FLAIR Images Research Articles

MRI-Based Prediction of Clinical Improvement after Ventricular Shunt Placement for Normal Pressure Hydrocephalus: Development and Evaluation of an Integrated Multisequence Machine Learning Algorithm.

Symptoms of normal pressure hydrocephalus (NPH) are sometimes refractory to shunt placement, with limited ability to predict improvement for individual patients. We evaluated an MRI-based artificial intelligence method to predict postshunt NPH symptom improvement. Patients with NPH who underwent MRI before shunt placement at a single center (2014-2021) were identified. Twelve-month postshunt improvement in mRS, incontinence, gait, and cognition were retrospectively abstracted from clinical documentation. 3D deep residual neural networks were built on skull-stripped T2-weighted and FLAIR images. Predictions based on both sequences were fused by additional network layers. Patients from 2014-2019 were used for parameter optimization, while those from 2020-2021 were used for testing. Models were validated on an external validation data set from a second institution (n = 33). Of 249 patients, n = 201 and n = 185 were included in the T2-based and FLAIR-based models according to imaging availability. The combination of T2-weighted and FLAIR sequences offered the best performance in mRS and gait improvement predictions relative to models trained on imaging acquired by using only 1 sequence, with area under the receiver operating characteristic (AUROC) values of 0.7395 [0.5765-0.9024] for mRS and 0.8816 [0.8030-0.9602] for gait. For urinary incontinence and cognition, combined model performances on predicting outcomes were similar to FLAIR-only performance, with AUROC values of 0.7874 [0.6845-0.8903] and 0.7230 [0.5600-0.8859]. Application of a combined algorithm by using both T2-weighted and FLAIR sequences offered the best image-based prediction of postshunt symptom improvement, particularly for gait and overall function in terms of mRS.

Assessing the efficacy of 3D Dual-CycleGAN model for multi-contrast MRI synthesis

BackgroundThis research presents a novel methodology for synthesizing 3D multi-contrast MRI images utilizing the 3D Dual-CycleGAN architecture. The performance of the model is evaluated on different MRI sequences, including T1-weighted (T1W), T1-weighted contrast-enhanced (T1c), T2-weighted (T2W), and FLAIR sequences.ResultsOur approach demonstrates proficient learning capabilities in transforming T1W images into target modalities. The proposed framework encompasses a combination of different loss functions including voxel-wise, gradient difference, perceptual, and structural similarity losses. These loss components, along with adversarial and dual cycle-consistency losses, contribute significantly to realistic and accurate syntheses. Evaluation metrics including MAE, PMAE, RMSE, PCC, PSNR, and SSIM are employed to assess the fidelity of synthesized images compared to their ground truth counterparts. Empirical results indicate the effectiveness of the 3D Dual-CycleGAN model in generating T1c images from T1W inputs with minimal average discrepancies (MAE of 2.8 ± 2.61) and strong similarity (SSIM of 0.82 ± 0.28). Furthermore, the synthesis of T2W and FLAIR images yields promising outcomes, demonstrating acceptable average discrepancies (MAE of 3.87 ± 3.32 for T2W and 3.82 ± 3.32 for FLAIR) and reasonable similarities (SSIM of 0.82 ± 0.28 for T2W and 0.80 ± 0.29 for FLAIR) relative to the original images.ConclusionsThese findings underscore the efficacy of the 3D Dual-CycleGAN model in generating high-fidelity images, with significant implications for diverse applications in the field of medical imaging.

Meningeal contrast enhancement in multiple sclerosis: Assessment of field strength, acquisition delay, and clinical relevance.

Leptomeningeal enhancement (LME) on post-contrast FLAIR is described as a potential biomarker of meningeal inflammation in multiple sclerosis (MS). Here we report an assessment of the impact of MRI field strength and acquisition timing on meningeal contrast enhancement (MCE). This was a cross-sectional, observational study of 95 participants with MS and 17 healthy controls (HC) subjects. Each participant underwent an MRI of the brain on both a 7 Tesla (7T) and 3 Tesla (3T) MRI scanner. 7T protocols included a FLAIR image before, soon after (Gd+ Early 7T FLAIR), and 23 minutes after gadolinium (Gd+ Delayed 7T FLAIR). 3T protocol included FLAIR before and 21 minutes after gadolinium (Gd+ Delayed 3T FLAIR). LME was seen in 23.3% of participants with MS on Gd+ Delayed 3T FLAIR, 47.4% on Gd+ Early 7T FLAIR (p = 0.002) and 57.9% on Gd+ Delayed 7T FLAIR (p < 0.001 and p = 0.008, respectively). The count and volume of LME, leptomeningeal and paravascular enhancement (LMPE), and paravascular and dural enhancement (PDE) were all highest for Gd+ Delayed 7T FLAIR and lowest for Gd+ Delayed 3T FLAIR. Non-significant trends were seen for higher proportion, counts, and volumes for LME and PDE in MS compared to HCs. The rate of LMPE was different between MS and HCs on Gd+ Delayed 7T FLAIR (98.9% vs 82.4%, p = 0.003). MS participants with LME on Gd+ Delayed 7T FLAIR were older (47.6 (10.6) years) than those without (42.0 (9.7), p = 0.008). 7T MRI and a delay after contrast injection increased sensitivity for all forms of MCE. However, the lack of difference between groups for LME and its association with age calls into question its relevance as a biomarker of meningeal inflammation in MS.

AI-based model for automatic identification of multiple sclerosis based on enhanced sea-horse optimizer and MRI scans

This study aims to develop an AI-enhanced methodology for the expedited and accurate diagnosis of Multiple Sclerosis (MS), a chronic disease affecting the central nervous system leading to progressive impairment. Traditional diagnostic methods are slow and require substantial expertise, underscoring the need for innovative solutions. Our approach involves two phases: initially, extracting features from brain MRI images using first-order histograms, the gray level co-occurrence matrix, and local binary patterns. A unique feature selection technique combining the Sine Cosine Algorithm with the Sea-horse Optimizer is then employed to identify the most significant features. Utilizing the eHealth lab dataset, which includes images from 38 MS patients (mean age 34.1 ± 10.5 years; 17 males, 21 females) and matched healthy controls, our model achieved a remarkable 97.97% detection accuracy using the k-nearest neighbors classifier. Further validation on a larger dataset containing 262 MS cases (199 females, 63 males; mean age 31.26 ± 10.34 years) and 163 healthy individuals (109 females, 54 males; mean age 32.35 ± 10.30 years) demonstrated a 92.94% accuracy for FLAIR images and 91.25% for T2-weighted images with the Random Forest classifier, outperforming existing MS detection methods. These results highlight the potential of the proposed technique as a clinical decision-making tool for the early identification and management of MS.

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.

Long-term clinical observation of patients with heterozygous KIF1A variants.

KIF1A-related disorders (KRDs) encompass recessive and dominant variants with wide clinical variability. Recent genetic investigations have expanded the clinical phenotypes of heterozygous KIF1A variants. However, there have been a few long-term observational studies of patients with heterozygous KIF1A variants. A retrospective chart review of consecutive patients diagnosed with spastic paraplegia at Miyagi Children's Hospital from 2016 to 2020 identified six patients with heterozygous KIF1A variants. To understand the long-term changes in clinical symptoms, we examined these patients in terms of their characteristics, clinical symptoms, results of electrophysiological and neuroimaging studies, and genetic testing. The median follow-up period was 30 years (4-44 years). This long-term observational study showed that early developmental delay and equinus gait, or unsteady gait, are the first signs of disease onset, appearing with the commencement of independent walking. In addition, later age-related progression was observed in spastic paraplegia, and the appearance of axonal neuropathy and reduced visual acuity were characteristic features of the late disease phenotype. Brain imaging showed age-related progression of cerebellar atrophy and the appearance of hyperintensity of optic radiation on T2WI and FLAIR imaging. Long-term follow-up revealed a pattern of steady progression and a variety of clinical symptoms, including spastic paraplegia, peripheral neuropathy, reduced visual acuity, and some degree of cerebellar ataxia. Clinical variability between patients was observed to some extent, and therefore, further studies are required to determine the phenotype-genotype correlation.

Improving the Generalizability of Deep Learning for T2-Lesion Segmentation of Gliomas in the Post-Treatment Setting.

Although fully automated volumetric approaches for monitoring brain tumor response have many advantages, most available deep learning models are optimized for highly curated, multi-contrast MRI from newly diagnosed gliomas, which are not representative of post-treatment cases in the clinic. Improving segmentation for treated patients is critical to accurately tracking changes in response to therapy. We investigated mixing data from newly diagnosed (n = 208) and treated (n = 221) gliomas in training, applying transfer learning (TL) from pre- to post-treatment imaging domains, and incorporating spatial regularization for T2-lesion segmentation using only T2 FLAIR images as input to improve generalization post-treatment. These approaches were evaluated on 24 patients suspected of progression who had received prior treatment. Including 26% of treated patients in training improved performance by 13.9%, and including more treated and untreated patients resulted in minimal changes. Fine-tuning with treated glioma improved sensitivity compared to data mixing by 2.5% (p < 0.05), and spatial regularization further improved performance when used with TL by 95th HD, Dice, and sensitivity (6.8%, 0.8%, 2.2%; p < 0.05). While training with ≥60 treated patients yielded the majority of performance gain, TL and spatial regularization further improved T2-lesion segmentation to treated gliomas using a single MR contrast and minimal processing, demonstrating clinical utility in response assessment.

Quantifying chronic lesion expansion in multiple sclerosis: Exploring imaging markers for longitudinal assessment

ObjectivesGradual expansion of multiple sclerosis lesions over time is known to have a significant impact on disease progression. However, accurately quantifying the volume changes in chronic lesions presents challenges due to their slow rate of progression and the need for longitudinal segmentation. Our study addresses this by estimating the expansion of chronic lesions using data collected over a 1–2 year period and exploring imaging markers that do not require longitudinal lesion segmentation. MethodsPre- and post-gadolinium 3D-T1, 3D FLAIR and diffusion tensor images were acquired from 42 patients with MS. Lesion expansion, stratified by the severity of tissue damage as measured by mean diffusivity change, was analysed between baseline and 48 months (Progressive Volume/Severity Index, PVSI). Central brain atrophy (CBA) and the degree of tissue loss inside chronic lesions (measured by the change of T1 intensity and mean diffusivity (MD)) were used as surrogate markers. ResultsCBA measured after 2 years of follow-up estimated lesion expansion at 4 years with a high degree of accuracy (r = 0.82, p < 0.001, ROC area under the curve 0.92, sensitivity of 94 %, specificity of 85 %). Increased MD within chronic lesions measured over 2 years was strongly associated with future expansion (r = 0.77, p < 0.001, ROC area under the curve 0.87, sensitivity of 81 % and specificity of 81 %). In contrast, change in lesion T1 hypointensity poorly explained future PVSI (best sensitivity and specificity 60 % and 59 % respectively). InterpretationCBA and, to a lesser extent, the change in MD within chronic MS lesions, measured over a period of 2 years, can provide a reliable and sensitive estimate of the extent and severity of chronic lesion expansion.

Deep learning for discrimination of active and inactive lesions in multiple sclerosis using non-contrast FLAIR MRI: A multicenter study

BackgroundWithin the domain of multiple sclerosis (MS), the precise discrimination between active and inactive lesions bears immense significance. Active lesions are enhanced on T1-weighted MRI images after administration of gadolinium-based contrast agents, which brings about associated complexities. This study investigates the potential of deep learning to differentiate between active and inactive lesions in MS using non-contrast FLAIR-type MRI data, presenting a non-invasive alternative to conventional gadolinium-based MRI methods. MethodsThe dataset encompasses 9097 lesion images collected from 130 MS patients across four distinct imaging centers, with post-contrast T1-weighted images as the benchmark reference. We initially identified and labeled the lesions and carefully selected corresponding regions of interest (ROIs). These ROIs were employed as inputs for a convolutional neural network (CNN) to predict lesion status. Also, transfer learning was utilized, incorporating 12 pre-trained CNN models. Subsequently, an ensemble technique was applied to 3 of best models, followed by a systematic comparison of the results. ResultsThrough a 5-fold cross-validation, our custom designed network exhibited an average accuracy of 85 %, a sensitivity of 95 %, a specificity of 75 %, and an AUC value of 0.90. Among the pre-trained models, ResNet50 emerged as the most effective, achieving a specificity of 58 %, an accuracy of 75 %, a sensitivity of 91 %, and an AUC value of 0.81. Our comprehensive evaluations encompassed the receiver operating characteristic curve, precision-recall curve, and confusion matrix analyses. ConclusionThe findings underscore the efficacy of the proposed CNN, trained on FLAIR MRI data ROIs, in accurately discerning active and inactive lesions without reliance on contrast agents. Our multicenter study of 130 patients with diverse imaging devices outperforms the other single-center studies, achieving superior sensitivity and specificity. Unlike studies using multiple modalities, our exclusive use of FLAIR images streamlines the process, and our streamlined approach, excluding conventional pre-processing, demonstrates efficiency. The external validation conducted on diverse datasets, coupled with the analysis of dilated masks, underscores the adaptability and efficacy of our custom CNN model in discerning between active and inactive lesions.

CT Perfusion Derived rCBV < 42% Lesion Volume Is Independently Associated with Followup FLAIR Infarct Volume in Anterior Circulation Large Vessel Occlusion.

Pretreatment CT Perfusion (CTP) parameter rCBV < 42% lesion volume has recently been shown to predict 90-day mRS. In this study, we aim to assess the relationship between rCBV < 42% and a radiographic follow-up infarct volume delineated on FLAIR images. In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 9 January 2017 and 10 January 2023. Follow-up FLAIR imaging was used to determine the final infarct volume. Student t, Mann-Whitney-U, and Chi-Square tests were used to assess differences. Spearman's rank correlation and linear regression analysis were used to assess associations between rCBV < 42% and follow-up infarct volume on FLAIR. In total, 158 patients (median age: 68 years, 52.5% female) met our inclusion criteria. rCBV < 42% (ρ = 0.56, p < 0.001) significantly correlated with follow-up-FLAIR infarct volume. On multivariable linear regression analysis, rCBV < 42% lesion volume (beta = 0.60, p < 0.001), ASPECTS (beta = -0.214, p < 0.01), mTICI (beta = -0.277, p < 0.001), and diabetes (beta = 0.16, p < 0.05) were independently associated with follow-up infarct volume. The rCBV < 42% lesion volume is independently associated with FLAIR follow-up infarct volume.

234 Predictive Radiomics in Glioblastoma: A Machine Learning Approach for Personalized Survival Outcomes and a Novel Web Application

INTRODUCTION: Diverse characteristics of glioblastomas (GBMs) complicate survival predictions, requiring personalized approaches. METHODS: From The Cancer Imaging Archive (TCIA) database, 611 patients from the UPENN-GBM collection and 500 patients from the UCSF-PDGM collections were included in this study. UPENN-GBM collection was used as the training set and for 5-fold internal cross-validation, whereas the UCSF-PDGM collection was used for external validation. Survival at 3, 6, 12, and 24 months were the outcomes of interest. Necrotic tumor core, enhancing tumor, and whole tumor segmentation masks for T1 with contrast, T1, T2, and FLAIR images were obtained automatically with a pre-trained Swin UNETR model. From these regions of interest, radiomic features were extracted using the PyRadiomicsLibrary. The LASSO (least absolute shrinkage and selection operator) method was employed for radiomic feature selection. The ML algorithms TabPFN, XGBoost, LightGBM, Support Vector Machine (SVM), and Random Forest (RF) were utilized to predict the survival outcomes of interest. RESULTS: The highlight of our study, our web application is available for use via the following link: https://huggingface.co/spaces/MSHSNeurosurgeryResearch/Radiomics_Based_Glioblastoma_Survival_Prediction_Tool. CONCLUSIONS: This study not only provides a radiomics pipeline for predicting the GBM survival outcomes, but it also contributes to the clinical adoption of these models by developing a fully automatic and user-friendly web application which make them available to clinicians worldwide.

Differential associations between abnormal cardiac left ventricular geometry types and cerebral white matter disease

ObjectivesReduced cardiac outflow due to left ventricular hypertrophy has been suggested as a potential risk factor for development of cerebral white matter disease. Our study aimed to examine the correlation between left ventricular geometry and white matter disease volume to establish a clearer understanding of their relationship, as it is currently not well-established. MethodsConsecutive patients from 2016 to 2021 who were ≥18 years and underwent echocardiography, cardiac MRI, and brain MRI within one year were included. Four categories of left ventricular geometry were defined based on left ventricular mass index and relative wall thickness on echocardiography. White matter disease volume was quantified using an automated algorithm applied to axial T2 FLAIR images and compared across left ventricular geometry categories. ResultsWe identified 112 patients of which 34.8 % had normal left ventricular geometry, 20.5 % had eccentric hypertrophy, 21.4 % had concentric remodeling, and 23.2 % had concentric hypertrophy. White matter disease volume was highest in patients with concentric hypertrophy and concentric remodeling, compared to eccentric hypertrophy and normal morphology with a trend-P value of 0.028. Patients with higher relative wall thickness had higher white matter disease volume (10.73 ± 10.29 cc vs 5.89 ± 6.46 cc, P = 0.003), compared to those with normal relative wall thickness. ConclusionOur results showed that abnormal left ventricular geometry is associated with higher white matter disease burden, particularly among those with abnormal relative wall thickness. Future studies are needed to explore causative relationships and potential therapeutic options that may mediate the adverse left ventricular remodeling and its effect in slowing white matter disease progression.

Cerebral arterial stiffness is linked to white matter hyperintensities and perivascular spaces in older adults – A 4D flow MRI study

White matter hyperintensities (WMH), perivascular spaces (PVS) and lacunes are common MRI features of small vessel disease (SVD). However, no shared underlying pathological mechanism has been identified. We investigated whether SVD burden, in terms of WMH, PVS and lacune status, was related to changes in the cerebral arterial wall by applying global cerebral pulse wave velocity (gcPWV) measurements, a newly described marker of cerebral vascular stiffness. In a population-based cohort of 190 individuals, 66–85 years old, SVD features were estimated from T1-weighted and FLAIR images while gcPWV was estimated from 4D flow MRI data. Additionally, the gcPWV’s stability to variations in field-of-view was analyzed. The gcPWV was 10.82 (3.94) m/s and displayed a significant correlation to WMH and white matter PVS volume (r = 0.29, p < 0.001; r = 0.21, p = 0.004 respectively from nonparametric tests) that persisted after adjusting for age, blood pressure variables, body mass index, ApoB/A1 ratio, smoking as well as cerebral pulsatility index, a previously suggested early marker of SVD. The gcPWV displayed satisfactory stability to field-of-view variations. Our results suggest that SVD is accompanied by changes in the cerebral arterial wall that can be captured by considering the velocity of the pulse wave transmission through the cerebral arterial network.

Evaluating contouring accuracy and dosimetry impact of current MRI-guided adaptive radiation therapy for brain metastases: a retrospective study

BackgroundMagnetic resonance imaging (MRI) guided adaptive radiotherapy (MRgART) has gained increasing attention, showing clinical advantages over conventional radiotherapy. However, there are concerns regarding online target delineation and modification accuracy. In our study, we aimed to investigate the accuracy of brain metastases (BMs) contouring and its impact on dosimetry in 1.5 T MRI-guided online adaptive fractionated stereotactic radiotherapy (FSRT).MethodsEighteen patients with 64 BMs were retrospectively evaluated. Pre-treatment 3.0 T MRI scans (gadolinium contrast-enhanced T1w, T1c) and initial 1.5 T MR-Linac scans (non-enhanced online-T1, T2, and FLAIR) were used for gross target volume (GTV) contouring. Five radiation oncologists independently contoured GTVs on pre-treatment T1c and initial online-T1, T2, and FLAIR images. We assessed intra-observer and inter-observer variations and analysed the dosimetry impact through treatment planning based on GTVs generated by online MRI, simulating the current online adaptive radiotherapy practice.ResultsThe average Dice Similarity Coefficient (DSC) for inter-observer comparison were 0.79, 0.54, 0.59, and 0.64 for pre-treatment T1c, online-T1, T2, and FLAIR, respectively. Inter-observer variations were significantly smaller for the 3.0 T pre-treatment T1c than for the contrast-free online 1.5 T MR scans (P < 0.001). Compared to the T1c contours, the average DSC index of intra-observer contouring was 0.52‒0.55 for online MRIs. For BMs larger than 3 cm3, visible on all image sets, the average DSC indices were 0.69, 0.71 and 0.64 for online-T1, T2, and FLAIR, respectively, compared to the pre-treatment T1c contour. For BMs < 3 cm3, the average visibility rates were 22.3%, 41.3%, and 51.8% for online-T1, T2, and FLAIR, respectively. Simulated adaptive planning showed an average prescription dose coverage of 63.4‒66.9% when evaluated by ground truth planning target volumes (PTVs) generated on pre-treatment T1c, reducing it from over 99% coverage by PTVs generated on online MRIs.ConclusionsThe accuracy of online target contouring was unsatisfactory for the current MRI-guided online adaptive FSRT. Small lesions had poor visibility on 1.5 T non-contrast-enhanced MR-Linac images. Contour inaccuracies caused a one-third drop in prescription dose coverage for the target volume. Future studies should explore the feasibility of contrast agent administration during daily treatment in MRI-guided online adaptive FSRT procedures.

Abstract TMP79: Magnetic Resonance Imaging Elements Predict Incident Intracerebral Hemorrhage

Introduction: Incident intracerebral hemorrhage (ICH) is an uncommon medical event that can lead to devastating outcomes, including death. Small vessel disease (SVD), as measured by cerebral microbleeds (CMBs) and white matter hyperintensities (WMH), has been associated with risk factors and might be a predictor of incident ICH. CMBs result from irregularities in brain vessel structure due to chronic hypertension and cerebral amyloid angiopathy. These microhemorrhages are a possible risk factor for ICH. Methods: A cohort of ARIC (Atherosclerosis Risk in Communities) participants underwent 3T MRI at visit 5 (2011-2013). CMBs were assessed using a T2* gradient echo. CMBs are homogenous hypointense lesions ≤10 mm in diameter. WMH volumes were derived from FLAIR images using an automated program to measure the volumetric burden of leukoaraiosis. During 10 years of follow-up, incident ICH was noted in 7 (0.4%) out of 1,656 participants were dichotomized based on the presence of CMB and the median of WMH volume. Results: ARIC subjects (n=1656, mean age=76±5 years; 40% men; 26% African American race) underwent MRI. CMBs were present in 385 (23.25%) patients out of 1656. ICH occurred in 0.16% and 1.30% of patients without CMBs and with CMBs, respectively. Kaplan Meier (KM) survival analysis between CMBs and ICH was significant (logrank p=0.002). Cox Regression yielded a significant crude hazards ratio (HR 8.7, 95% CI 1.7-44.7, p=0.010). Adjustment for age, gender, race, and hypertension was also significant (HR 9.9, 95% CI 1.9-52.0, p=0.07). KM survival analysis between WMH median (11.68 cm 3 ) and ICH was significant (logrank p=0.006). Figure 1 displays the KM curves of those with and without CMBs and the incidence of ICH as well as those below and above WMH median and incidence of ICH. Conclusion: CMBs and WMH detected by MRI appear to be an important predictor for the occurrence of incident ICH. Further research is needed to observe if MRI could be used to screen subjects at high-risk for elevated risk of incident ICH.

Abstract TMP70: Association of Distal Hyperintense Vessel Sign and Outcomes in Patients With Symptomatic Posterior Circulation Intracranial Atherosclerotic Disease

Objectives: The Distal Hyperintense Vessel (DHV) sign on FLAIR imaging is a radiographic marker of inadequate blood flow due to poor collateral flow distal to the stenotic artery. Previous studies suggest that the DHV sign was associated with early recurrent ischemic stroke in the anterior circulation secondary to intracranial atherosclerotic disease (ICAD). However, its significance in ischemic stroke in the posterior circulation is unknown. Here, we investigate the association of DHV sign in the basilar artery and outcomes at discharge in patients with posterior circulation stroke or TIA secondary to ICAD. Methods: We retrospectively reviewed patients with ischemic strokes or TIA attributed to ICAD of the basilar or vertebral arteries admitted to two comprehensive stroke centers affiliated with UTSW Medical Center from 2010 to 2022. Patients were included if they met VERiTAS criteria (≥50% vertebrobasilar stenosis). The DHV sign was defined as positive when the increased intensity in the basilar artery distal to stenosis was higher than the surrounding CSF signal. The DHV sign was evaluated by blinded vascular neurologists and neuro-radiologists. The primary outcome was mRS at discharge. The secondary outcome was the incidence of clinical deterioration during admission, defined as any worsened neurological exam with associated new infarct or infarct expansion in the posterior circulation. Results: A total of 135 patients were included in the study. A total of 33 (24%) patients had the presence of the DHV sign on admission MRI. Compared to patients without DHV sign, patients with DHV sign had higher NIHSS scores on admission (median 7 vs 2, p&lt;0.001), higher risk of clinical deterioration during hospitalization (27% vs 2%, p&lt;0.001), higher mRS scores at discharge (median 3.45 vs 1.8, p&lt;0.001) and higher mortality rate (24% vs 0%, p&lt;0.001). In multivariate analysis, the DHV sign was an independent predictor of higher mRS scores at discharge. Conclusions: In patients with ischemic stroke or TIA secondary to basilar or vertebral artery atherosclerotic stenosis, the presence of the DHV sign in the basilar artery is associated with more severe disease and poorer outcomes at discharge compared to patients without DHV sign.

Abstract WP127: Diagnostic Value of the Ivy Sign Compared to MR With Cerebrovascular Reactivity in Decompensated Moyamoya Arteriopathy

Introduction: MR cerebrovascular reactivity (CVR) mapping during rapid manipulation of end-tidal pCO2 is used to assess hemodynamic compromise in patients with Moyamoya arteriopathy (MMA). Leptomeningeal ivy sign on MRI FLAIR sequence may indicate regions of decreased perfusion, which would be an inexpensive and easy way to screen for hemodynamic compromise. The purpose of this study was to examine the diagnostic value of the ivy sign compared to MR CVR. Methods: For this retrospective cross-sectional study, we included consecutive patients with uncompensated MMA who were eligible for extracranial-intracranial bypass surgery and completed a preoperative MRI FLAIR with CVR study. Patients with incomplete or artefact degraded imaging were excluded. Two neuroradiologists who were blinded for the CVR result independently assessed the degree of ivy sign on axial 2D FLAIR imaging as grade 0, 1 or 2 in 8 predefined brain regions. Two different readers, blinded for ivy sign, independently assessed the presence or absence of steal in the same regions on MR CVR. Consensus was reached for discordant findings. We assessed interrater agreement using weighted kappa followed by diagnostic accuracy measures for the ivy sign using MR CVR as a reference. Results: We enrolled 73 patients (41/73 (56.1%) female; mean age 43). 47/73 (64%) patients had bilateral MMA with on average 3 brain regions with abnormal CVR. Interrater agreement was good for ivy sign (weighted kappa 0.63 (95%CI 0.54-0.71)) and excellent for CVR (kappa 0.95 (95% CI 0.87-1.00)). For a total of 156/584 (26.7%) brain regions, there was agreement between a positive ivy sign and the presence of steal on CVR and 240/584 (41%) brain regions had no ivy sign and normal CVR. The sensitivity of the ivy sign was 64.5%, specificity 70.2%, positive predictive value 60.5% and negative predictive value 73.6% with an AUC of 0.68 (95% CI 0.64-0.72) Conclusions: In this preselected cohort with high pretest probability for hemodynamic compromise, the diagnostic accuracy of the ivy sign compared to MR CVR was limited and needs to be used with caution to determine haemodynamic status in MMA.

Abstract TP38: Cognitive Decline After Stroke Correlates With Small Vessel Disease and Race

Introduction: An Asian study found 30% of patients showed a cognitive decline measured by Montreal Cognitive Assessment (MoCA) score one year after stroke. There is limited US data on this subject. We investigated 1) decline rate in MoCA one year post-stroke 2) racial differences in baseline MoCA post-stroke and 3) if racial differences in small vessel disease (SVD) correlated with the decline in MoCA. Methods: MoCA was assessed in the PREMIERS trial (NCT#02541032) at baseline and one year after index stroke. Sub-scores for each MoCA domain were combined into a total score. The Fazekas scale was used to rate white matter signal abnormalities on patient MRI FLAIR images at baseline. Patients were categorized by total, deep white matter (DWM), and periventricular white matter scores. They were then sub-categorized into none/mild or moderate/severe groups. A one year cognitive decline was defined as a decline in MoCA scores by ≥2 points. Results: Of 280 patients, 30.4% had a ≥2 decline in MoCA score, with no significant difference between AA and white (29.4% vs. 32.9%, p=0.57). However, AA patients had lower baseline total MoCA scores (20.4±4.6 vs. 22.6±3.9, p&lt;0.001), visuospatial/executive (3.1±1.5 vs. 3.7±1.3, p&lt;0.001), naming (2.5±0.7 vs. 2.8±0.4, p=0.001), and attention (4.2±2.1 vs. 4.9±1.9, p&lt;0.001) sub-scores. Overall, baseline MoCA was lower in patients with moderate/severe total Fazekas scores (20.1±5.0 vs. 21.6±4.2, p=0.015) and DWM sub-scores (20.1±5.2 vs. 21.4±4.2, p=0.048). In AA, baseline MoCA remained lower in patients with moderate/severe total Fazekas scores (19.6±5.1 vs. 21.0±4.2, p=0.039) and DWM sub-scores (19.4±5.5 vs. 21.0±4.1, p=0.055). This was contrary to White patients with moderate/severe total Fazekas scores (22.3. ±4.4 vs. 23.0±3.9, p=0.50) and DWM sub-scores (23.1±2.3 vs. 22.7±4.4, p=0.73). Conclusion: We report that 30.4% of patients in this US-based study showed a decline in MoCA score one year post-stroke, which aligns with a similar study conducted in Asia. A significant association was found between the AA race and lower baseline total MoCA score compared to white patients. Moderate/severe total and DWM Fazekas scores are also significantly associated with lower baseline MoCA scores, a finding specific to AA patients.

Lobar Microbleeds in the Posterior Cortical Atrophy Syndrome: A Comparison to Typical Alzheimer's Disease.

Posterior cortical atrophy is a clinico-radiographical syndrome that presents with higher-order visual dysfunction and is most commonly due to Alzheimer's disease. Understanding factors associated with atypical presentations of Alzheimer's disease, such as posterior cortical atrophy (PCA), holds promise to shape our understanding of AD pathophysiology. Thus, we aimed to compare MRI evidence of lobar microbleeds (LMBs) in posterior cortical atrophy (PCA) syndrome to typical AD (tAD) and to assess and compare MRI evidence of cerebral amyloid angiopathy (CAA) in each group. We retrospectively collected clinical and MRI data from participants with PCA (n = 26), identified from an institutional PCA registry, and participants with tAD (n = 46) identified from electronic health records from a single institution. LMBs were identified on susceptibility-weighted imaging (SWI); the Fazekas grade of white matter disease was assessed using FLAIR images, and Boston criteria version 2.0 for cerebral amyloid angiopathy were applied to all data. The proportion of participants with PCA and LMB (7.7%) was lower than for tAD (47.8%) (p = 0.005). The frequency of "probable" CAA was similar in both groups, while "possible" CAA was more frequent in tAD (30.4%) than PCA (0%) (p = 0.001). The Fazekas grades were not different between groups. Lobar microbleeds on SWI were not more common in PCA than in typical AD. Clinicopathological investigations are necessary to confirm these findings. The factors that contribute to the posterior cortical atrophy phenotype are unknown.

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.