Fluid-attenuated Inversion Recovery MRI Research Articles

3D FLAIR Cochlear Abnormality Does Not Predict on Sudden Sensorineural Hearing Loss Prognosis: A Retrospective Cohort Study

Background and Objectives Previous studies reported abnormalities in MRI as a poor prognostic indicator of sudden sensorineural hearing loss (SSNHL). Since abnormalities in three-dimensional (3D) fluid-attenuated inversion recovery (FLAIR) are strongly correlated with the initial hearing function, the prognostic value of the 3D FLAIR images should be carefully evaluated to avoid collinearity. We aimed to evaluate abnormalities on the 3D FLAIR images as an independent prognostic factor in the matched SSNHL groups.Subjects and Method We retrospectively reviewed medical records of 179 patients with SSNHL who underwent temporal MRI, including the 3D FLAIR sequence, between January 2015 and December 2019. Patients were divided based on the presence of cochlear abnormalities on the 3D FLAIR images. Hearing prognosis was evaluated with and without matching for initial hearing and treatment interval.Results The groups were similar in sex (p=0.091), age (p=0.925), treatment interval (p= 0.216), and MRI interval (p=0.828). Notably, patients with cochlear abnormalities on the 3D FLAIR images showed distinctly more severe hearing loss (p<0.001) at the initial pure tone average (PTA) assessment and poorer outcomes (p<0.001) compared to those without abnormality. After matching for initial hearing and treatment interval, the hearing outcome, measured by PTA, was similar between the groups (p=0.681).Conclusion Cochlear signal abnormality in 3D FLAIR MRI was associated with poor initial hearing. However, it did not affect hearing recovery outcomes when the groups were matched.

FLAIR MRI biomarkers of the normal appearing brain matter are related to cognition

A novel biomarker panel was proposed to quantify macro and microstructural biomarkers from the normal-appearing brain matter (NABM) in multicentre fluid-attenuation inversion recovery (FLAIR) MRI. The NABM is composed of the white and gray matter regions of the brain, with the lesions and cerebrospinal fluid removed. The primary hypothesis was that NABM biomarkers from FLAIR MRI are related to cognitive outcome as determined by MoCA score. There were three groups of features designed for this task based on 1) texture: microstructural integrity (MII), macrostructural damage (MAD), microstructural damage (MID), 2) intensity: median, skewness, kurtosis and 3) volume: NABM to ICV volume ratio. Biomarkers were extracted from over 1400 imaging volumes from more than 87 centres and unadjusted ANOVA analysis revealed significant differences in means of the MII, MAD, and NABM volume biomarkers across all cognitive groups. In an adjusted ANCOVA model, a significant relationship between MoCA categories was found that was dependent on subject age for MII, MAD, intensity, kurtosis and NABM volume biomarkers. These results demonstrate that structural brain changes in the NABM are related to cognitive outcome (with different relationships depending on the age of the subjects). Therefore these biomarkers have high potential for clinical translation. As a secondary hypothesis, we investigated whether texture features from FLAIR MRI can quantify microstructural changes related to how “structured” or “damaged” the tissue is. Based on correlation analysis with diffusion weighted MRI (dMRI), it was shown that FLAIR MRI texture biomarkers (MII and MAD) had strong correlations to mean diffusivity (MD) which is related to tissue degeneration in the GM and WM regions. As FLAIR MRI is routinely collected for clinical neurological examinations, novel biomarkers from FLAIR MRI could be used to supplement current clinical biomarkers and for monitoring disease progression. Biomarkers could also be used to stratify patients into homogeneous disease subgroups for clinical trials, or to learn more about mechanistic development of dementia disease.

Comparison of 11C-Pittsburgh Compound B and 18F-Flutemetamol White Matter Binding in PET.

PET imaging with β-amyloid ligands is emerging as a molecular imaging technique targeting white matter integrity and demyelination. β-amyloid PET ligands such as 11C-Pittsburgh compound B (11C-PiB) have been considered for quantitative measurement of myelin content changes in multiple sclerosis, but 11C-PiB is not commercially available given its short half-life. A 18F PET ligand such as flutemetamol with a longer half-life may be an alternative, but its ability to differentiate white matter hyperintensities (WMH) from normal-appearing white matter (NAWM) and its relationship with age remains to be investigated. Methods: Cognitively unimpaired (CU) older and younger adults (n = 61) were recruited from the community responding to a study advertisement for β-amyloid PET. Participants prospectively underwent MRI, 11C-PiB, and 18F-flutemetamol PET scans. MRI fluid-attenuated inversion recovery images were segmented into WMH and NAWM and registered to the T1-weighted MRI. 11C-PiB and 18F-flutemetamol PET images were also registered to the T1-weighted MRI. 11C-PiB and 18F-flutemetamol SUV ratios (SUVrs) from the WMH and NAWM were calculated using cerebellar crus uptake as a reference for both 11C-PiB and 18F-flutemetamol. Results: The median age was 38 y (range, 30-48 y) in younger adults and 67 y (range, 61-83 y) in older adults. WMH and NAWM SUVrs were higher with 18F-flutemetamol than with 11C-PiB in both older (P < 0.001) and younger (P < 0.001) CU adults. 11C-PiB and 18F-flutemetamol SUVrs were higher in older than in younger CU adults in both WMH (P < 0.001) and NAWM (P < 0.001). 11C-PiB and 18F-flutemetamol SUVrs were higher in NAWM than WMH in both older (P < 0.001) and younger (P < 0.001) CU adults. There was no apparent difference between 11C-PiB and 18F-flutemetamol SUVrs in differentiating WMH from NAWM in older and in younger adults. Conclusion:11C-PiB and 18F-flutemetamol show a similar topographic pattern of uptake in white matter with a similar association with age in WMH and NAWM. 11C-PiB and 18F-flutemetamol can also effectively distinguish between WMH and NAWM. However, given its longer half-life, commercial availability, and higher binding potential, 18F-flutemetamol can be an alternative to 11C-PiB in molecular imaging studies specifically targeting multiple sclerosis to evaluate white matter integrity.

Symptom: Serous Middle Ear Effusion and Hearing Loss

Symptom: Serous Middle Ear Effusion and Hearing Loss

Association Between Antemortem FLAIR White Matter Hyperintensities and Neuropathology in Brain Donors Exposed to Repetitive Head Impacts.

Late neuropathologies of repetitive head impacts from contact sports can include chronic traumatic encephalopathy (CTE) and white matter degeneration. White matter hyperintensities (WMH) on fluid-attenuated inversion recovery (FLAIR) MRI scans are often viewed as microvascular disease from vascular risk, but might have unique underlying pathologies and risk factors in the setting of repetitive head impacts. We investigated the neuropathologic correlates of antemortem WMH in brain donors exposed to repetitive head impacts. The association between WMH and repetitive head impact exposure and informant-reported cognitive and daily function were tested. This imaging-pathologic correlation study included symptomatic male decedents exposed to repetitive head impacts. Donors had antemortem FLAIR scans from medical records and were without evidence of CNS neoplasm, large vessel infarcts, hemorrhage, or encephalomalacia. WMH were quantified using log-transformed values for total lesion volume (TLV), calculated using the lesion prediction algorithm from the Lesion Segmentation Toolbox. Neuropathologic assessments included semiquantitative ratings of white matter rarefaction, cerebrovascular disease, hyperphosphorylated tau (p-tau) severity (CTE stage, dorsolateral frontal cortex), and β-amyloid (Aβ). Among football players, years of play was a proxy for repetitive head impact exposure. Retrospective informant-reported cognitive and daily function were assessed using the Cognitive Difficulties Scale (CDS) and Functional Activities Questionnaire (FAQ). Regression models controlled for demographics, diabetes, hypertension, and MRI resolution. Statistical significance was defined as p ≤ 0.05. The sample included 75 donors: 67 football players and 8 nonfootball contact sport athletes or military veterans. Dementia was the most common MRI indication (64%). Fifty-three (70.7%) had CTE at autopsy. Log TLV was associated with white matter rarefaction (odds ratio [OR] 2.32, 95% confidence interval [CI] 1.03, 5.24; p = 0.04), arteriolosclerosis (OR 2.38, 95% CI 1.02, 5.52; p = 0.04), CTE stage (OR 2.58, 95% CI 1.17, 5.71; p = 0.02), and dorsolateral frontal p-tau severity (OR 3.03, 95% CI 1.32, 6.97; p = 0.01). There was no association with Aβ. More years of football play was associated with log TLV (unstandardized β 0.04, 95% CI 0.01, 0.06; p = 0.01). Greater log TLV correlated with higher FAQ (unstandardized β 4.94, 95% CI 0.42, 8.57; p = 0.03) and CDS scores (unstandardized β 15.35, 95% CI -0.27, 30.97; p = 0.05). WMH might capture long-term white matter pathologies from repetitive head impacts, including those from white matter rarefaction and p-tau, in addition to microvascular disease. Prospective imaging-pathologic correlation studies are needed. This study provides Class IV evidence of associations between FLAIR white matter hyperintensities and neuropathologic changes (white matter rarefaction, arteriolosclerosis, p-tau accumulation), years of American football play, and reported cognitive symptoms in symptomatic brain donors exposed to repetitive head impacts.

Acute posterior reversible encephalopathy syndrome (PRES) in setting of interferon-beta use: case presentation with reduction of edema in 72\u2009h after cessation of interferon-beta therapy with sub-clinical inflammation

BackgroundPosterior reversible encephalopathy syndrome (PRES) represents a transient change in mental status with associated vasogenic edema of cortical and subcortical brain structures. It is often attributed to multifactorial etiology including hypertension and altered hemodynamics and disruption of vessel integrity. Patients with autoimmune disease and certain immune modulator therapies are at greater risk.Case presentationA 54-year-old female with past medical history of well-controlled multiple sclerosis on interferon-beta since 2013, presented with witnessed tonic colonic seizure. She also was noted to demonstrate left gaze deviation and left-sided hemiparesis. MRI fluid-attenuated inversion recovery sequence showed hyperintensity of the subcortical U fibers, concentrated in the occipital, parietal lobes and frontal lobes. Systolic blood pressure was 160 mmHg on arrival. The patient was started on seizure prophylxis and Interferon beta was discontinued. The patient’s mentation, seizures and hemiapresis significantly improved in next 72 h with tight blood pressure control, and had notble improvement on MRI imaging and inflammatory markers. Lumbar puncture CSF results were devoid of infectious and autoimmune pathology.ConclusionsA middle-aged female with multiple sclerosis who was on chronic IFN-beta presented to the emergency room with a witnessed tonic-clonic seizure, with MRI T2 FLAIR imaging consistent with PRES. She had notable clinical improvement with decreased edema on imaging and improved inflammatory markers 72 h after cessation of IFN-beta therapy.

Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia.

To test the hypothesis that a multicenter-validated computer deep learning algorithm detects MRI-negative focal cortical dysplasia (FCD). We used clinically acquired 3-dimensional (3D) T1-weighted and 3D fluid-attenuated inversion recovery MRI of 148 patients (median age 23 years [range 2-55 years]; 47% female) with histologically verified FCD at 9 centers to train a deep convolutional neural network (CNN) classifier. Images were initially deemed MRI-negative in 51% of patients, in whom intracranial EEG determined the focus. For risk stratification, the CNN incorporated bayesian uncertainty estimation as a measure of confidence. To evaluate performance, detection maps were compared to expert FCD manual labels. Sensitivity was tested in an independent cohort of 23 cases with FCD (13 ± 10 years). Applying the algorithm to 42 healthy controls and 89 controls with temporal lobe epilepsy disease tested specificity. Overall sensitivity was 93% (137 of 148 FCD detected) using a leave-one-site-out cross-validation, with an average of 6 false positives per patient. Sensitivity in MRI-negative FCD was 85%. In 73% of patients, the FCD was among the clusters with the highest confidence; in half, it ranked the highest. Sensitivity in the independent cohort was 83% (19 of 23; average of 5 false positives per patient). Specificity was 89% in healthy and disease controls. This first multicenter-validated deep learning detection algorithm yields the highest sensitivity to date in MRI-negative FCD. By pairing predictions with risk stratification, this classifier may assist clinicians in adjusting hypotheses relative to other tests, increasing diagnostic confidence. Moreover, generalizability across age and MRI hardware makes this approach ideal for presurgical evaluation of MRI-negative epilepsy. This study provides Class III evidence that deep learning on multimodal MRI accurately identifies FCD in patients with epilepsy initially diagnosed as MRI negative.

Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.

Segmentation of white matter lesions in multicentre FLAIR MRI

White matter lesions (WML) in the brain are thought to be related to ischemic processes, demyelination, and axonal degeneration. The presence of WML predict cognitive decline, dementia, stroke, and death. Lesion progression increases these risks, making WML significant clinical biomarkers for investigation. To analyze WML objectively, consistently, and efficiently, automated WML segmentation methods for neurological MRI have been the focus of extensive research efforts. There have been many unsupervised and traditional machine learning methods proposed over the years. Recently, deep learning architectures have been utilized for WML segmentation with promising results. In this work, we evaluate seven WML segmentation tools for multicentre fluid attenuated inversion recovery (FLAIR) MRI. Two traditional methods were evaluated, one unsupervised method and the other a traditional machine learning approach. The traditional methods were compared to five deep learning-based approaches. FLAIR MRI have the advantage of highlighting WML lesions robustly and are used routinely in neurological workflows. Automated WML segmentation tools for FLAIR MRI could optimize clinical workflows and improve patient care. The WML segmentation algorithms were evaluated on a multicentre, multi-disease FLAIR MRI database acquired with varying scanners and protocols. In total 252 imaging volumes (~13 K image slices) with annotations, from 5 multicentre datasets (33 imaging centres) were used to train, validate and test the WML segmentation methods. Two clinical datasets, which include dementia and vascular disease pathologies, and three open-source datasets were used. To examine clinical utility of each algorithm and establish proof of effectiveness, algorithms were evaluated over several dimensions related to accuracy, generalizability, and robustness to pathology. This work presents a framework for evaluating the efficacy of WML segmentation algorithms for improved reliability, patient safety and clinical trials. Of all methods, SC U-Net was found to be the best algorithm for WML segmentation in terms of highest Dice similarity coefficient (DSC) over most dimensions (mean DSC = 0.71 over all volumes). Deep learning methods outperformed traditional methods, especially in lower lesion loads, but were not able to generalize across all disease categories or datasets.

Diagnostic value of 3D-FLAIR MRI in children with sudden deafness caused by inner ear hemorrhage

ObjectiveTo investigate the diagnostic value of three-dimensional fluid-attenuated inversion recovery (3D-FLAIR) MRI in children with sudden deafness caused by inner ear hemorrhage.MethodsThe diagnostic efficacies of three different MRI sequences in...

Fluid-Attenuated Inversion Recovery Hyperintense Ischemic Stroke Predicts Less Favorable 90-Day Outcome after Intravenous Thrombolysis

Introduction: The absence of an ischemic lesion on MRI fluid-attenuated inversion recovery (FLAIR) is helpful in predicting stroke onset within 4.5 h. However, some ischemic strokes become visible on FLAIR within 4.5 h. We hypothesized that the early lesion visibility on FLAIR may predict stroke outcome 90 days after intravenous (IV) thrombolysis, independent of time. Materials and Methods: We analyzed data from acute ischemic stroke patients presenting over the last 10 years who were screened with MRI and treated with IV thrombolysis within 4.5 h from onset. Three independent readers assessed whether ischemic lesions seen on diffusion-weighted imaging were also FLAIR positive based on visual inspection. Multivariable regression analyses were used to obtain an adjusted odds ratio of favorable clinical and radiological outcomes based on FLAIR positivity. Results: Of 297 ischemic stroke patients, 25% had lesion visibility on initial FLAIR. The interrater agreement for the FLAIR positivity assessment was 84% (κ = 0.604, 95% CI: 0.557–0.652). Patients with FLAIR-positive lesions had more right hemispheric strokes (57 vs. 41%, p = 0.045), were imaged later (129 vs. 104 min, p = 0.036), and had less frequent favorable 90-day functional outcome (49 vs. 63%, p = 0.028), less frequent early neurologic improvement (30 vs. 58%, p = 0.001), and more frequent contrast extravasation to the cerebrospinal fluid space (44 vs. 26%, p = 0.008). Conclusions: Early development of stroke lesion on FLAIR within 4.5 h of onset is associated with reduced likelihood of favorable 90-day outcome after IV thrombolysis.

Multi-Modal Segmentation of 3D Brain Scans Using Neural Networks.

Anatomical segmentation of brain scans is highly relevant for diagnostics and neuroradiology research. Conventionally, segmentation is performed on T1-weighted MRI scans, due to the strong soft-tissue contrast. In this work, we report on a comparative study of automated, learning-based brain segmentation on various other contrasts of MRI and also computed tomography (CT) scans and investigate the anatomical soft-tissue information contained in these imaging modalities. A large database of in total 853 MRI/CT brain scans enables us to train convolutional neural networks (CNNs) for segmentation. We benchmark the CNN performance on four different imaging modalities and 27 anatomical substructures. For each modality we train a separate CNN based on a common architecture. We find average Dice scores of 86.7 ± 4.1% (T1-weighted MRI), 81.9 ± 6.7% (fluid-attenuated inversion recovery MRI), 80.8 ± 6.6% (diffusion-weighted MRI) and 80.7 ± 8.2% (CT), respectively. The performance is assessed relative to labels obtained using the widely-adopted FreeSurfer software package. The segmentation pipeline uses dropout sampling to identify corrupted input scans or low-quality segmentations. Full segmentation of 3D volumes with more than 2 million voxels requires <1s of processing time on a graphical processing unit.

Protein A Immunoadsorption Relieves Autoimmune Glial Fibrillary Acidic Protein Astrocytopathy after Unsuccessful Methylprednisolone Treatment

Background: Autoimmune glial fibrillary acidic protein (GFAP) astrocytopathy (GFAP-A) is a recently defined autoimmune inflammatory disease of the central nervous system in which GFAP IgG is present in the cerebrospinal fluid (CSF). Its primary clinical manifestation is meningoencephalitis, and it usually responds well to corticosteroids. Herein, we report a case of a patient with GFAP-A with initial symptoms of psychological and cognitive impairment, which did not respond to high-dose methylprednisolone therapy but was successfully treated with protein A immunoadsorption (PAIA) therapy. Methods: GFAP IgG was detected by indirect immunofluorescence assay. The patient’s data were analyzed retrospectively. Results: A 48-year-old man presented with anxiety, depression, cognitive decline, tremor, gait disturbance, and fecal and urine incontinence. Autoimmune GFAP-A was diagnosed based on the following: (1) T2-weighted and fluid-attenuated inversion recovery MRI findings of hypersensitive lesions in the subcortical and deep white matter of the brain, with multiple longitudinally extensive lesions in the cervical and chest regions of the spinal cord, and (2) high levels of GFAP IgG in the CSF. Clinical symptoms and abnormalities detected on neuroimaging worsened after administration of high-dose intravenous methylprednisolone (IVMP) and intravenous immunoglobulin (IVIG) but improved significantly after PAIA therapy. Conclusion: Psychological impairment can be the first sign of autoimmune GFAP-A. PAIA might be an effective treatment for patients with GFAP-A who respond poorly to conventional IVMP and IVIG therapy.

White Matter Hyperintensities Are Associated With Severity of Essential Tremor in the Elderly.

Background: Essential tremor (ET) occurs with steeply increasing prevalence in the elderly, and apart from disease duration, age is independently associated with an increase of tremor amplitude and a decrease of frequency. White matter hyperintensities (WMHs) are a common finding in the elderly, and their role in the pathophysiology of ET is unknown. The aims of this study were to examine whether ET patients differ in their total or region-specific WMH volumes from healthy controls and to determine the impact of WMH on tremor characteristics.Methods: A total of 47 elderly ET patients with a mean age of 72 years and 39 age-matched healthy controls underwent a thorough clinical assessment and 3T MRI. Total WMH volumes were derived from T2-weighted fluid-attenuated inversion recovery (FLAIR) MR images. Additionally, region of interest-based WMH volumes for the Johns Hopkins University (JHU) white matter tracts and labels were calculated, and WMHs were assessed semiquantitatively using the Fazekas scale.Results: Essential tremor patients and healthy controls did not differ in their total or tract-specific WMH volumes or Fazekas scores. However, WMH volume was significantly positively correlated with tremor severity on the TETRAS scale, and there was a significant negative correlation with the mean accelerometric tremor frequency. In a multiple linear regression model including disease duration, age, and age-adjusted total WMH volume, only the WMH volume significantly predicted tremor severity, while age and disease duration were not significant.Conclusion: We found evidence for a direct association between WMH volume and tremor severity. If confirmed by larger studies, our findings could explain the well-known relation between age and tremor severity.

Spatial concordance of DNA methylation classification in diffuse glioma.

BackgroundIntratumoral heterogeneity is a hallmark of diffuse gliomas. DNA methylation profiling is an emerging approach in the clinical classification of brain tumors. The goal of this study is to investigate the effects of intratumoral heterogeneity on classification confidence.MethodsWe used neuronavigation to acquire 133 image-guided and spatially separated stereotactic biopsy samples from 16 adult patients with a diffuse glioma (7 IDH-wildtype and 2 IDH-mutant glioblastoma, 6 diffuse astrocytoma, IDH-mutant and 1 oligodendroglioma, IDH-mutant and 1p19q codeleted), which we characterized using DNA methylation arrays. Samples were obtained from regions with and without abnormalities on contrast-enhanced T1-weighted and fluid-attenuated inversion recovery MRI. Methylation profiles were analyzed to devise a 3-dimensional reconstruction of (epi)genetic heterogeneity. Tumor purity was assessed from clonal methylation sites.ResultsMolecular aberrations indicated that tumor was found outside imaging abnormalities, underlining the infiltrative nature of this tumor and the limitations of current routine imaging modalities. We demonstrate that tumor purity is highly variable between samples and explains a substantial part of apparent epigenetic spatial heterogeneity. We observed that DNA methylation subtypes are often, but not always, conserved in space taking tumor purity and prediction accuracy into account.ConclusionOur results underscore the infiltrative nature of diffuse gliomas and suggest that DNA methylation subtypes are relatively concordant in this tumor type, although some heterogeneity exists.

The diagnostic reliability and validity of noninvasive imaging modalities to assess leptomeningeal collateral flow for ischemic stroke patients: A systematic review and meta-analysis.

Leptomeningeal collateral flow (LMF) is associated with infarct area and clinical outcome for ischemic stroke patients. Although LMF can be detected by multiple imaging methods, but their diagnostic performance is uncertain.The aim of this study was to evaluate the diagnostic validity or reliability of noninvasive image methods in assessing LMF.Databases included PubMed, Web of Science, Embase, and Cochrane Library.Original observational cohort studies.Ischemic stroke patients.Different noninvasive image methods to assess LMF.Newcastle–Ottawa Scale to evaluate the quality of the studies; forest plot to show pooled results; I2 and Egger test to evaluate the heterogeneity and publication bias.Thirty of the 126 selected studies were eligible. For CT angiography, the interobserver agreement ranged from 0.494 to 0.93 and weighted kappa was 0.888; for patients receiving thrombolysis or endovascular treatment, 0.68 to 0.91; 0.494 to 0.89 for the 2-point system, 0.60 to 0.93 for the 3-point system, 0.68 to 0.87 for the system of >4 points; area under the curve (AUC) was 0.78. For perfusion computed tomography (CTP), the interobserver agreement ranged from 0.724 to 0.872; for patients receiving thrombolysis or endovascular treatment, 0.74 to 0.872; 0.724 for the 2-point system, 0.783 to 0.953 for the 3-point system; the intraobserver agreement was 0.884; AUC was 0.826. For MRI-fluid attenuated inversion recovery (FLAIR), the interobserver agreement ranged from 0.58 to 0.86; for patients receiving thrombolysis or endovascular treatment, 0.75 to 0.86; 0.86 for the two-point system, 0.77 to 0.87 for the system of more than 5 points; AUC was 0.82.No pooled data of CTP and FLAIR. The difference cohort study had difference bias. The unpublished data were not included.CT angiography is a good tool for assessing LMF. CTP shows a good validity and reliability, but its diagnostic value needs more evidence. FLAIR is a good modality to assess LMF. These image methods had better validity and reliability to evaluate LMF of patients receiving thrombolysis or endovascular treatment than all ischemic stroke patients.

Diffusion models reveal white matter microstructural changes with ageing, pathology and cognition.

White matter microstructure undergoes progressive changes during the lifespan, but the neurobiological underpinnings related to ageing and disease remains unclear. We used an advanced diffusion MRI, Neurite Orientation Dispersion and Density Imaging, to investigate the microstructural alterations due to demographics, common age-related pathological processes (amyloid, tau and white matter hyperintensities) and cognition. We also compared Neurite Orientation Dispersion and Density Imaging findings to the older Diffusion Tensor Imaging model-based findings. Three hundred and twenty-eight participants (264 cognitively unimpaired, 57 mild cognitive impairment and 7 dementia with a mean age of 68.3 ± 13.1 years) from the Mayo Clinic Study of Aging with multi-shell diffusion imaging, fluid attenuated inversion recovery MRI as well as amyloid and tau PET scans were included in this study. White matter tract level diffusion measures were calculated from Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging. Pearson correlation and multiple linear regression analyses were performed with diffusion measures as the outcome and age, sex, education/occupation, white matter hyperintensities, amyloid and tau as predictors. Analyses were also performed with each diffusion MRI measure as a predictor of cognitive outcomes. Age and white matter hyperintensities were the strongest predictors of all white matter diffusion measures with low associations with amyloid and tau. However, neurite density decrease from Neurite Orientation Dispersion and Density Imaging was observed with amyloidosis specifically in the temporal lobes. White matter integrity (mean diffusivity and free water) in the corpus callosum showed the greatest associations with cognitive measures. All diffusion measures provided information about white matter ageing and white matter changes due to age-related pathological processes and were associated with cognition. Neurite orientation dispersion and density imaging and diffusion tensor imaging are two different diffusion models that provide distinct information about variation in white matter microstructural integrity. Neurite Orientation Dispersion and Density Imaging provides additional information about synaptic density, organization and free water content which may aid in providing mechanistic insights into disease progression.

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.

Abstract P708: Artificial Intelligence Enabled-Electrocardiography for the Detection of Cerebral Infarcts in Patients With Atrial Fibrillation

Background: Atrial fibrillation (AF) is an established risk factor for ischemic stroke, but it can be paroxysmal and may go undiagnosed. An artificial intelligence (AI)-enabled ECG acquired during normal sinus rhythm was recently shown to detect silent AF. The objective of this study was to determine if AI-ECG AF score is associated with presence of cerebral infarcts. Methods: Participants from a population-based study ages 30 to 95 years with T2 fluid attenuation inversion recovery (FLAIR) MRI obtained between October 10, 2011, and November 2, 2017 were considered for inclusion. Participants without ECG were excluded. AI-ECG score was calculated using most recent ECG with normal sinus rhythm at the time of MRI. Presence of infarcts was determined on FLAIR MRI scans. Logistic regression was run to evaluate the relationship between AI-ECG AF score and presence of cerebral infarcts. Similar analyses were performed using history of AF rather than AI-ECG AF score as predictor. Age and sex were included as covariates. We also examined whether a high-threshold AI-ECG score was associated with infarcts. In a prior study, an AI-ECG AF score &gt; 0.5 was associated with a cumulative incidence of AF of 21.5% at 2 years and 52.2% at 10 years. Results: This study included 1,373 individuals. Average age was 69.6 years and 53% of participants were male. There were 136 (10%) individuals with ECG-confirmed AF; 1237 (90%) participants had no AF history. Of participants with AF, 23% (n=31) were on anticoagulation, 47% (n=64) were on antiplatelet and 18% (n=24) were on dual therapy. Only 1.3% (n=16) of patients without AF were on anticoagulation and 47% (n=578) were on antiplatelet therapy. Ischemic infarcts were detected in 214 (15.6%) patients. As a continuous measure AI-ECG was associated with infarcts but not after adjusting for age and sex (p=0.46). AI-ECG AF score &gt; 0.5 was associated with infarcts ( p &lt; 0.001); even after adjusting for age and sex ( p = 0.03). History of AF was also associated with infarcts after adjusting for age and sex ( p = 0.018). Conclusion: AI-ECG AF score and history of AF were associated with presence of cerebral infarcts after adjusting for age and sex. This tool could be useful in select patients with cryptogenic stroke but further investigation would be required.

Bevacizumab for radiation necrosis following radiotherapy of brain metastatic disease: a systematic review & meta-analysis

BackgroundRadiotherapy is the mainstay of brain metastasis (BM) management. Radiation necrosis (RN) is a serious complication of radiotherapy. Bevacizumab (BV), an anti-vascular endothelial growth factor monoclonal antibody, has been increasingly used for RN treatment. We systematically reviewed the medical literature for studies reporting the efficacy and safety of bevacizumab for treatment of RN in BM patients.Materials and methodsPubMed, Medline, EMBASE, and Cochrane library were searched with various search keywords such as “bevacizumab” OR “anti-VEGF monoclonal antibody” AND “radiation necrosis” OR “radiation-induced brain necrosis” OR “RN” OR “RBN” AND “Brain metastases” OR “BM” until 1st Aug 2020. Studies reporting the efficacy and safety of BV treatment for BM patients with RN were retrieved. Study selection and data extraction were carried out by independent investigators. Open Meta Analyst software was used as a random effects model for meta-analysis to obtain mean reduction rates.ResultsTwo prospective, seven retrospective, and three case report studies involving 89 patients with RN treated with BV were included in this systematic review and meta-analysis. In total, 83 (93%) patients had a recorded radiographic response to BV therapy, and six (6.7%) had experienced progressive disease. Seven studies (n = 73) reported mean volume reductions on gadolinium-enhanced T1 (mean: 47.03%, +/− 24.4) and T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI images (mean: 61.9%, +/− 23.3). Pooling together the T1 and T2 MRI reduction rates by random effects model revealed a mean of 48.58 (95% CI: 38.32–58.85) for T1 reduction rate and 62.017 (95% CI: 52.235–71.799) for T2W imaging studies. Eighty-five patients presented with neurological symptoms. After BV treatment, nine (10%) had stable symptoms, 39 (48%) had improved, and 34 (40%) patients had complete resolution of their symptoms. Individual patient data was available for 54 patients. Dexamethasone discontinuation or reduction in dosage was observed in 30 (97%) of 31 patients who had recorded dosage before and after BV treatment. Side effects were mild.ConclusionsBevacizumab presents a promising treatment strategy for patients with RN and brain metastatic disease. Radiographic response and clinical improvement was observed without any serious adverse events. Further class I evidence would be required to establish a bevacizumab recommendation in this group of patients.