, 15 texture features were extracted from the ROIs of each patient using grey-level co-occurrence matrix (GLCM) and local binary pattern histogram Fourier (LBP-HF) methods. The correlations of NIHSS score on admission (NIHSSbaseline), NIHSS score 24 h after stroke onset (NIHSS24h) and mRS score with the texture features were evaluated using Spearman's partial correlations. The receiver operating characteristic (ROC) curve was used to compare the performance of the selected texture features in the evaluation of stroke severity and prognosis. ResultsTexture features derived from the T2-FLAIR images and ADC maps were correlated with NIHSS score and mRS score. EntropyADC and 0.75QuantileT2-FLAIR showed the best diagnostic performance for assessing stroke severity. The combination of EntropyADC and 0.75QuantileT2-FLAIR achieved a better performance in the evaluation of stroke severity (AUC = 0.7, p = 0.01) than either feature alone. Only 0.05QuantileT2-FLAIR was found to be correlated with mRS score, and none of the texture features were predictive of mRS score. ConclusionTexture features derived from T2-FLAIR images and ADC maps might serve as biomarkers to evaluate stroke severity, but were insufficient to predict stroke prognosis.

Evaluation of the 'ring sign' and the 'core sign' as a magnetic resonance imaging marker of disease activity and progression in clinically isolated syndrome and early multiple sclerosis.

BackgroundBrain lesions with a hypointense ring or core were described in multiple sclerosis on susceptibility weighted imaging.ObjectiveThe purpose of this study was to study the evolution and prognostic relevance of susceptibility weighted imaging hypointense lesions in clinically isolated syndrome and early multiple sclerosis.MethodsSixty-six early multiple sclerosis and clinically isolated syndrome patients were followed over a median period of 2.9 years (range 1.6–4.6 years) and underwent 3T magnetic resonance imaging including 3D susceptibility weighted imaging and T2-weighted fluid-attenuated inversion recovery. We assessed the presence of susceptibility weighted imaging hypointense core or ring lesions, and Expanded Disability Status Scale at baseline and follow-up.ResultsOf 611 lesions at baseline, 64 (10.5%) had a susceptibility weighted imaging hypointense core, and 28 (4.6%) had a susceptibility weighted imaging hypointense ring. Hypointense ring lesions were larger (p < 0.001) and more T1w hypointense (p = 0.002) than others. During follow-up, hypointense core lesions became susceptibility weighted imaging isointense (52 lesions, 81%); few developed into hypointense ring lesions (two lesions, 3%). Hypointense ring lesions did not shrink on T2-weighted fluid-attenuated inversion recovery images (p = 0.077, trend towards more enlargement compared to others), while hypointense core lesions more often shrunk in comparison to lesions without a hypointense core (p = 0.002). The number of susceptibility weighted imaging hypointense ring lesions at baseline correlated with Expanded Disability Status Scale progression at follow-up (p = 0.021, R = 0.289).ConclusionIn our cohort of patients with clinically isolated syndrome or early multiple sclerosis, susceptibility weighted imaging hypointense ring lesions were only rarely detectable, but did not shrink and were associated with future disability progression.

Optimal Detection of Subtle Gadolinium Leakage in CSF with Heavily T2-Weighted Fluid-Attenuated Inversion Recovery Imaging.

Pericortical enhancement on postcontrast FLAIR images is a marker for subtle leptomeningeal blood-brain barrier leakage. We explored the optimal FLAIR sequence parameters for the detection of low gadolinium concentrations within the CSF. On the basis of phantom experiments and human in vivo data, we showed that detection of subtle pericortical enhancement can be facilitated by using a relatively long TE. Future studies should choose their FLAIR sequence parameters carefully when assessing pericortical enhancement due to subtle blood-brain barrier leakage.

Brain MRI features and scoring of leukodystrophy in adult-onset Krabbe disease.

To perform a systematic analysis and scoring of brain MRI white matter hyperintensities (WMH) in adult-onset Krabbe disease. We retrospectively collected basic clinical data and the first available brain MRI from patients with confirmed Krabbe disease with first clinical manifestations beyond 10 years of age. Data were obtained from our reference center for lysosomal diseases (n = 6) and from contacted authors of published articles describing patients with adult-onset Krabbe disease (n = 15). T2-weighted fluid-attenuated inversion recovery images of each patient were analyzed and scored using a radiologic score of WMH in a single center. The corticospinal tract was always affected by WMH (100% of patients), however, with some distinctions along the tract: the precentral gyrus (100%), corona radiata (95%), and posterior internal capsule (81%) were highly abnormal, whereas the mesencephalon (57%), pons (52%), and medulla oblongata (5%) were less affected. WMH were also frequently present in the posterior lateral periventricular white matter (95%), optic radiations (86%), postcentral gyrus (71%), medial lemniscus (62%), and corpus callosum, especially in the isthmus (71%), whereas the genu was always normal. A few patients did not have the classical MRI pattern but extensive hyperintensities (n = 3), or patchy distribution of hyperintensities mimicking an acquired etiology (n = 2), or very subtle hyperintensities of the corticospinal tract (n = 1). We specified the main locations of WMH, which were observed in the earliest stages of the disease and were also present in patients with atypical MRI pattern, highlighting the importance of radiologic features to guide the diagnosis.

Dynamic contrast-enhanced magnetic resonance imaging may act as a biomarker for vascular damage in normal appearing brain tissue after radiotherapy in patients with glioblastoma.

BackgroundDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising perfusion method and may be useful in evaluating radiation-induced changes in normal-appearing brain tissue.PurposeTo assess whether radiotherapy induces changes in vascular permeability (Ktrans) and the fractional volume of the extravascular extracellular space (Ve) derived from DCE-MRI in normal-appearing brain tissue and possible relationships to radiation dose given.Material and MethodsSeventeen patients with glioblastoma treated with radiotherapy and chemotherapy were included; five were excluded because of inconsistencies in the radiotherapy protocol or early drop-out. DCE-MRI, contrast-enhanced three-dimensional (3D) T1-weighted (T1W) images and T2-weighted fluid attenuated inversion recovery (T2-FLAIR) images were acquired before and on average 3.3, 30.6, 101.6, and 185.7 days after radiotherapy. Pre-radiotherapy CE T1W and T2-FLAIR images were segmented into white and gray matter, excluding all non-healthy tissue. Ktrans and Ve were calculated using the extended Kety model with the Parker population-based arterial input function. Six radiation dose regions were created for each tissue type, based on each patient’s computed tomography-based dose plan. Mean Ktrans and Ve were calculated over each dose region and tissue type.ResultsGlobal Ktrans and Ve demonstrated mostly non-significant changes with mean values higher for post-radiotherapy examinations in both gray and white matter compared to pre-radiotherapy. No relationship to radiation dose was found.ConclusionAdditional studies are needed to validate if Ktrans and Ve derived from DCE-MRI may act as potential biomarkers for acute and early-delayed radiation-induced vascular damages. No dose-response relationship was found.