Fluid-attenuated Inversion Recovery Images Research Articles

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

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

Synergistic Role of Quantitative Diffusion Magnetic Resonance Imaging and Structural Magnetic Resonance Imaging in Predicting Outcomes After Traumatic Brain Injury.

This study aimed to assess if quantitative diffusion magnetic resonance imaging analysis would improve prognostication of individual patients with severe traumatic brain injury. We analyzed images of 30 healthy controls to extract normal fractional anisotropy ranges along 18 white-matter tracts. Then, we analyzed images of 33 patients, compared their fractional anisotropy values with normal ranges extracted from controls, and computed severity of injury to white-matter tracts. We also asked 2 neuroradiologists to rate severity of injury to different brain regions on fluid-attenuated inversion recovery and susceptibility-weighted imaging. Finally, we built 3 models: (1) fed with neuroradiologists' ratings, (2) fed with white-matter injury measures, and (3) fed with both input types. The 3 models respectively predicted survival at 1 year with accuracies of 70%, 73%, and 88%. The accuracy with both input types was significantly better (P < 0.05). Quantifying severity of injury to white-matter tracts complements qualitative imaging findings and improves outcome prediction in severe traumatic brain injury.

Post-Covid-19 Acute Disseminated Encephalomyelitis (ADEM) in a 27-year-old girl: Case Report

Acute disseminated encephalomyelitis (ADEM) is a monophasic, multifocal, demyelinating, autoimmune disease that affects the central nervous system (CNS). It usually occurs after a systemic infection, usually viral, including certain coronavirus infections. A 27-year-old girl presented with complaints of left interscapular pain, paresthesias and weakness in the ipsilateral upper limb. These symptoms followed paresthesias on the fingertips of her right hand the day before her admission. she was treated two weeks earlier for pneumonia with COVID-19. Her clinical pattern resulted in a moderate weakness of the left limbs associated with tactil and algic hypoesthesia in the lower left limb ascending until the C4 level in the left side. Magnetic resonance imaging (MRI) of the brain and spinal cord showed diffuse spontaneous hypersignals on fluid-attenuated inversion recovery (FLAIR) images at the cerebral level and on T2-weighted images at the spinal level. These imaging lesions coupled with the medical history of a recent COVID-19 infection led to the diagnosis of acute disseminated encephalomyelitis (ADEM) post covid-19. The clinical condition improved rapidly with intravenous (IV) corticosteroid therapy and IV immunoglobulin combined with physiotherapy. ADEM is a demyelinating autoimmune disease which is increasingly reported during this current corona virus pandemic.

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&lt;0.001) at the initial pure tone average (PTA) assessment and poorer outcomes (p&lt;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.

MRI-Detected Brain Lesions and Cognitive Function in Patients With Atrial Fibrillation Undergoing Left Atrial Catheter Ablation in the Randomized AXAFA-AFNET 5 Trial.

We aimed to assess the prevalence of ischemic brain lesions detected by magnetic resonance imaging and their association with cognitive function 3 months after first-time ablation using continuous oral anticoagulation in patients with paroxysmal atrial fibrillation (AF). We performed a prespecified analysis of the AXAFA-AFNET 5 trial (Anticoagulation Using the Direct Factor Xa Inhibitor Apixaban During Atrial Fibrillation Catheter Ablation: Comparison to Vitamin K Antagonist Therapy), which randomized 674 patients with AF 1:1 to uninterrupted apixaban or vitamin K antagonist therapy before first-time ablation. Brain magnetic resonance imaging using fluid-attenuated inversion recovery and high-resolution diffusion-weighted imaging was obtained within 3 to 48 hours after AF ablation in all eligible patients enrolled in 25 study centers in Europe and the United States. Patients underwent cognitive assessment 3 to 6 weeks before ablation and 3 months after ablation using the Montreal Cognitive Assessment (MoCA). In 84 (26.1%) of 321 patients with analyzable magnetic resonance imaging, high-resolution diffusion-weighted imaging detected at least 1 acute brain lesion, including 44 (27.2%) patients treated with apixaban and 40 (24.8%) patients treated with vitamin K antagonist (P=0.675). Median MoCA score was similar in patients with or without acute brain lesions at 3 months after ablation (28 [interquartile range (IQR), 26-29] versus 28 [IQR, 26-29]; P=0.948). Cerebral chronic white matter damage (defined as Wahlund score ≥4 points) detected by fluid-attenuated inversion recovery was present in 130 (40.5%) patients and associated with lower median MoCA scores before ablation (27 [IQR, 24-28] versus 27 [IQR, 25-29]; P=0.026) and 3 months after ablation (27 [IQR, 25-29] versus 28 [IQR, 26-29]; P=0.011). This association was no longer significant when adjusted for age and sex. Age was associated with lower MoCA scores before ablation (relative risk, 1.02 per 10 years [95% CI, 1.01-1.03]) and 3 months after ablation (relative risk, 1.02 per 10 years [95% CI, 1.01-1.03]). Chronic white matter damage as well as acute ischemic lesions detected by brain magnetic resonance imaging were found frequently after first-time ablation for paroxysmal AF using uninterrupted oral anticoagulation. Acute ischemic brain lesions detected by high-resolution diffusion-weighted imaging were not associated with cognitive function at 3 months after ablation. Lower MoCA scores before and after ablation were associated only with older age, highlighting the safety of AF ablation on uninterrupted oral anticoagulation. URL: https://www. gov; Unique identifier: NCT02227550.

Abstract WP111: Deployment Of Portable, Bedside, Low-field Magnetic Resonance Imaging In The Emergency Department To Evaluate Patients With Acute Stroke

Background and Aims: MRI is critical for diagnosing acute stroke and guiding candidate selection for potential reperfusion therapy. However, rapid stroke evaluation using MRI is often dissuaded by the time required for patients to travel to access-controlled, high-field (1.5-3T) systems. Advances in low-field MRI enable the acquisition of clinically valuable images at the bedside. We report neuroimaging in patients presenting to the Emergency Department (ED) with stroke symptoms using a low-field portable MRI (pMRI) device. Methods: A 64mT pMRI device was deployed in the Yale-New Haven Hospital ED from August 2020 to July 2021. Patients presenting as a “Stroke Code” or “Intracranial Hemorrhage Alert” with no MRI contraindications were scanned. Exams were performed at the bedside, in the vicinity of ED room equipment. Research staff acquired imaging via tablet, with images available immediately after acquisition. Sequences obtained and axial scan times (in minutes) included T1-weighted imaging (4:54), T2-weighted imaging (7:03), fluid-attenuated inversion recovery imaging (9:31), and diffusion-weighed imaging with apparent diffusion coefficient mapping (9:04). Patients’ demographic information, hours from the time of patients' last known normal (LKN) to time of scan, and discharge diagnoses (determined from final imaging interpretation) were assessed. Results: pMRI exams were obtained on 54 patients (28 females, 51.9%; median age 71 years, 20-98 years). Discharge diagnoses included ischemic stroke (42.6%) no intracranial abnormality (31.5%), intraparenchymal hemorrhage (7.4%), atherosclerosis (7.4%), tumor (5.6%), subdural hematoma (3.7%), and intraventricular hemorrhage (1.9%). Patient LKN times ranged from 2 to 144 hours (median of 12 hours; 3 patients with no LKN excluded). The pMRI did not interfere with ED equipment and no significant adverse events occurred. Conclusion: We report the use of a pMRI for bedside neuroimaging in the ED. This approach suggests that pMRI may be viable for supporting rapid diagnosis and treatment candidate selection in patients presenting with stroke symptoms to the ED.

Use of DWI-FLAIR Mismatch to Estimate the Onset Time in Wake-Up Strokes.

PurposeTo compare the MRI characteristics of patients with wake-up ischemic stroke (WUS) and with ischemic stroke with known onset time (clear-onset-time stroke, COS) to clarify the role of diffusion-weighted imaging–fluid-attenuated inversion recovery (DWI-FLAIR) mismatch in estimating the onset time of WUS patients.Patients and MethodsTwo hundred patients with acute ischemic stroke were selected for complete brain MRI within six hours of symptom onset, including DWI and FLAIR sequences. The patients were divided into WUS (n = 78) and COS (n = 122) groups, based on whether the time of onset was known. The general conditions and imaging characteristics were collected to compare the DWI-FLAIR mismatch features between the two groups at different time intervals.ResultsThere was no significant difference in the DWI-FLAIR mismatch on MRI within 2 hour after the first found abnormality between the two groups (50.0% vs 71.8%, p = 0.180). With increasing time, the DWI-FLAIR mismatch decreased substantially in the WUS group, while a higher DWI-FLAIR mismatch presence persisted in the COS group within a four-hour interval from the onset of symptoms to the MRI. The DWI-FLAIR mismatch was significantly lower in the WUS group than in the COS group from symptom identification to MRI at 2–3 h, 3–4 h, and 4–5 h intervals (15% vs 60%, 10.5% vs 48%, 6.7% vs 45.4%; p < 0.01).ConclusionOur results suggest that the presence of DWI-FLAIR mismatch within 2 h of the first found abnormality was not significantly different between WUS and COS. Therefore, Patients with WUS within 2 hours after the first detected abnormality may be suitable for intravenous thrombolysis.

Autoimmune glial fibrillary acidic protein astrocytopathy in children: a retrospective study

ObjectiveTo describe the clinical features of autoimmune glial fibrillary acidic protein (GFAP) astrocytopathy in children.MethodData from 11 pediatric patients with autoimmune GFAP astrocytopathy were retrospectively analyzed.ResultsAll of the patients showed encephalitis and meningoencephalitis or meningoencephalomyelitis with or without myelitis. 45.4% of the patients had fever, 27.3% headaches, 18.2% dizziness, 18.2% drowsiness, and 18.2% mental disorders. Cerebrospinal fluid (CSF) was detected in all patients. The white blood cell counts (WBC) (90.9%), lactic dehydrogenase levels (72.7%), protein level (36.4%), and adenosine deaminase activity (ADA) level (27.3%) were elevated, and the CSF glucose levels (72.7%) were slightly reduced. Nine patients (90%) were found to have brain abnormalities, of which five (50.0%) patients had abnormal symmetrical laminar patterns or line patterns hyperintensity lesions on T2-weighted and fluid-attenuated inversion recovery (FLAIR) images in the basal ganglia, hypothalamus, subcortical white matter and periventricular white matter. The linear radial enhancement pattern of the cerebral white matter was only seen in two patients, with the most common being abnormal enhancement of leptomeninges (50%). Five patients had longitudinally extensive spinal cord lesions.ConclusionThe findings of pediatric patients with autoimmune GFAP astrocytopathy are different from previous reports.

Circulating Metabolome and White Matter Hyperintensities in Women and Men.

White matter hyperintensities (WMH), identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness, are a major risk factor of stroke, dementia, and death. There are no large-scale studies testing associations between WMH and circulating metabolites. We studied up to 9290 individuals (50.7% female, average age 61 years) from 15 populations of 8 community-based cohorts. WMH volume was quantified from T2-weighted or fluid-attenuated inversion recovery images or as hypointensities on T1-weighted images. Circulating metabolomic measures were assessed with mass spectrometry and nuclear magnetic resonance spectroscopy. Associations between WMH and metabolomic measures were tested by fitting linear regression models in the pooled sample and in sex-stratified and statin treatment-stratified subsamples. Our basic models were adjusted for age, sex, age×sex, and technical covariates, and our fully adjusted models were also adjusted for statin treatment, hypertension, type 2 diabetes, smoking, body mass index, and estimated glomerular filtration rate. Population-specific results were meta-analyzed using the fixed-effect inverse variance-weighted method. Associations with false discovery rate (FDR)-adjusted P values (PFDR)<0.05 were considered significant. In the meta-analysis of results from the basic models, we identified 30 metabolomic measures associated with WMH (PFDR<0.05), 7 of which remained significant in the fully adjusted models. The most significant association was with higher level of hydroxyphenylpyruvate in men (PFDR.full.adj=1.40×10-7) and in both the pooled sample (PFDR.full.adj=1.66×10-4) and statin-untreated (PFDR.full.adj=1.65×10-6) subsample. In men, hydroxyphenylpyruvate explained 3% to 14% of variance in WMH. In men and the pooled sample, WMH were also associated with lower levels of lysophosphatidylcholines and hydroxysphingomyelins and a larger diameter of low-density lipoprotein particles, likely arising from higher triglyceride to total lipids and lower cholesteryl ester to total lipids ratios within these particles. In women, the only significant association was with higher level of glucuronate (PFDR=0.047). Circulating metabolomic measures, including multiple lipid measures (eg, lysophosphatidylcholines, hydroxysphingomyelins, low-density lipoprotein size and composition) and nonlipid metabolites (eg, hydroxyphenylpyruvate, glucuronate), associate with WMH in a general population of middle-aged and older adults. Some metabolomic measures show marked sex specificities and explain a sizable proportion of WMH variance.

Development and external validation of a stability machine learning model to identify wake-up stroke onset time from MRI.

To develop and externally validate a machine learning (ML) model based on diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) to identify the onset time of wake-up stroke from MRI. DWI and FLAIR images of stroke patients within 24h of clear symptom onset in our hospital (dataset 1, n = 410) and another hospital (dataset 2, n = 177) were included. Seven ML models based on dataset 1 were developed to estimate the stroke onset time for binary classification (≤ 4.5h or > 4.5h): Random Forest (RF), support vector machine with kernel (svmLinear) or radial basis function kernel (svmRadial), Bayesian (Bayes), K-nearest neighbor (KNN), adaptive boosting (AdaBoost), and neural network (NNET). ROC analysis and RSD were performed to evaluate the performance and stability of the ML models, respectively, and dataset 2 was externally validated to evaluate the model generalization ability using ROC analysis. svmRadial achieved the best performance with the highest AUC and accuracy (AUC: 0.896, accuracy: 0.878), and was the most stable (RSD% of AUC: 0.08, RSD% of accuracy: 0.06). The svmRadial model was then selected as the final model, and the AUC of the svmRadial model for predicting the onset time external validation was 0.895, with 0.825 accuracy. The svmRadial model using DWI + FLAIR is the most stable and generalizable for identifying the onset time of wake-up stroke patients within 4.5h of symptom onset. • Machining learning model helps clinicians to identify wake-up stroke patients within 4.5h of symptom onset. • A prospective study showed that svmRadial model based on DWI + FLAIR was the most stable in predicting the stroke onset time. • External validation showed that svmRadial model has good generalization ability in predicting the stroke onset time.

Consistency of the “central vein sign” in chronic multiple sclerosis lesions

BackgroundIn recent years, there has been an increasing interest in the central vein sign (CVS) as a new imaging marker and previous cross-sectional studies demonstrated that the CVS has the potential to discriminate multiple sclerosis (MS) lesions from non-MS lesions. The aim of this study was to investigate the consistency of the CVS in a longitudinal magnetic resonance imaging (MRI) data set. Methods3T MRI datasets from seventy-one people with MS acquired at baseline and after 12 months-follow-up were analyzed. Chronic lesions were identified on fluid-attenuated inversion recovery (FLAIR) images. Co-registered susceptibility-weighted/FLAIR images were analyzed for the presence of a CVS at baseline and follow-up. ResultsA total of 183 chronic lesions were included in the final analysis. At baseline MRI, a CVS was detectable in 141/183 (77%) lesions. Overall, the CVS was consistent in 114/141 (81%) lesions (Cohen's kappa = 0.46, standard error = 0.07). ConclusionThe CVS is a rather stable feature in chronic MS lesions and therefore represents a robust imaging marker that could increase the specificity of MRI in MS.

High-fidelity fast volumetric brain MRI using synergistic wave-controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN).

The goal of this study is to leverage an advanced fast imaging technique, wave-controlled aliasing in parallel imaging (Wave-CAIPI), and a generative adversarial network (GAN) for denoising to achieve accelerated high-quality high-signal-to-noise-ratio (SNR) volumetric magnetic resonance imaging (MRI). Three-dimensional (3D) T2 -weighted fluid-attenuated inversion recovery (FLAIR) image data were acquired on 33 multiple sclerosis (MS) patients using a prototype Wave-CAIPI sequence (acceleration factor R=3×2, 2.75min) and a standard T2 -sampling perfection with application-optimized contrasts by using flip angle evolution (SPACE) FLAIR sequence (R=2, 7.25min). A hybrid denoising GAN entitled "HDnGAN" consisting of a 3D generator and a 2D discriminator was proposed to denoise highly accelerated Wave-CAIPI images. HDnGAN benefits from the improved image synthesis performance provided by the 3D generator and increased training samples from a limited number of patients for training the 2D discriminator. HDnGAN was trained and validated on data from 25 MS patients with the standard FLAIR images as the target and evaluated on data from eight MS patients not seen during training. HDnGAN was compared to other denoising methods including adaptive optimized nonlocal means (AONLM), block matching with 4D filtering (BM4D), modified U-Net (MU-Net), and 3D GAN in qualitative and quantitative analysis of output images using the mean squared error (MSE) and Visual Geometry Group (VGG) perceptual loss compared to standard FLAIR images, and a reader assessment by two neuroradiologists regarding sharpness, SNR, lesion conspicuity, and overall quality. Finally, the performance of these denoising methods was compared at higher noise levels using simulated data with added Rician noise. HDnGAN effectively denoised low-SNR Wave-CAIPI images with sharpness and rich textural details, which could be adjusted by controlling the contribution of the adversarial loss to the total loss when training the generator. Quantitatively, HDnGAN (λ=10-3 ) achieved low MSE and the lowest VGG perceptual loss. The reader study showed that HDnGAN (λ=10-3 ) significantly improved the SNR of Wave-CAIPI images (p<0.001), outperformed AONLM (p=0.015), BM4D (p<0.001), MU-Net (p<0.001), and 3D GAN (λ=10-3 ) (p<0.001) regarding image sharpness, and outperformed MU-Net (p<0.001) and 3D GAN (λ=10-3 ) (p=0.001) regarding lesion conspicuity. The overall quality score of HDnGAN (λ=10-3 ) (4.25±0.43) was significantly higher than those from Wave-CAIPI (3.69±0.46, p=0.003), BM4D (3.50±0.71, p=0.001), MU-Net (3.25±0.75, p<0.001), and 3D GAN (λ=10-3 ) (3.50±0.50, p<0.001), with no significant difference compared to standard FLAIR images (4.38±0.48, p=0.333). The advantages of HDnGAN over other methods were more obvious at higher noise levels. HDnGAN provides robust and feasible denoising while preserving rich textural detail in empirical volumetric MRI data. Our study using empirical patient data and systematic evaluation supports the use of HDnGAN in combination with modern fast imaging techniques such as Wave-CAIPI to achieve high-fidelity fast volumetric MRI and represents an important step to the clinical translation of GANs.

Optimization of magnetization transfer contrast for EPI FLAIR brain imaging.

To evaluate the impact of magnetization transfer (MT) on brain tissue contrast in turbo-spin-echo (TSE) and EPI fluid-attenuated inversion recovery (FLAIR) images, and to optimize an MT-prepared EPI FLAIR pulse sequence to match the tissue contrast of a clinical reference TSE FLAIR protocol. Five healthy volunteers underwent 3T brain MRI, including single slice TSE FLAIR, multi-slice TSE FLAIR, EPI FLAIR without MT-preparation, and MT-prepared EPI FLAIR with variations of the MT-preparation parameters, including number of preparation pulses, pulse amplitude, and resonance offset. Automated co-registration and gray matter (GM) versus white matter (WM) segmentation was performed using a T1-MPRAGE acquisition, and the GM versus WM signal intensity ratio (contrast ratio) was calculated for each FLAIR acquisition. Without MT preparation, EPI FLAIR showed poor tissue contrast (contrast ratio = 0.98), as did single slice TSE FLAIR. Multi-slice TSE FLAIR provided high tissue contrast (contrast ratio = 1.14). MT-prepared EPI FLAIR closely approximated the contrast of the multi-slice TSE FLAIR images for two combinations of the MT-preparation parameters (contrast ratio = 1.14). Optimized MT-prepared EPI FLAIR provided a 50% reduction in scan time compared to the reference TSE FLAIR acquisition. Optimized MT-prepared EPI FLAIR provides comparable brain tissue contrast to the multi-slice TSE FLAIR images used in clinical practice.

Prevalence, distribution, and clinical associations of suspected postictal changes on brain magnetic resonance imaging in epileptic dogs.

To determine the prevalence of presumed postictal changes (PC) on brain MRI in epileptic dogs, describe their distribution, and recognize possible correlations with different epilepsy features. 540 client-owned dogs with epilepsy and a complete medical record that underwent brain MRI at 4 veterinary referral hospitals between 2016 and 2019. Data were collected regarding signalment, seizure type, seizure severity, time between last seizure and MRI, and etiological classification of epilepsy. Postictal changes were considered when solitary or multiple intraparenchymal hyperintense lesions were observed on T2-weighted and fluid-attenuated inversion recovery images and were hypointense or isointense on T1-weighted sequences, which were not confined to a vascular territory and showed no to mild mass effect and no to mild contrast enhancement. Sixty-seven dogs (12.4%) showed MRI features consistent with PC. The most common brain sites affected were the piriform lobe, hippocampus, temporal neocortex, and cingulate gyrus. Dogs having suffered cluster seizures or status epilepticus were associated with a higher probability of occurrence of PC, compared to dogs with self-limiting seizures (OR 2.39; 95% confidence interval, 1.33 to 4.30). Suspected PC were detected both in dogs with idiopathic epilepsy and in those with structural epilepsy. Dogs with unknown-origin epilepsy were more likely to have presumed PC than were dogs with structural (OR 0.15; 95% confidence interval, 0.06 to 0.33) or idiopathic epilepsy (OR 0.42; 95% confidence interval, 0.20 to 0.87). Time between last seizure and MRI was significantly shorter in dogs with PC. MRI lesions consistent with PC were common in epileptic dogs, and the brain distribution of these lesions varied. Occurrence of cluster seizures or status epilepticus, diagnosis of unknown origin epilepsy, and lower time from last seizure to MRI are predictors of suspected PC.

A prospective study to evaluate the role of multiparametric magnetic resonance imaging in the grading of gliomas using magnetic resonance imaging perfusion and diffusion and multivoxel magnetic resonance spectroscopy

Abstract Background: The primary aim of this study was to evaluate the individual and combined efficacy of magnetic resonance imaging (MRI) parameters, which include MRI perfusion, MRI diffusion-weighted imaging (DWI) and magnetic resonance spectroscopy (MRS) in the grading of gliomas as low grade versus high grade. The pre-operative imaging-based grading of gliomas by multiparametric MRI was compared with the gold standard histopathological studies. Methods: A total of 22 patients referred to the radiology department for multiparametric MRI of the brain with presumptive diagnosis of glioma on computed tomography/MRI were included in the study. Conventional T1, T2 and fluid-attenuated inversion recovery images were obtained followed by perfusion MRI using gadopentetate dimeglumine (Magnevist) administration. This was followed by DWI and MRS. Results: Our statistical analysis demonstrated that a cut-off of apparent diffusion coefficient value of 954.085 (10–6 mm2/Sec) provides a sensitivity and specificity of 87.5% and 85.7%, respectively, in differentiating low-grade gliomas (LGGs) from high-grade gliomas (HGGs). A choline/creatine ratio cut-off value of 2 provides sensitivity and specificity of 100% and 92.9%, respectively, while a cut-off value of 1.45 of choline/N-acetylaspartate ratio provides both sensitivity and specificity of 100% in differentiating LGG from HGG. A cut-off of 1.9 for maximum relative cerebral blood volume (rCBV) value provides both sensitivity and specificity of 100% in differentiating LGGs from HGGs. Conclusions: We concluded that perfusion MRI (rCBV) was the best parameter among perfusion MRI, DWI and MRI spectroscopy in differentiating HGGs from LGGs. Combined multiparametric results showed a diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value of 86.4%, 82.4%, 100%, 100% and 62.5%, respectively, on comparison with gold standard histopathological grading.

Magnetic resonance image-based brain tumour segmentation methods: A systematic review.

BackgroundImage segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development.PurposeTo determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation.MethodsWe conducted a systematic review of 572 brain tumour segmentation studies during 2015–2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score).Statistical testsWe compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour.ResultsWe found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation.ConclusionU-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available.

Hyperintense vessels on imaging account for neurological function independent of lesion volume in acute ischemic stroke

In acute ischemic stroke, reported relationships between lesion metrics and behavior have largely focused on lesion volume and location. However, hypoperfusion has been shown to correlate with deficits in the acute stage. Hypoperfusion is typically identified using perfusion imaging in clinical settings, which requires contrast. Unfortunately, contrast is contraindicated for some individuals. An alternative method has been proposed to identify hypoperfusion using hyperintense vessels on fluid-attenuated inversion recovery (FLAIR) imaging. This study aimed to validate the clinical importance of considering hypoperfusion when accounting for behavior in acute stroke and demonstrate the clinical utility of scoring the presence of hyperintense vessels to quantify it.One hundred and fifty-three participants with acute ischemic stroke completed a battery of commonly-used neurological and behavioral measures. Clinical MRIs were used to determine lesion volume and to score the presence of hyperintense vessels seen on FLAIR images to estimate severity of hypoperfusion in six different vascular regions. National Institutes of Health Stroke Scale (NIHSS) scores, naming accuracy (left hemisphere strokes), and language content produced during picture description were examined in relation to lesion volume, hypoperfusion, and demographic variables using correlational analyses and multivariable linear regression.Results showed that lesion volume and hypoperfusion, in addition to demographic variables, were independently associated with performance on NIHSS, naming, and content production. Specifically, hypoperfusion in the frontal lobe independently correlated with NIHSS scores, while hypoperfusion in parietal areas independently correlated with naming accuracy and content production.These results correspond to previous reports associating hypoperfusion with function, confirming that hypoperfusion is an important consideration—beyond lesion volume—when accounting for behavior in acute ischemic stroke. Quantifying hypoperfusion using FLAIR hyperintense vessels can be an essential clinical tool when other methods of identifying hypoperfusion are unavailable or time prohibitive.

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

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

Diagnostic performance of arterial spin-labeled perfusion imaging and diffusion-weighted imaging in full-term neonatal hypoxic-ischemic encephalopathy.

We evaluated the performance of arterial spin-labeled perfusion imaging and diffusion-weighted imaging in diagnosing full-term neonatal hypoxic-ischemic encephalopathy. Arterial spin-labeled, diffusion-weighted imaging and conventional magnetic resonance imaging (T1-weighted imaging, T2-weighted imaging and T2 fluid-attenuated inversion recovery) were performed in 23 full-term neonates with hypoxic-ischemic encephalopathy group 10 normal neonates (Control group). The cerebral blood flow and the apparent diffusion coefficient were measured in the bilateral basal ganglia, thalamus and frontal white matter. The effect of neonatal age on the CBF and apparent diffusion coefficient values were further investigated after dividing the 23 ischemic encephalopathy cases into three subgroups (1-3 days, 4-7 days, and 8-15 days). The cerebral blood flow values in the thalamus and lenticular nucleus were significantly higher. The apparent diffusion coefficient values in the thalamus, frontal white matter and lenticular nucleus head were significantly lower in the hypoxic-ischemic encephalopathy group than those in the Control group (p < 0.05). There were no significant differences between the ischemic encephalopathy and Control groups in the cerebral blood flow values in the caudate nucleus head and frontal lobe white matter (p > 0.05). The cerebral blood flow and apparent diffusion coefficient values in the thalamus and lenticular nucleus were negatively correlated. Comparison among different age subgroups of hypoxic-ischemic encephalopathyneonates showed that the cerebral blood flow value was higher. In comparison, the apparent diffusion coefficient value was lower in the 1-3 days old neonates than those in the older neonates (p < 0.05). Arterial spin-labeled and diffusion-weighted imaging could reflect the ischemic encephalopathy pathological processes more comprehensively. The cerebral blood flow measurement and apparent diffusion coefficient values in the thalamus and the lenticular nucleus may represent a novel way to diagnose ischemic encephalopathy early.

Capping Protein Regulator and Myosin 1 Linker 3 (CARMIL3) as a Molecular Signature of Ischemic Neurons in the DWI-T2 Mismatch Areas After Stroke.

Ischemic stroke with a mismatch between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) or T2-weighted images indicates onset within 4.5 h, but the pathological substrates in the DWI-T2 mismatch and T2(+) areas remain elusive. In this study, proteomics was used to explore (1) the protein expression profiles in the T2(+), mismatch, and contralateral areas, and (2) the protein with the highest expression in the T2(+) area in the brains of male Sprague-Dawley rats within 4.5 h after middle cerebral artery occlusion (MCAO). The expression of the candidate protein was further validated in (1) rat brain subjected to MCAO, (2) rat primary cortical neuronal culture with oxygen-glucose deprivation (OGD), and (3) infarcted human brain tissues. This study showed that apoptosis was observed in the T2(+) and mismatch regions and necroptosis in the T2(+) region of rat brains after MCAO. We identified capping protein regulator and myosin 1 linker 3 (CARMIL3) as the candidate molecule in the T2(+) and mismatch areas, exclusively in neurons, predominantly in the cytoplasm, and most abundant in the mismatch area. The CARMIL3(+) neurons and neurites in the mismatch and T2(+) areas were larger than those in the control area, and associated with (1) increased expression of sulfonylurea receptor 1 (SUR1), indicating edema, (2) accumulation of p62, indicating impaired autophagy, and (3) increase in 8-hydroxy-2′-deoxyguanosine (8-OHdG), indicating oxidative stress. The increased expression of CARMIL3 was validated in a cell model of cortical neurons after OGD and in infarcted human brain tissues. In conclusion, this study shows that the mismatch and T2(+) areas within 4.5 h after ischemia are characterized by upregulated expression of CARMIL3 in neurons, particularly the mismatch area, which is associated with neuronal edema, impaired autophagy, and oxidative stress, indicating that CARMIL3 serves as a molecular signature of brain ischemia.