T2-weighted FLAIR Research Articles

ANGI-11. SEX DIFFERENCES IN SOX2 GLIOBLASTOMA EXPRESSION OUTSIDE MRI-DEFINED CONTRAST ENHANCEMENT AT AUTOPSY

Abstract INTRODUCTION Sex-determining region Y-box 2 (SOX2) has recently been used as a marker for pluripotency to identify cellular glioblastoma invasion pathologically. This can be used to identify areas of tumor invasion consisting of sparse individual cells, likely highlighting the furthest margin of tumor invasion. This study aligned large format SOX2 stained tissue samples to conventional MRI to determine demographic factors that impact the presence of SOX2 positive invasion outside the T1 contrast-enhanced tumor margin. METHODS The cohort for this study consisted of 36 IDH-wildtype glioblastoma patients (23 male, 13 female, mean age=61.9). Tissue samples from areas of suspected tumor invasion and normal tissue were collected at autopsy, stained for SOX2, digitized at 40X resolution, and automatically segmented for SOX2 positive staining cell percent (SOX2+%) at MR resolution using a color deconvolution algorithm. Contrast enhanced T1 scans from acquisitions close to death were annotated manually for tumor presence and aligned to the T2-weighted FLAIR image. Tissue data was then aligned to MR images using a custom in-house software that relies on manually defined control points to generate a nonlinear transform that warps tissue into MR space while correcting for tissue shrinkage. Mean SOX2+% was computed for each patient outside of contrast enhancement and compared across age and sex as demographic factors. RESULTS Males were found to have a higher SOX2+% than females outside contrast enhancement (t=2.09, p=0.045), while no association with age was observed (r=0.146, p=0.397). CONCLUSIONS Sex differences were observed for non-enhancing SOX2 positivity, which may relate to the more aggressive tumors identified in male patients in some studies. Further research into the mechanisms behind sex differences in tumor growth patterns and molecular characteristics impacting the infiltration of tumor beyond the MR defined region is needed.

Extracutaneous Lesions in Patients with Focal Scleroderma of Craniofacial Localization: a Retrospective Cross-Sectional Study

Background. Focal scleroderma is rare, acquired disease characterized by inflammation, sclerosis, skin and underlying tissues atrophy, with no lesions to internal organs. However, craniofacial localized scleroderma (CLS) can lead to neurological, ophthalmic, and maxillofacial changes. Extracutaneous lesions at limited scleroderma remain poorly studied despite its high prevalence. Objective. The aim of the study is to characterize brain anomalies revealed at neuroimaging and to analyze their incidence in pediatric patients with focal scleroderma, as well as to describe comorbid neurological and ophthalmic disorders. Methods. Descriptive retrospective cross-sectional study included 33 pediatric patients who underwent hospital treatment at dermatology and allergology department of National Medical Research Center of Children’s Health with diagnosis of focal scleroderma from 2016 to 2024. All patients were examined by neurologist and ophthalmologist during hospitalization, as well as magnetic resonance imaging (MRI) was performed. The incidence of neurological, ophthalmic, and radiological signs was evaluated in the studied patients as well as their correlations within the data analysis. Results. 33 pediatric patients from 3 to 17 years old were studied. The number of boys and girls was not significantly different (p=0.46). MRI neuroimaging changes were revealed in 17 of 33 patients (51.5%). 12 patients (36.4%) had isolated disorders, 5 (15.1%) — multiple (two or more types) according to MRI data. Neurological disorders were reported in 8 out of 33 patients (24%). Ophthalmic disorders — in 10 out of 33 (30.3%). The study has revealed correlation indicating that the presence of epilepsy in CLS is associated with changes in the brain structure according to MRI (hyperintensive signal at T2-weighted image and FLAIR) (p=0,022). Also, such changes as hypointense signal during T2-VI are correlated with tension headache (p=0.03). No correlation between ophthalmic changes and neuroimaging changes was revealed. Conclusion. Significant number of pediatric patients with CLS have wide range of ophthalmic and neurological manifestations, including neuroimaging abnormalities. Precise and timely diagnosis of these symptoms is crucial for determining treatment intervention.

Associations between structural neuroimaging markers of Alzheimer's risk and scam susceptibility.

Older adults with greater scam susceptibility are at greater risk for mild cognitive impairment and incident Alzheimer's dementia, regardless of baseline cognition. This, combined with documented associations between scam susceptibility and beta amyloid at death suggests that scam susceptibility may be an earlier indicator of pathological aging than cognition. Little, however, is known about whether in vivo neuroimaging markers of early-stage risk for Alzheimer's dementia are also related to scam susceptibility; such knowledge will inform upon the associations of neurodegenerative processes with scam susceptibility and may help identify vulnerable individuals. Participants were 472 community-based adults without dementia (age ~ 81y; 75% women) from the Rush Memory and Aging Project. Baseline 3T MRI T1-weighted structural and T2-weighted FLAIR data were used to assess the cortical thickness 'signature' of Alzheimer's disease (AD-CT) and white matter hyperintensity (WMH) burden, respectively. Scam susceptibility was measured using a questionnaire that assessed behaviors associated with vulnerability to fraud and scams. Demographically-adjusted linear effects regression models determined the relationship of each neuroimaging measure, first separately and then combined, with scam susceptibility. Reduced AD-CT was associated with higher levels of scam susceptibility (estimate=-0.10, standard error = 0.03, p = 0.002). WMH burden was not associated with scam susceptibility either alone or when combined in the same model as AD-CT (p-values ≥ 0.14). Results for AD-CT persisted after the inclusion of WMH burden. AD-CT was associated with scam susceptibility in older adults without dementia possibly signaling an in vivo profile of this behavior.

E.1 Focal leptomeningeal vascular anomalies on brain MRI: a mimic of leptomeningeal metastatic disease

Background: The diagnosis of leptomeningeal metastatic disease has major prognostic implications. We report 13 patients with a radiologically distinct, focal, enhancing leptomeningeal lesion on brain MRI mimicking leptomeningeal metastatic disease. Methods: These patients were assessed at University Health Network between January 2001 and December 2023. Results: Median age was 68 years and 10 patients were women. All patients had brain MRI including contrast-enhanced T2-weighted FLAIR and T1-weighted spin echo sequences. MRI in all patients showed a focal enhancing lesion along the leptomeningeal surface of the brain. The MRI exams were reported as possible metastatic disease for the majority (9/13) of patients. Each lesion was curvilinear rather than sheet-like, and some consisted of multiple connected/branching curvilinear structures with the appearance of abnormal vessels. Some lesions had visible connection with a nearby cortical vein. The lesions were distinct from normal blood vessels. Follow-up contrast-enhanced brain MRI for 8/13 patients at a median of 3.9 years showed all lesions were unchanged over time. Conclusions: We describe a distinct kind of focal, enhancing leptomeningeal lesion on brain MRI that mimics metastatic disease. These lesions are likely a type of low-flow vascular anomaly. Their curvilinear/branching shape and intense enhancement particularly on T2-weighted FLAIR images distinguishes these lesions from tumor.

Cortical Lesions as an Early Hallmark of Multiple Sclerosis: Visualization by 7 T MRI.

Compelling evidence indicates a significant involvement of cortical lesions in the progressive phase of multiple sclerosis (MS), significantly contributing to late-stage disability. Despite the promise of ultra-high-field magnetic resonance imaging (MRI) in detecting cortical lesions, current evidence falls short in providing insights into the existence of such lesions during the early stages of MS or their underlying cause. This study delineated, at the early stage of MS, (1) the prevalence and spatial distribution of cortical lesions identified by 7 T MRI, (2) their relationship with white matter lesions, and (3) their clinical implications. Twenty individuals with early-stage relapsing-remitting MS (disease duration <1 year) underwent a 7 T MRI session involving T1-weighted MP2RAGE, T2*-weighted multiGRE, and T2-weighted FLAIR sequences for cortical and white matter segmentation. Disability assessments included the Expanded Disability Status Scale, the Multiple Sclerosis Functional Composite, and an extensive evaluation of cognitive function. Cortical lesions were detected in 15 of 20 patients (75%). MP2RAGE revealed a total of 190 intracortical lesions (median, 4 lesions/case [range, 0-44]) and 216 leukocortical lesions (median, 2 lesions/case [range, 0-75]). Although the number of white matter lesions correlated with the total number of leukocortical lesions (r = 0.91, P < 0.001), no correlation was observed between the number of white matter or leukocortical lesions and the number of intracortical lesions. Furthermore, the number of leukocortical lesions but not intracortical or white-matter lesions was significantly correlated with cognitive impairment (r = 0.63, P = 0.04, corrected for multiple comparisons). This study highlights the notable prevalence of cortical lesions at the early stage of MS identified by 7 T MRI. There may be a potential divergence in the underlying pathophysiological mechanisms driving distinct lesion types, notably between intracortical lesions and white matter/leukocortical lesions. Moreover, during the early disease phase, leukocortical lesions more effectively accounted for cognitive deficits.

Deep learning denoising reconstruction enables faster T2-weighted FLAIR sequence acquisition with satisfactory image quality.

Deep learning reconstruction (DLR) technologies are the latest methods attempting to solve the enduring problem of reducing MRI acquisition times without compromising image quality. The clinical utility of this reconstruction technique is yet to be fully established. This study aims to assess whether a commercially available DLR technique applied to 2D T2-weighted FLAIR brain images allows a reduction in scan time, without compromising image quality and thus diagnostic accuracy. 47 participants (24 male, mean age 55.9 ± 18.7 SD years, range 20-89 years) underwent routine, clinically indicated brain MRI studies in March 2022, that included a standard-of-care (SOC) T2-weighted FLAIR sequence, and an accelerated acquisition that was reconstructed using the DLR denoising product. Overall image quality, lesion conspicuity, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artefacts for each sequence, and preferred sequence on direct comparison, were subjectively assessed by two readers. There was a strong preference for SOC FLAIR sequence for overall image quality (P = 0.01) and head-to-head comparison (P < 0.001). No difference was observed for lesion conspicuity (P = 0.49), perceived SNR (P = 1.0), and perceived CNR (P = 0.84). There was no difference in motion (P = 0.57) nor Gibbs ringing (P = 0.86) artefacts. Phase ghosting (P = 0.038) and pseudolesions were significantly more frequent (P < 0.001) on DLR images. DLR algorithm allowed faster FLAIR acquisition times with comparable image quality and lesion conspicuity. However, an increased incidence and severity of phase ghosting artefact and presence of pseudolesions using this technique may result in a reduction in reading speed, efficiency, and diagnostic confidence.

MRI Investigation of the Association of Left Atrial and Left Atrial Appendage Hemodynamics with Silent Brain Infarction.

Left atrial (LA) myopathy is thought to be associated with silent brain infarctions (SBI) through changes in blood flow hemodynamics leading to thrombogenesis. 4D-flow MRI enables in-vivo hemodynamic quantification in the left atrium (LA) and LA appendage (LAA). To determine whether LA and LAA hemodynamic and volumetric parameters are associated with SBI. Prospective observational study. A single-site cohort of 125 Participants of the multiethnic study of atherosclerosis (MESA), mean age: 72.3 ± 7.2 years, 56 men. 1.5T. Cardiac MRI: Cine balanced steady state free precession (bSSFP) and 4D-flow sequences. Brain MRI: T1- and T2-weighted SE and FLAIR. Presence of SBI was determined from brain MRI by neuroradiologists according to routine diagnostic criteria in all participants without a history of stroke based on the MESA database. Minimum and maximum LA volumes and ejection fraction were calculated from bSSFP data. Blood stasis (% of voxels <10 cm/sec) and peak velocity (cm/sec) in the LA and LAA were assessed by a radiologist using an established 4D-flow workflow. Student's t test, Mann-Whitney U test, one-way ANOVA, chi-square test. Multivariable stepwise logistic regression with automatic forward and backward selection. Significance level P < 0.05. 26 (20.8%) had at least one SBI. After Bonferroni correction, participants with SBI were significantly older and had significantly lower peak velocities in the LAA. In multivariable analyses, age (per 10-years) (odds ratio (OR) = 1.99 (95% confidence interval (CI): 1.30-3.04)) and LAA peak velocity (per cm/sec) (OR = 0.87 (95% CI: 0.81-0.93)) were significantly associated with SBI. Older age and lower LAA peak velocity were associated with SBI in multivariable analyses whereas volumetric-based measures from cardiac MRI or cardiovascular risk factors were not. Cardiac 4D-flow MRI showed potential to serve as a novel imaging marker for SBI. 3 TECHNICAL EFFICACY: Stage 2.

Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan

BackgroundThe choroid plexus functions as the blood-cerebrospinal fluid (CSF) barrier, plays an important role in CSF production and circulation, and has gained increased attention in light of the recent elucidation of CSF circulation dysfunction in neurodegenerative conditions. However, methods for routinely quantifying choroid plexus volume are suboptimal and require technical improvements and validation. Here, we propose three deep learning models that can segment the choroid plexus from commonly-acquired anatomical MRI data and report performance metrics and changes across the adult lifespan.MethodsFully convolutional neural networks were trained from 3D T1-weighted, 3D T2-weighted, and 2D T2-weighted FLAIR MRI using gold-standard manual segmentations in control and neurodegenerative participants across the lifespan (n = 50; age = 21–85 years). Dice coefficients, 95% Hausdorff distances, and area-under-curve (AUCs) were calculated for each model and compared to segmentations from FreeSurfer using two-tailed Wilcoxon tests (significance criteria: p < 0.05 after false discovery rate multiple comparisons correction). Metrics were regressed against lateral ventricular volume using generalized linear models to assess model performance for varying levels of atrophy. Finally, models were applied to an expanded cohort of adult controls (n = 98; age = 21–89 years) to provide an exemplar of choroid plexus volumetry values across the lifespan.ResultsDeep learning results yielded Dice coefficient = 0.72, Hausdorff distance = 1.97 mm, AUC = 0.87 for T1-weighted MRI, Dice coefficient = 0.72, Hausdorff distance = 2.22 mm, AUC = 0.87 for T2-weighted MRI, and Dice coefficient = 0.74, Hausdorff distance = 1.69 mm, AUC = 0.87 for T2-weighted FLAIR MRI; values did not differ significantly between MRI sequences and were statistically improved compared to current commercially-available algorithms (p < 0.001). The intraclass coefficients were 0.95, 0.95, and 0.96 between T1-weighted and T2-weighted FLAIR, T1-weighted and T2-weighted, and T2-weighted and T2-weighted FLAIR models, respectively. Mean lateral ventricle choroid plexus volume across all participants was 3.20 ± 1.4 cm3; a significant, positive relationship (R2 = 0.54-0.60) was observed between participant age and choroid plexus volume for all MRI sequences (p < 0.001).ConclusionsFindings support comparable performance in choroid plexus delineation between standard, clinically available, non-contrasted anatomical MRI sequences. The software embedding the evaluated models is freely available online and should provide a useful tool for the growing number of studies that desire to quantitatively evaluate choroid plexus structure and function (https://github.com/hettk/chp_seg).

Quantification of T2-FLAIR Mismatch in Nonenhancing Diffuse Gliomas Using Digital Subtraction.

The T2-FLAIR mismatch sign on MR imaging is a highly specific imaging biomarker of isocitrate dehydrogenase (IDH)-mutant astrocytomas, which lack 1p/19q codeletion. However, most studies using the T2-FLAIR mismatch sign have used visual assessment. This study quantified the degree of T2-FLAIR mismatch using digital subtraction of fluid-nulled T2-weighted FLAIR images from non-fluid-nulled T2-weighted images in human nonenhancing diffuse gliomas and then used this information to assess improvements in diagnostic performance and investigate subregion characteristics within these lesions. Two cohorts of treatment-naïve, nonenhancing gliomas with known IDH and 1p/19q status were studied (n = 71 from The Cancer Imaging Archive (TCIA) and n = 34 in the institutional cohort). 3D volumes of interest corresponding to the tumor were segmented, and digital subtraction maps of T2-weighted MR imaging minus T2-weighted FLAIR MR imaging were used to partition each volume of interest into a T2-FLAIR mismatched subregion (T2-FLAIR mismatch, corresponding to voxels with positive values on the subtraction maps) and nonmismatched subregion (T2-FLAIR nonmismatch corresponding to voxels with negative values on the subtraction maps). Tumor subregion volumes, percentage of T2-FLAIR mismatch volume, and T2-FLAIR nonmismatch subregion thickness were calculated, and 2 radiologists assessed the T2-FLAIR mismatch sign with and without the aid of T2-FLAIR subtraction maps. Thresholds of ≥42% T2-FLAIR mismatch volume classified IDH-mutant astrocytoma with a specificity/sensitivity of 100%/19.6% (TCIA) and 100%/31.6% (institutional); ≥25% T2-FLAIR mismatch volume showed 92.0%/32.6% and 100%/63.2% specificity/sensitivity, and ≥15% T2-FLAIR mismatch volume showed 88.0%/39.1% and 93.3%/79.0% specificity/sensitivity. In IDH-mutant astrocytomas with ≥15% T2-FLAIR mismatch volume, T2-FLAIR nonmismatch subregion thickness was negatively correlated with the percentage T2-FLAIR mismatch volume (P < .0001) across both cohorts. The percentage T2-FLAIR mismatch volume was higher in grades 3-4 compared with grade 2 IDH-mutant astrocytomas (P < .05), and ≥15% T2-FLAIR mismatch volume IDH-mutant astrocytomas were significantly larger than <15% T2-FLAIR mismatch volume IDH-mutant astrocytoma (P < .05) across both cohorts. When evaluated by 2 radiologists, the additional use of T2-FLAIR subtraction maps did not show a significant difference in interreader agreement, sensitivity, or specificity compared with a separate evaluation of T2-FLAIR and T2-weighted MR imaging alone. T2-FLAIR digital subtraction maps may be a useful, automated tool to obtain objective segmentations of tumor subregions based on quantitative thresholds for classifying IDH-mutant astrocytomas using the percentage T2 FLAIR mismatch volume with 100% specificity and exploring T2-FLAIR mismatch/T2-FLAIR nonmismatch subregion characteristics. Conversely, the addition of T2-FLAIR subtraction maps did not enhance the sensitivity or specificity of the visual T2-FLAIR mismatch sign assessment by experienced radiologists.

Effect of Training Data Differences on Accuracy in MR Image Generation Using Pix2pix

Using a magnetic resonance (MR) image generation technique with deep learning, we elucidated whether changing the training data patterns affected image generation accuracy. The pix2pix training model generated T1-weighted images from T2-weighted images or FLAIR images. Head MR images obtained at our hospital were used in this study. We prepared 300 cases for each model and four training data patterns for each model (a: 150 cases for one MR system, b: 300 cases for one MR system, c: 150 cases and augmentation data for one MR system, and d: 300 cases for two MR systems). The extension data were images of 150 cases rotated in the XY plane. The similarity between the images generated by the training and evaluation data in each group was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). For both MR systems, the PSNR and SSIM were higher for training dataset b than training dataset a. The PSNR and SSIM were lower for training dataset d. MR image generation accuracy varied among training data patterns.

NIMG-50. TOTAL TUMOR CELLULARITY AND CELLULAR GROWTH RATES ESTIMATED USING A RADIO-PATHOMIC AI MODEL PREDICTS PROGNOSIS AND RESPONSE TO CYTOTOXIC THERAPY IN RECURRENT GBM

Abstract PURPOSE A radio-pathomic machine learning model trained with postmortem tissue samples has recently been developed to estimate tumor cellularity from standard anatomic MR images (Bobholz SA et al. AJNR 2022). The aim of this study was to use this externally developed, machine learning-based model to assess the impact of tumor cellularity and changes in cellularity on survival in recurrent glioblastoma (rGBM) treated with standard cytotoxic therapies. METHODS We retrospectively evaluated 22 cases of rGBM treated with chemotherapy. Radio-pathomic maps of predicted tumor cellularity were generated using the radio-pathomic model using pre- and post-contrast T1-weighted MR images, T2-weighted FLAIR images and apparent diffusion coefficient calculated from diffusion-weighted imaging. Total cellularity was estimated by integrating the cellularity measures within a contrast-enhancing tumor volume of interest. Cellularity growth rates (CGRs) were computed using three pre-treatment scans and available post-treatment scans. Tumor cellularity and CGRs were assessed in terms of their impact on overall survival (OS) using log-rank analysis. RESULTS Patients with baseline tumor cellularity &amp;lt; 8.89x108 cells and those with pre-treatment CGR &amp;lt; 7.55x106 cells/day showed significantly longer OS than those with higher cellularity or CGRs (P = 0.001 and 0.008, respectively). Patients with cellularity &amp;lt; 8.90x108 cells at the 1st follow-up (one month after treatment), those with cellularity at 2nd follow-up &amp;lt; 8.89x108 cells (two months after treatment), post-treatment CGR &amp;lt; 1.51x107 cells/day, and change in CGR after treatment &amp;lt; +5.91x106 cells/day showed significantly longer OS than those with higher values (P = 0.03, &amp;lt; 0.001, &amp;lt; 0.001, = 0.02, respectively). CONCLUSION These preliminary results suggest the potential value of estimating total cellularity within the enhancing tumor as a biomarker for prognosis prediction and cytotoxic therapy effect evaluation in rGBM. Additional analyses with more cases and treatments are warranted.

Increasing discrepancy of MR imaging and CSF study in patients with leptomeningeal seeding from lung adenocarcinoma after targeted therapy using a tyrosine kinase inhibitor.

To evaluate the correlation between contrast-enhanced (CE) MRI and cerebrospinal fluid (CSF) cytology for the evaluation of leptomeningeal metastasis (LM) on MRI after targeted therapy with tyrosine kinase inhibitors. We retrospectively reviewed the data of nonsmall cell lung cancer patients registered with NCT03257124 from May 2017 to December 2018, with progressive disease despite targeted therapy. Twenty-nine patients whose MRI scans exhibited LM at the time of registration were enrolled. During the targeted therapy with osimertinib, MRI scans, and subsequent CSF examinations were performed in every 2 months. In total, 113 MRI scans and CSF cytology data after treatment were collected. For each CE MRI scan, LM positivity was evaluated on 3D T1-weighted image (T1WI) and 2D FLAIR. The correlation between MRI and CSF cytology results and the diagnostic performance of MRI with CSF cytology as a reference standard were evaluated. After treatment, MRI revealed positivity for LM in 81 and negativity in 32. CSF results were positive in 69 examinations and negative in 44. The diagnostic accuracy of CE 3D T1WI and 2D FLAIR was 0.52 and 0.46, respectively. After targeted therapy, discrepancy in the CSF and MRI results tended to increase over time. The proportions of concordant MRI and CSF cytology results after targeted therapy were 66%, 58%, 62%, and 47% at the first, second, third, and fourth follow-up, respectively. The discrepancy of MRI in evaluation of LM and CSF cytology increases over time after targeted therapy with osimertinib. LM positivity on MRI could be a surrogate imaging marker in the pre- and immediate posttargeted-treatment with Osimertinib but not after sessions of osimertinib.

Parietal resting-state EEG alpha source connectivity is associated with subcortical white matter lesions in HIV-positive people

ObjectiveParietal resting-state electroencephalographic (rsEEG) alpha (8–10 Hz) source connectivity is abnormal in HIV-positive persons. Here we tested whether this abnormality may be associated with subcortical white matter vascular lesions in the cerebral hemispheres. MethodsClinical, rsEEG, and magnetic resonance imaging (MRI) datasets in 38 HIV-positive persons and clinical and rsEEG datasets in 13 healthy controls were analyzed. Radiologists visually evaluated the subcortical white matter hyperintensities from T2-weighted FLAIR MRIs (i.e., Fazekas scale). In parallel, neurophysiologists estimated the eLORETA rsEEG source lagged linear connectivity from parietal cortical regions of interest. ResultsCompared to the HIV participants with no/negligible subcortical white matter hyperintensities, the HIV participants with mild/moderate subcortical white matter hyperintensities showed lower parietal interhemispheric rsEEG alpha lagged linear connectivity. This effect was also observed in HIV-positive persons with unimpaired cognition. This rsEEG marker allowed good discrimination (area under the receiver operating characteristic curve > 0.80) between the HIV-positive individuals with different amounts of subcortical white matter hyperintensities. ConclusionsThe parietal rsEEG alpha source connectivity is associated with subcortical white matter vascular lesions in HIV-positive persons, even without neurocognitive disorders. SignificanceThose MRI-rsEEG markers may be used to screen HIV-positive persons at risk of neurocognitive disorders.

Progressive Changes in Cerebral Apparent Diffusion Values in Fabry Disease: A 5-Year Follow-up MRI Study.

White matter lesions are commonly found in patients with Fabry disease. Existing studies have shown elevated diffusivity in healthy-appearing brain regions that are commonly associated with white matter lesions, suggesting that DWI could help detect white matter lesions at an earlier stage This study explores whether diffusivity changes precede white matter lesion formation in a cohort of patients with Fabry disease undergoing yearly MR imaging examinations during a 5-year period. T1-weighted anatomic, FLAIR, and DWI scans of 48 patients with Fabry disease (23 women; median age, 44 years; range, 15-69 years) were retrospectively included. White matter lesions and tissue probability maps were segmented and, together with ADC maps, were transformed into standard space. ADC values were determined within lesions before and after detection on FLAIR images and compared with normal-appearing white matter ADC. By means of linear mixed-effects modeling, changes in ADC and ΔADC (relative to normal-appearing white matter) across time were investigated. ADC was significantly higher within white matter lesions compared with normal-appearing white matter (P < .01), even before detection on FLAIR images. ADC and ΔADC were significantly affected by sex, showing higher values in men (60.1 [95% CI, 23.8-96.3] ×10-6mm2/s and 35.1 [95% CI, 6.0-64.2] ×10-6mm2/s), respectively. ΔADC increased faster in men compared with women (0.99 [95% CI, 0.27-1.71] ×10-6mm2/s/month). ΔADC increased with time even when only considering data from before detection (0.57 [95% CI, 0.01-1.14] ×10-6mm2/s/month). Our results indicate that in Fabry disease, changes in diffusion precede the formation of white matter lesions and that microstructural changes progress faster in men compared with women. These findings suggest that DWI may be of predictive value for white matter lesion formation in Fabry disease.

A Fully Automatic Method to Segment Choroid Plexuses in Multiple Sclerosis Using Conventional MRI Sequences.

Choroid plexus (CP) volume has been recently proposed as a proxy for brain neuroinflammation in multiple sclerosis (MS). To develop and validate a fast automatic method to segment CP using routinely acquired brain T1-weighted and FLAIR MRI. Retrospective. Fifty-five MS patients (33 relapsing-remitting, 22 progressive; mean age = 46.8 ± 10.2 years; 31 women) and 60 healthy controls (HC; mean age = 36.1 ± 12.6 years, 33 women). 3D T2-weighted FLAIR and 3D T1-weighted gradient echo sequences at 3.0 T. Brain tissues were segmented on T1-weighted sequences and a Gaussian Mixture Model (GMM) was fitted to FLAIR image intensities obtained from the ventricle masks of the SIENAX. A second GMM was then applied on the thresholded and filtered ventricle mask. CP volumes were automatically determined and compared with those from manual segmentation by two raters (with 3 and 10 years' experience; reference standard). CP volumes from previously published automatic segmentation methods (freely available Freesurfer [FS] and FS-GMM) were also compared with reference standard. Expanded Disability Status Scale (EDSS) score was assessed within 3 days of MRI. Computational time was assessed for each automatic technique and manual segmentation. Comparisons of CP volumes with reference standard were evaluated with Bland Altman analysis. Dice similarity coefficients (DSC) were computed to assess automatic CP segmentations. Volume differences between MS and HC for each method were assessed with t-tests and correlations of CP volumes with EDSS were assessed with Pearson's correlation coefficients (R). A P value <0.05 was considered statistically significant. Compared to manual segmentation, the proposed method had the highest segmentation accuracy (mean DSC = 0.65 ± 0.06) compared to FS (mean DSC = 0.37 ± 0.08) and FS-GMM (0.58 ± 0.06). The percentage CP volume differences relative to manual segmentation were -0.1% ± 0.23, 4.6% ± 2.5, and -0.48% ± 2 for the proposed method, FS, and FS-GMM, respectively. The Pearson's correlations between automatically obtained CP volumes and the manually obtained volumes were 0.70, 0.54, and 0.56 for the proposed method, FS, and FS-GMM, respectively. A significant correlation between CP volume and EDSS was found for the proposed automatic pipeline (R = 0.2), for FS-GMM (R = 0.3) and for manual segmentation (R = 0.4). Computational time for the proposed method (32 ± 2 minutes) was similar to the manual segmentation (20 ± 5 minutes) but <25% of the FS (120 ± 15 minutes) and FS-GMM (125 ± 15 minutes) methods. This study developed an accurate and easily implementable method for automatic CP segmentation in MS using T1-weighted and FLAIR MRI. 1 TECHNICAL EFFICACY: Stage 4.

The Relationship of MRI-Derived Alzheimer's and Cerebrovascular-Related Signatures With Level of and Change in Health and Financial Literacy

The cortical thickness "signature" of Alzheimer's disease (AD-CT) and white matter hyperintensity (WMH) burden have each been associated with cognitive aging and incident AD and related dementias. Less is known about how these structural neuroimaging markers associate with other critical behaviors. We investigated associations of AD-CT and WMH volumes with a composite index of health and financial literacy given that the ability to access, understand, and utilize health and financial information significantly influences older adults' health outcomes. Participants were 303 adults without dementia (age∼80 years; 74% women) from the Rush Memory and Aging Project. Baseline 3T MRI T1-weighted structural and T2-weighted FLAIR data were used to assess AD-CT and WMH volumes, respectively. Literacy was measured using questions designed to assess comprehension of health and financial information and concepts, yielding a total literacy score. Multivariable linear mixed effects regression models determined the relationship of each neuroimaging marker, first separately and then combined, with the level of and change in literacy. Reduced AD-CT and higher WMH at baseline were each associated with lower levels of literacy; only AD-CT was associated with the rate of decline in literacy over time. The association of AD-CT with change in literacy persisted when both neuroimaging markers were included in the same model. The cortical thickness signature of AD predicts changes in health and financial literacy in nondemented older adults suggesting that the multidimensional construct of health and financial literacy relies on specific brain networks implicated in AD.

AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis

BackgroundContrast-enhancing (CE) lesions are an important finding on brain magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) but can be missed easily. Automated solutions for reliable CE lesion detection are emerging; however, independent validation of artificial intelligence (AI) tools in the clinical routine is still rare.MethodsA three-dimensional convolutional neural network for CE lesion segmentation was trained externally on 1488 datasets of 934 MS patients from 81 scanners using concatenated information from FLAIR and T1-weighted post-contrast imaging. This externally trained model was tested on an independent dataset comprising 504 T1-weighted post-contrast and FLAIR image datasets of MS patients from clinical routine. Two neuroradiologists (R1, R2) labeled CE lesions for gold standard definition in the clinical test dataset. The algorithmic output was evaluated on both patient- and lesion-level.ResultsOn a patient-level, recall, specificity, precision, and accuracy of the AI tool to predict patients with CE lesions were 0.75, 0.99, 0.91, and 0.96. The agreement between the AI tool and both readers was within the range of inter-rater agreement (Cohen’s kappa; AI vs. R1: 0.69; AI vs. R2: 0.76; R1 vs. R2: 0.76). On a lesion-level, false negative lesions were predominately found in infratentorial location, significantly smaller, and at lower contrast than true positive lesions (p < 0.05).ConclusionsAI-based identification of CE lesions on brain MRI is feasible, approaching human reader performance in independent clinical data and might be of help as a second reader in the neuroradiological assessment of active inflammation in MS patients.Critical relevance statementAl-based detection of contrast-enhancing multiple sclerosis lesions approaches human reader performance, but careful visual inspection is still needed, especially for infratentorial, small and low-contrast lesions.Graphical

Prevalence of White Matter Hyperintensities in Young Adults With and Without Mild Traumatic Brain Injury (S28.004)

<h3>Objective:</h3> To determine the prevalence and severity of white matter T2 hyperintensities (WMHs) in young healthy adults and those with recent mild traumatic brain injury (mTBI). <h3>Background:</h3> WMHs on T2-weighted (T2W) MRI are common. Normative data on their prevalence in young healthy adults and in the setting of mTBI is not well characterized, and dependent upon classification system, MRI field strength, scan resolution and imaging parameters. <h3>Design/Methods:</h3> High-resolution 3-Tesla 3D T2W FLAIR and SWI images were retrospectively reviewed from 602 mTBI and control patients who participated in a larger multicenter study of advanced neuroimaging biomarkers. All scans were independently reviewed by 2 board-certified blinded neuroradiologists. All patients with WMHs were identified; patients with excessive WMHs were also identified if ≥5 objective, punctate, white matter foci were present or lesions were &gt;3 mm or atypical in location. <h3>Results:</h3> 413 patients with documented mTBI and 189 controls were analyzed. 32% (134/413) of the mTBI patients and 40% (76/189) of the control patients demonstrated at least 1 WMH on high-resolution T2W-FLAIR imaging. Of these patients, 34% (45/134) of the mTBI group and 34% (26/76) of the control group demonstrated ≥5 lesions, one &gt;3 mm lesion, or lesions in atypical locations. There was no significant difference in number of patients with at least 1 WMH (p=0.064) or excessive WMHs (p=0.313) between mTBI and control groups. <h3>Conclusions:</h3> This represents the largest study cohort of mTBI patients and healthy controls assessing the prevalence of WMHs on high-resolution 3-Telsa 3D T2W-FLAIR. Prevalence of WMHs did not differ between groups and were observed at higher rates than reported in the current literature. These results reflect an evidence-based need to reassess our understanding and clinical interpretation of WMHs in normal healthy adults and patients with mTBI. <b>Disclosure:</b> Dr. Shetty has received personal compensation in the range of $0-$499 for serving on a Scientific Advisory or Data Safety Monitoring board for Biohaven Pharmaeucticals. Dr. Shetty has received personal compensation in the range of $500-$4,999 for serving as an Expert Witness for mTBI, Inc. The institution of Dr. Shetty has received research support from Marker AG. The institution of Dr. Shetty has received research support from Teva Pharmaceuticals Industry. The institution of Dr. Shetty has received research support from GE-NFL. Mr. Westafer has nothing to disclose. Joseph T Nguyen has received personal compensation in the range of $0-$499 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for The American Journal of Sports Medicine. Joseph T Nguyen has received personal compensation in the range of $0-$499 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for The Women’s Journal of Sports Medicine. Miss Coffey has nothing to disclose.

Automatic brain extraction and brain tissues segmentation on multi-contrast animal MRI

For many neuroscience applications, brain extraction in MRI images is the first pre-processing step of a quantification pipeline. Once the brain is extracted, further post-processing calculations become faster, more specific and easier to implement and interpret. It is the case, for example, of functional MRI brain studies, or relaxation time mappings and brain tissues classifications to characterise brain pathologies. Existing brain extraction tools are mostly adapted to work on the human anatomy, this gives poor results when applied to animal brain images. We have developed an atlas-based Veterinary Images Brain Extraction (VIBE) algorithm that encompasses a pre-processing step to adapt the atlas to the patient’s image, and a subsequent registration step. We show that the brain extraction is achieved with excellent results in terms of Dice and Jaccard metrics. The algorithm is automatic, with no need to adapt the parameters in a broad range of situations: we successfully tested multiple MRI contrasts (T1-weighted, T2-weighted, T2-weighted FLAIR), all the acquisition planes (sagittal, dorsal, transverse), different animal species (dogs and cats) and canine cranial conformations (brachycephalic, mesocephalic, dolichocephalic). VIBE can be successfully extended to other animal species, provided that an atlas for that specific species exists. We show also how brain extraction, as a preliminary step, can help to segment brain tissues with a K-Means clustering algorithm.

Posterior reversible encephalopathy syndrome: Co-relation between MR perfusion and the clinico-pathological spectrum

Objectives: The study was aimed to assess the epidemiological profile and patient characteristics, various morphological patterns of posterior reversible encephalopathy syndrome (PRES) on MRI and various pathophysiological mechanisms on MR perfusion. Methods: It was a prospective and observational study done over a total of 40 patients. Standard sequences included T1 and T2-weighted sequences, FLAIR, DWI, SWI and MR perfusion. Results: Females were affected predominantly with F:M ratio of 12:1. The commonest age group affected was between 20-40 years. The most common symptom was headache. The commonest etiology being pregnancy induced(37.5%).Hypertension was reported in 70%,out of which 37.5% included pregnancy induced hypertension. The commonest region involved was occipital lobe seen in 85% of patients. Atypical PRES was seen in 42.5% of patients in the form of involvement of atypical locations; basal ganglia 10%, thalami 2.5% and cerebellum 2.5%; diffusion restriction 12.5% and hemorrhage 22.5%, out of which 10% had subarachnoid hemorrhage,10% has intraparenchymal hematoma and 2.5% had minute focal hemorrhage. rCBV and rCBF was decreased in 82.5% of patients and unchanged in 12.5% of the total number of patients supporting the hypoperfusion theory. Conclusion: PRES predominantly involves female, hypertension is seen in most of the patients. The commonest lobes involved are the occipital and parietal lobes; however the incidence of atypical PRES is also seen in a significant number of patients. The basic pathophysiological mechanism is hypoperfusion.