Fluid-attenuated Inversion Recovery Magnetic Resonance Imaging Research Articles

A non-invasive AI-based system for precise grading of anosmia in COVID-19 using neuroimaging

COVID-19 (Coronavirus), an acute respiratory disorder, is caused by SARS-CoV-2 (coronavirus severe acute respiratory syndrome). The high prevalence of COVID-19 infection has drawn attention to a frequent illness symptom: olfactory and gustatory dysfunction. The primary purpose of this manuscript is to create a Computer-Assisted Diagnostic (CAD) system to determine whether a COVID-19 patient has normal, mild, or severe anosmia. To achieve this goal, we used fluid-attenuated inversion recovery (FLAIR) Magnetic Resonance Imaging (FLAIR-MRI) and Diffusion Tensor Imaging (DTI) to extract the appearance, morphological, and diffusivity markers from the olfactory nerve. The proposed system begins with the identification of the olfactory nerve, which is performed by a skilled expert or radiologist. It then proceeds to carry out the subsequent primary steps: (i) extract appearance markers (i.e., 1st and 2nd order markers), morphology/shape markers (i.e., spherical harmonics), and diffusivity markers (i.e., Fractional Anisotropy (FA) & Mean Diffusivity (MD)), (ii) apply markers fusion based on the integrated markers, and (iii) determine the decision and corresponding performance metrics based on the most-promising classifier. The current study is unusual in that it ensemble bags the learned and fine-tuned ML classifiers and diagnoses olfactory bulb (OB) anosmia using majority voting. In the 5-fold approach, it achieved an accuracy of 94.1%, a balanced accuracy (BAC) of 92.18%, precision of 91.6%, recall of 90.61%, specificity of 93.75%, F1 score of 89.82%, and Intersection over Union (IoU) of 82.62%. In the 10-fold approach, stacking continued to demonstrate impressive results with an accuracy of 94.43%, BAC of 93.0%, precision of 92.03%, recall of 91.39%, specificity of 94.61%, F1 score of 91.23%, and IoU of 84.56%. In the leave-one-subject-out (LOSO) approach, the model continues to exhibit notable outcomes, achieving an accuracy of 91.6%, BAC of 90.27%, precision of 88.55%, recall of 87.96%, specificity of 92.59%, F1 score of 87.94%, and IoU of 78.69%. These results indicate that stacking and majority voting are crucial components of the CAD system, contributing significantly to the overall performance improvements. The proposed technology can help doctors assess which patients need more intensive clinical care.

Artificial intelligence for volumetric measurement of cerebral white matter hyperintensities on thick-slice fluid-attenuated inversion recovery (FLAIR) magnetic resonance images from multiple centers

We aimed to develop a new artificial intelligence software that can automatically extract and measure the volume of white matter hyperintensities (WMHs) in head magnetic resonance imaging (MRI) using only thick-slice fluid-attenuated inversion recovery (FLAIR) sequences from multiple centers. We enrolled 1092 participants in Japan, comprising the thick-slice Private Dataset. Based on 207 randomly selected participants, neuroradiologists annotated WMHs using predefined guidelines. The annotated images of participants were divided into training (n = 138) and test (n = 69) datasets. The WMH segmentation model comprised a U-Net ensemble and was trained using the Private Dataset. Two other models were trained for validation using either both thin- and thick-slice MRI datasets or the thin-slice dataset alone. The voxel-wise Dice similarity coefficient (DSC) was used as the evaluation metric. The model trained using only thick-slice MRI showed a DSC of 0.820 for the test dataset, which is comparable to the accuracy of human readers. The model trained with the additional thin-slice dataset showed only a slightly improved DSC of 0.822. This automatic WMH segmentation model comprising a U-Net ensemble trained on a thick-slice FLAIR MRI dataset is a promising new method. Despite some limitations, this model may be applicable in clinical practice.

Inferior Frontal Sulcal Hyperintensities on Brain MRI Are Associated with Amyloid Positivity beyond Age-Results from the Multicentre Observational DELCODE Study.

Inferior frontal sulcal hyperintensities (IFSHs) on fluid-attenuated inversion recovery (FLAIR) sequences have been proposed to be indicative of glymphatic dysfunction. Replication studies in large and diverse samples are nonetheless needed to confirm them as an imaging biomarker. We investigated whether IFSHs were tied to Alzheimer's disease (AD) pathology and cognitive performance. We used data from 361 participants along the AD continuum, who were enrolled in the multicentre DELCODE study. The IFSHs were rated visually based on FLAIR magnetic resonance imaging. We performed ordinal regression to examine the relationship between the IFSHs and cerebrospinal fluid-derived amyloid positivity and tau positivity (Aβ42/40 ratio ≤ 0.08; pTau181 ≥ 73.65 pg/mL) and linear regression to examine the relationship between cognitive performance (i.e., Mini-Mental State Examination and global cognitive and domain-specific performance) and the IFSHs. We controlled the models for age, sex, years of education, and history of hypertension. The IFSH scores were higher in those participants with amyloid positivity (OR: 1.95, 95% CI: 1.05-3.59) but not tau positivity (OR: 1.12, 95% CI: 0.57-2.18). The IFSH scores were higher in older participants (OR: 1.05, 95% CI: 1.00-1.10) and lower in males compared to females (OR: 0.44, 95% CI: 0.26-0.76). We did not find sufficient evidence linking the IFSH scores with cognitive performance after correcting for demographics and AD biomarker positivity. IFSHs may reflect the aberrant accumulation of amyloid β beyond age.

In vivo PET of synaptic density as potential diagnostic marker for cognitive disorders: prospective comparison with current imaging markers for neuronal dysfunction and relation to symptomatology - study protocol

Background18F-FDG brain PET is clinically used for differential diagnosis in cognitive dysfunction of unclear etiology and for exclusion of a neurodegenerative cause in patients with cognitive impairment in late-life psychiatric disorders. 18F-FDG PET measures regional glucose metabolism, which represents a combination of neuronal/synaptic activity but also astrocytic activity and neuroinflammation. Recently, imaging of synaptic vesicle protein 2 A (SV2A) has become available and was shown to be a proxy of synaptic density. This prospective study will investigate the use of 18F-SynVesT-1 for imaging SV2A and its discriminative power for differential diagnosis in cognitive disorders in a head-to-head comparison to 18F-FDG PET. In addition, simultaneous PET/MR allows an evaluation of contributing factors and the additional value of advanced MRI imaging to FDG/SV2A PET imaging will be investigated. In this work, the study design and protocol are depicted.MethodsIn this prospective, multimodal imaging study, 110 patients with uncertain diagnosis of cognitive impairment who are referred for 18F-FDG PET brain imaging in their diagnostic work-up in a tertiary memory clinic will be recruited. In addition, 40 healthy volunteers (HV) between 18 and 85 years (M/F) will be included. All study participants will undergo simultaneous 18F-SynVesT-1 PET/MR and an extensive neuropsychological evaluation. Amyloid status will be measured by PET using 18FNAV4694, in HV above 50 years of age. Structural T1-weighted and T2-weighted fluid-attenuated inversion recovery MR images, triple-tagging arterial spin labeling (ASL) and resting-state functional MRI (rs-fMRI) will be obtained. The study has been registered on ClinicalTrials.gov (NCT05384353) and is approved by the local Research Ethics Committee.DiscussionThe main endpoint of the study will be the comparison of the diagnostic accuracy between 18F-SynVesT-1 and 18F-FDG PET in cognitive disorders with uncertain etiology and in exclusion of a neurodegenerative cause in patients with cognitive impairment in late-life psychiatric disorders. The strength of the relationship between cognition and imaging data will be assessed, as well as the potential incremental diagnostic value of including MR volumetry, ASL perfusion and rs-fMRI.

Small vessel cerebrovascular disease is associated with cognition in prospective Alzheimer’s clinical trial participants

BackgroundSecondary prevention clinical trials for Alzheimer’s disease (AD) target amyloid accumulation in asymptomatic, amyloid-positive individuals, but it is unclear to what extent other pathophysiological processes, such as small vessel cerebrovascular disease, account for participant performance on the primary cognitive outcomes in those trials. White matter hyperintensities are areas of increased signal on T2-weighted magnetic resonance imaging (MRI) that reflect small vessel cerebrovascular disease. They are associated with cognitive functioning in older adults and with clinical presentation and course of AD, particularly when distributed in posterior brain regions. The purpose of this study was to examine to what degree regional WMH volume is associated with performance on the primary cognitive outcome measure in the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) study, a secondary prevention trial.MethodsData from 1791 participants (59.5% women, mean age (SD) 71.6 (4.74)) in the A4 study and the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) companion study at the screening visit were used to quantify WMH volumes on T2-weighted fluid-attenuated inversion recovery (FLAIR) MR images. Cognition was assessed with the preclinical Alzheimer cognitive composite (PACC). We tested the association of total and regional WMH volumes with PACC performance, adjusting for age, education, and amyloid positivity status, with general linear models. We also considered interactions between WMH and amyloid positivity status.ResultsIncreased frontal and parietal lobe WMH volume was associated with poorer performance on the PACC. While amyloid positivity was also associated with lower cognitive test scores, WMH volumes did not interact with amyloid positivity status.ConclusionThese results highlight the potential of small vessel cerebrovascular disease to drive AD-related cognitive profiles. Measures of small vessel cerebrovascular disease should be considered when evaluating outcome in trials, both as potential effect modifiers and as a possible target for intervention or prevention.

Cerebrovascular lesion loads and accelerated brain aging: insights into the cognitive spectrum.

White matter hyperintensities (WMHs) and cerebral microbleeds are widespread among aging population and linked with cognitive deficits in mild cognitive impairment (MCI), vascular MCI (V-MCI), and Alzheimer's disease without (AD) or with a vascular component (V-AD). In this study, we aimed to investigate the association between brain age, which reflects global brain health, and cerebrovascular lesion load in the context of pathological aging in diverse forms of clinically-defined neurodegenerative conditions. We computed brain-predicted age difference (brain-PAD: predicted brain age minus chronological age) in the Comprehensive Assessment of Neurodegeneration and Dementia cohort of the Canadian Consortium on Neurodegeneration in Aging including 70 cognitively intact elderly (CIE), 173 MCI, 88 V-MCI, 50 AD, and 47 V-AD using T1-weighted magnetic resonance imaging (MRI) scans. We used a well-established automated methodology that leveraged fluid attenuated inversion recovery MRIs for precise quantification of WMH burden. Additionally, cerebral microbleeds were detected utilizing a validated segmentation tool based on the ResNet50 network, utilizing routine T1-weighted, T2-weighted, and T2* MRI scans. The mean brain-PAD in the CIE cohort was around zero, whereas the four categories showed a significantly higher mean brain-PAD compared to CIE, except MCI group. A notable association trend between brain-PAD and WMH loads was observed in aging and across the spectrum of cognitive impairment due to AD, but not between brain-PAD and microbleed loads. WMHs were associated with faster brain aging and should be considered as a risk factor which imperils brain health in aging and exacerbate brain abnormalities in the context of neurodegeneration of presumed AD origin. Our findings underscore the significance of novel research endeavors aimed at elucidating the etiology, prevention, and treatment of WMH in the area of brain aging.

White matter hyperintensities in behavioral variant frontotemporal dementia and semantic variant of primary progressive aphasia

AbstractBackgroundWhite matter hyperintensities (WMHs) are imaging abnormalities of white matter noted by hyperintense signals on fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI). When present among patients with CVRF (hypertension, diabetes, hypercholesteremia and smoking), WMHs are interpreted as cerebral vascular changes. However, WMHs in older individuals can be a feature of late onset leukodystrophies or varied neurodegenerative diseases such as Alzheimer disease (AD), Parkinson’s disease, and Frontotemporal Lobar Degeneration (FTLD). Additionally, WMHs can be present before symptom onset in neurodegenerative disorders and have been linked to the presence of some mutations, including progranulin (GRN). A clearer understanding of the underling etiology of WMHs in bvFTD and svPPA may improve management and treatment considerations.MethodsIn this cross‐sectional study, we included all participants meeting research diagnostic criteria for bvFTD (n = 152) and svPPA (n = 49) from ongoing studies at the UCSF Memory and Aging Center between the dates of September 2008 and December 2021 in comparison to a group of healthy controls enrolled at the same center with similar imaging parameters (n = 152, 1:1 matching for age and sex). All participants underwent a 3T FLAIR MRI and automated quantification of WMHs was performed. Linear regression analyses were performed to determine if WMHs were more frequent in participants with bvFTD or svPPA in comparison to controls and to explore associations with CVRF.ResultsAfter adjusting for age, sex, apolipoprotein E4 (APO‐E 4) status, and intracranial volume (ICV), both groups demonstrated a higher burden compared to controls (bvFTD (p = 0.012, R2 = 0.146) and svPPA (p = 0.008, R2 = 0.329). Modeling cardiovascular risk factors (CVRFs) as none or at least one, we did not find an association between CVRFs and WMH volume in models adjusting for ICV, ApoE4, and age among those with svPPA and bvFTD combined (p = 0.450).ConclusionIndividuals with bvFTD and svPPA appear to face a greater burden of WMHs compared to age‐ and sex‐matched healthy controls. WMHs in bvFTD and svPPA do not appear to be related CVRF.

Early Detection of hyperemia with Magnetic Resonance Fluid Attenuation Inversion Recovery Imaging after Superficial Temporal Artery to Middle Cerebral Artery Anastomosis.

Cerebral hyperperfusion syndrome (CHS) manifests as a collection of symptoms brought on by heightened focal cerebral blood flow (CBF), afflicting nearly 30% of patients who have undergone superficial temporal artery (STA)-middle cerebral artery (MCA) anastomosis. The aim of this study was to investigate whether the amalgamation of magnetic resonance imaging (MRI) fluid-attenuated inversion recovery (FLAIR) and apparent diffusion coefficient (ADC) imaging via MRI can discern cerebral hyperemia after STA-MCA anastomosis surgery. A retrospective study was performed of patients who underwent STA-MCA anastomosis due to Moyamoya disease or atherosclerotic steno-occlusive disease. A protocol aimed at preventing CHS was instituted, leveraging the use of MRI FLAIR. Patients underwent MRI diffusion with FLAIR imaging 24 hours after STA-MCA anastomosis. A high signal on FLAIR images signified the presence of hyperemia at the bypass site, triggering a protocol of hyperemia care. All patients underwent hemodynamic evaluations, including perfusion MRI, single-photon emission computed tomography (SPECT), and digital subtraction angiography, both before and after the surgery. If a high signal intensity is observed on MRI FLAIR within 24 hours of the surgery, a repeat MRI is performed to confirm the presence of hyperemia. Patients with confirmed hyperemia are managed according to a protocol aimed at preventing further progression. Out of a total of 162 patients, 24 individuals (comprising 16 women and 8 men) exhibited hyperemia on their MRI FLAIR scans following the procedure. SPECT was conducted on 23 patients, and 11 of them yielded positive results. All 24 patients underwent perfusion MRI, but nine of them showed no significant findings. Among the patients, 10 displayed elevations in both CBF and cerebral blood volume (CBV), three only showed elevation in CBF, and two only showed elevation in CBV. Follow-up MRI FLAIR scans conducted 6 months later on these patients revealed complete normalization of the previously observed high signal intensity, with no evidence of ischemic injury. The study determined that the use of MRI FLAIR and ADC mapping is a competent means of early detection of hyperemia after STA-MCA anastomosis surgery. The protocol established can be adopted by other neurosurgical institutions to enhance patient outcomes and mitigate the hazard of permanent cerebral injury caused by cerebral hyperemia.

Deep learning method for magnetic resonance imaging fluid-attenuated inversion recovery image synthesis

Magnetic resonance imaging(MRI) can obtain multi-modal images with different contrast, which provides rich information for clinical diagnosis. However, some contrast images are not scanned or the quality of the acquired images cannot meet the diagnostic requirements due to the difficulty of patient's cooperation or the limitation of scanning conditions. Image synthesis techniques have become a method to compensate for such image deficiencies. In recent years, deep learning has been widely used in the field of MRI synthesis. In this paper, a synthesis network based on multi-modal fusion is proposed, which firstly uses a feature encoder to encode the features of multiple unimodal images separately, and then fuses the features of different modal images through a feature fusion module, and finally generates the target modal image. The similarity measure between the target image and the predicted image in the network is improved by introducing a dynamic weighted combined loss function based on the spatial domain and K-space domain. After experimental validation and quantitative comparison, the multi-modal fusion deep learning network proposed in this paper can effectively synthesize high-quality MRI fluid-attenuated inversion recovery (FLAIR) images. In summary, the method proposed in this paper can reduce MRI scanning time of the patient, as well as solve the clinical problem of missing FLAIR images or image quality that is difficult to meet diagnostic requirements.

Changes in leukoencephalopathy and serum neurofilament after (neo)adjuvant chemotherapy for breast cancer

BackgroundPrevious case studies have provided evidence for chemotherapy-induced leukoencephalopathy in patients with breast cancer. However, prospective research is lacking. Hence, we investigated leukoencephalopathy before and after chemotherapy and its association with a serum neuroaxonal damage marker. MethodsThis prospective cohort study included 40 patients receiving chemotherapy for breast cancer, and two age- and education-matched control groups, recruited between 2018 and 2021 (31–64 years of age). The latter control groups consisted of 39 chemotherapy-naïve patients and 40 healthy women. Fluid-attenuated inversion-recovery magnetic resonance imaging was used for lesion volumetry (total, juxtacortical, periventricular, infratentorial, and deep white matter) and blood serum to measure neurofilament light chain (NfL) levels. Acquisition took place pre-chemotherapy and three months and one-year post-chemotherapy, or at corresponding intervals. Within/between group differences were compared using robust mixed-effects modeling, and associations between total lesion volume and serum-NfL with linear regression. ResultsStronger increases in deep white matter lesion volumes were observed shortly post-chemotherapy, compared with healthy women (ßstandardized=0.09, pFDR<0.001). Increases in total lesion volume could mainly be attributed to enlargement of existing lesions (mean±SD, 0.12±0.16 mL), rather than development of new lesions (0.02±0.02 mL). A stronger increase in serum-NfL concentration was observed shortly post-chemotherapy compared with both control groups (ß>0.70, p<0.004), neither of which showed any changes over time, whereas a decrease was observed compared with healthy women one-year post-chemotherapy (ß=-0.54, p = 0.002). Serum-NfL concentrations were associated with lesion volume one-year post-chemotherapy (or at matched timepoint; ß=0.36, p = 0.010), whereas baseline or short-term post-therapy levels or changes were not. ConclusionThese results underscore the possibility of chemotherapy-induced leukoencephalopathy months post-treatment, as well as the added value of serum-NfL as a prognostic marker for peripheral/central neurotoxicity. Translational relevancePrevious case studies have provided evidence of chemotherapy-induced leukoencephalopathy in patients with breast cancer. However, prospective studies to estimate longitudinal changes are currently missing. In this study, we used longitudinal fluid-attenuated inversion-recovery magnetic resonance imaging to assess white matter lesion volumes in patients treated for non-metastatic breast cancer and healthy women. Our findings demonstrate that chemotherapy-treated patients exhibit stronger increases in lesion volumes compared with healthy women, specifically in deep white matter, at three months post-chemotherapy. Increases could mainly be attributed to enlargement of existing lesions, rather than development of new lesions. Last, serum concentrations of neurofilament light chain, a neuroaxonal damage marker, increased shortly after chemotherapy and long-term post-chemotherapy levels were associated with lesion volumes. These findings highlight the potential of this non-invasive serum marker as a prognostic marker for peripheral and/or central neurotoxicity. Implementation in clinical practice could aid in therapeutic decisions, assessing disease activity, or monitoring treatment response.

Segmenting white matter hyperintensities in brain magnetic resonance images using convolution neural networks

White matter hyperintensities (WMHs) are found on magnetic resonance (MR) images of older individuals and are associated with many neurodegenerative disorders, although the exactrole of WMHs in Alzheimer’s disease and other dementias remains an open area of research. Fluid-attenuated inversion recovery (FLAIR) MR imaging sequences show WMHs with good image contrast. Manual segmentation of WMHs on FLAIR images is the widely accepted “gold standard”, however, this step is often time-consuming and has a high inter-rater variability. The absence of an automated, robust and accurate approach to segment WMHs remains a processing bottleneck. We explored convolutional neural networks (CNNs) for performing semantic segmentation of WMHs in FLAIR images. Two sets of experiments were conducted: (1) Variations of U-shaped CNNs (U-Nets) were evaluated in 186 individuals, specifically, four architectures (VGG16, VGG19, ResNet152 and EfficientNetB0) having three dimensionalities (2D, 2.5D and 3D). (2) New data from 60 individuals were added to test the generalizability of U-Net, LinkNet and Feature-Pyramid Network (FPN) variants. The first experiment showed that the 2.5D implementation with VGG16 or VGG19 was the most suitable configuration when segmenting WMH (F-measure > 95% and intersection-over-union > 90%). The second experiment confirmed generalizability of these variants when using unprocessed FLAIR images.

Two cases of unilateral cortical fluid‐attenuated inversion recovery‐hyperintense lesions in anti‐myelin oligodendrocyte glycoprotein‐associated encephalitis with seizures (FLAMES)

AbstractBackgroundMyelin oligodendrocyte glycoprotein (MOG) antibody‐associated disease (MOGAD) is a rare inflammatory disease of the central nervous system. Unilateral cortical fluid‐attenuated inversion recovery‐hyperintense lesions in anti‐MOG‐associated encephalitis with seizures (FLAMES) has recently been proposed as a subcategory of MOGAD. FLAMES is characterized by fluid‐attenuated inversion recovery (FLAIR) imaging showing hyperintense cortical lesions in MOG‐associated encephalitis with seizures.Case PresentationWe herein report two cases of unilateral cortical FLAMES. The first case was a 29‐year‐old woman who developed headaches, fever, convulsions and right hemiparesis. Brain FLAIR magnetic resonance imaging (MRI) showed hyperintense cortical lesions on the left side. The second case was a 37‐year‐old women who developed headaches and fever. Brain FLAIR MRI showed hyperintense cortical lesions on the left side. Both cases were positive for anti‐MOG antibodies in cerebrospinal fluid and serum, and were diagnosed with unilateral cortical FLAMES in MOGAD. Both patients were treated with intravenous methylprednisolone followed by oral corticosteroids, which improved MRI findings and clinical symptoms.ConclusionsBoth patients were diagnosed with MOGAD due to characteristic unilateral cortical encephalitis on brain FLAIR MRI. Unilateral cortical FLAMES is an important clue for the clinical diagnosis of MOGAD.

Prevalence of Venous Infarction in Patients With Cerebral Venous Thrombosis: Baseline Diffusion-Weighted MRI and Follow-Up MRI.

A venous pattern of infarction on neuroimaging is used as a clue to undiagnosed cerebral venous thrombosis (CVT); prevention of venous infarction is a goal of CVT management; and venous infarction is a factor used for clinical prognostication. Despite widespread use of the term venous infarct, the prevalence of true venous infarction is unclear. Our primary aim was to determine the prevalence of venous infarction in patients with CVT. We also measured the prevalence of diffusion abnormality without infarction, vasogenic edema, and intracranial hemorrhage. Single-center, retrospective cohort study using a registry of 110 consecutive patients admitted to hospital with cerebral venous thrombosis between 2004 and 2014. Inclusion criteria were brain magnetic resonance imaging (MRI) and contrast-enhanced venography at presentation, and repeat brain MRI ≥1 month later. Exclusion criteria were dural arteriovenous fistula, arteriovenous malformation, cavernous sinus thrombosis, or previous neurosurgical procedure. Main outcome was proportion of patients with venous infarction (irreversible ischemic injury) diagnosed using diffusion-weighted MRI at presentation, confirmed using T2-weighted fluid-attenuated inversion recovery MRI ≥1 month later, and reported with 95% CI using the Wilson score interval method. We also report the prevalence of transient diffusion MRI abnormality without infarction, vasogenic edema, and intracranial hemorrhage. Seventy-three patients met the inclusion criteria, and after exclusions, the final study population was 59 patients with median age 41 years (interquartile range, 32-57). Venous infarction occurred in 12% (7/59 [95% CI, 6%-23%]) of patients, and final infarct volume was >1 mL in only 5.1% (3/59) of patients. An additional 8% (5/59 [95% CI, 4%-18%]) of patients had a transient diffusion MRI abnormality without infarction. Prevalence of cerebral vasogenic edema and intracranial hemorrhage were 66% (39/59 [95% CI, 53%-77%]) and 54% (32/59 [95% CI, 41%-66%]), respectively. In patients with CVT, venous infarction is uncommon and venous infarcts are typically very small. Vasogenic edema and hemorrhage are more common consequences of CVT.

Young versus older subject diffusion magnetic resonance imaging data for virtual white matter lesion tractography.

White matter hyperintensity (WMH) lesions on T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) and changes in adjacent normal-appearing white matter can disrupt computerized tract reconstruction and result in inaccurate measures of structural brain connectivity. The virtual lesion approach provides an alternative strategy for estimating structural connectivity changes due to WMH. To assess the impact of using young versus older subject diffusion MRI data for virtual lesion tractography, we leveraged recently available diffusion MRI data from the Human Connectome Project (HCP) Lifespan database. Neuroimaging data from 50 healthy young (39.2 ± 1.6 years) and 46 healthy older (74.2 ± 2.5 years) subjects were obtained from the publicly available HCP-Aging database. Three WMH masks with low, moderate, and high lesion burdens were extracted from the WMH lesion frequency map of locally acquired FLAIR MRI data. Deterministic tractography was conducted to extract streamlines in 21 WM bundles with and without the WMH masks as regions of avoidance in both young and older cohorts. For intact tractography without virtual lesion masks, 7 out of 21 WM pathways showed a significantly lower number of streamlines in older subjects compared to young subjects. A decrease in streamline count with higher native lesion burden was found in corpus callosum, corticostriatal tract, and fornix pathways. Comparable percentages of affected streamlines were obtained in young and older groups with virtual lesion tractography using the three WMH lesion masks of increasing severity. We conclude that using normative diffusion MRI data from young subjects for virtual lesion tractography of WMH is, in most cases, preferable to using age-matched normative data.

White matter hyperintensities had a correlation with the cerebral perfusion level, but no correlation with the severity of large vessel stenosis in the anterior circulation.

The contribution of large vessel stenosis to the development of white matter hyperintensities (WMHs) has not been fully elucidated. This study aims to explore the correlation between ipsilateral white matter hyperintensities (WMHs) and the severity of large vessel stenosis in the anterior circulation and cerebral perfusion level, as well as analyze the factors influencing WMHs. A cross-sectional study of 150 patients with unilateral anterior circulation large vessel stenosis of ≥50% was conducted. The severity of ipsilateral WMHs was assessed by Fazekas scale on T2-weighted image and/or fluid-attenuated inversion recovery MR imaging, vascular stenosis severity was evaluated on computed tomography angiography images, and the level of cerebral perfusion was rated according to a staging system for abnormal cerebral perfusion based on CTP results. The relationships between the stenosis severity, cerebral perfusion level and ipsilateral WMHs severity were analyzed. A multivariate logistic regression analysis was performed to determine the factors independently influencing WMHs. Among 150 patients (mean age, 63.12±10.55 years), there was a statistically significant positive correlation between cerebral perfusion level and the severity of DWMHs and PWMHs (Gamma=0.561, p<.001; Gamma=0.600, p<.001), and a positive correlation between cerebral perfusion level and the severity of vascular stenosis (Gamma=0.495, p<.001).While, there was no statistically significant correlation between the severity of vascular stenosis and the severity of DWMHs and PWMHs (Gamma=0.188, p=.08; Gamma=0.196, p=.06). The multivariate logistic regression analysis results demonstrated that age (OR=1.047, 95% CI 1.003-1.093; p=.035), stroke/TIA history (OR=2.880, 95% CI 1.154-7.190; p=.023) and stage II of cerebral perfusion (OR=2.880, 95% CI 1.154-7.190; p=.023) were independent influencing factors on ipsilateral DWMHs. Age (OR=1.051, 95% CI 1.009-1.094; p=.018), and stage II of cerebral perfusion (OR=12.871, 95% CI 3.576-46.322; p<.001) were factors independently influencing ipsilateral PWMHs. White matter hyperintensities may be attributed to cerebral hypoperfusion secondary to vascular stenosis but not directly to the severity of stenosis in the large vessels of anterior circulation. Moreover, longitudinal studies with sequential imaging exams may further reveal the impact of cerebral perfusion secondary to vascular stenosis on the development and progression of WMHs.

Neuroinflammation and amyloid deposition in the progression of mixed Alzheimer and vascular dementia

BackgroundAlzheimer’s disease (AD) and vascular contributions to cognitive impairment and dementia (VCID) pathologies coexist in patients with cognitive impairment. Abnormal amyloid beta (Aβ) deposition is the hallmark pathologic biomarker for AD. Neuroinflammation may be a pathophysiological mechanism in both AD and VCID. In this study, we aimed to understand the role of neuroinflammation and Aβ deposition in white matter hyperintensities (WMH) progression and cognitive decline over a decade in patients with mixed AD and VCID pathologies. MethodsTwenty-four elderly participants (median [interquartile range] age 78 [64.8, 83] years old, 14 female) were recruited from the Knight Alzheimer Disease Research Center. 11C-PK11195 standard uptake value ratio (SUVR) and 11C-PiB mean cortical binding potential (MCBP) were used to evaluate neuroinflammation and Aβ deposition in-vivo, respectively. Fluid-attenuated inversion recovery MR images were acquired to obtain baseline WMH volume and its progression over 11.5 years. Composite cognitive scores (global, processing speed and memory) were computed at baseline and follow-up over 7.5 years. Multiple linear regression models evaluated the association between PET biomarkers (11C-PK11195 SUVR and 11C-PiB MCBP) and baseline WMH volume and cognitive function. Moreover, linear mixed-effects models evaluated whether PET biomarkers predicted greater WMH progression or cognitive decline over a decade. ResultsFifteen participants (62.5%) had mixed AD (positive PiB) and VCID (at least one vascular risk factor) pathologies. Elevated 11C-PK11195 SUVR, but not 11C-PiB MCBP, was associated with greater baseline WMH volume and predicted greater WMH progression. Elevated 11C-PiB MCBP was associated with baseline memory and global cognition. Elevated 11C-PK11195 SUVR and elevated 11C-PiB MCBP independently predicted greater global cognition and processing speed declines. No association was found between 11C-PK11195 SUVR and 11C-PiB MCBP. ConclusionsNeuroinflammation and Aβ deposition may represent two distinct pathophysiological pathways, and both independently contributed to the progression of cognitive impairment in mixed AD and VCID pathologies. Neuroinflammation, but not Aβ deposition, contributed to WMH volume and progression.

A case of progressive multifocal leukoencephalopathy associated with daratumumab, bortezomib, and dexamethasone for multiple myeloma

An 83-year-old man presented with visual disturbance and right hemiparalysis, one month after daratumumab, bortezomib, and dexamethasone administration for multiple myeloma (MM). Blood screens revealed a CD4+ T-lymphocyte count of 132/μl. Diffusion weighted and fluid-attenuated inversion-recovery MR imaging showed high intensity signals in the both occipital lobes and left precentral area. The patient had no history of human immunodeficiency virus infection. Cerebrospinal fluid (CSF) JC virus (JCV) was positive (83copies/ml), as indicated by PCR. The patient was diagnosed with progressive multifocal leukoencephalopathy (PML). MM treatment was discontinued, and mefloquine and mirtazapine therapy was started. However, the CSF JCV-DNA PCR count did not improve (111copies/ml) after 30 days from starting mefloquine and mirtazapine therapy. The patient died six months after symptom onset. Conclusively, patients with decreased CD4+ T lymphocyte counts following DBd therapy for MM, the possibility of PML should be considered.

The LUMIERE dataset: Longitudinal Glioblastoma MRI with expert RANO evaluation

Publicly available Glioblastoma (GBM) datasets predominantly include pre-operative Magnetic Resonance Imaging (MRI) or contain few follow-up images for each patient. Access to fully longitudinal datasets is critical to advance the refinement of treatment response assessment. We release a single-center longitudinal GBM MRI dataset with expert ratings of selected follow-up studies according to the response assessment in neuro-oncology criteria (RANO). The expert rating includes details about the rationale of the ratings. For a subset of patients, we provide pathology information regarding methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter status and isocitrate dehydrogenase 1 (IDH1), as well as the overall survival time. The data includes T1-weighted pre- and post-contrast, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) MRI. Segmentations from state-of-the-art automated segmentation tools, as well as radiomic features, complement the data. Possible applications of this dataset are radiomics research, the development and validation of automated segmentation methods, and studies on response assessment. This collection includes MRI data of 91 GBM patients with a total of 638 study dates and 2487 images.

Association between Serum Amyloid A Level and White Matter Hyperintensity Burden: a Cross-Sectional Analysis in Patients with Acute Ischemic Stroke.

This work aimed to determine the potential link between white matter hyperintensity (WMH) burden and serum amyloid A (SAA) level in patients with acute ischemic stroke. Consecutive patients with acute large artery atherosclerosis (LAA) stroke between April 2021 and May 2022 were included. WMH volumes (periventricular, deep, and total) were measured using the Fazekas score and a semiautomated volumetric analysis on fluid-attenuated inversion recovery-magnetic resonance imaging. The burdens of WMH were scored to assess the dose-dependent association between SAA and WMH volume. Multivariate regression and a two-piecewise linear regression model were used to evaluate whether SAA levels are an independent predictor of WMH, and to discover the threshold effect or saturation effect of SAA levels with respect to WMH volume. The mean age of patients was 63.2 ± 11.5years, with 65.9% men. The median SAA level was 3.93mg/L and the total WMH volume of 6.86cm3. In the multivariable analysis, SAA remained an independent predictor of total WMH volume [β = 0.82, 95% confidence interval (CI) = 0.49-1.07, p < 0.001], periventricular WMH volume (adjusted β = 0.76, 95% CI = 0.46-1.07, p < 0.001), and deep WMH volume (adjusted β = 0.26, 95% CI = 0.06-0.45, p = 0.011) after controlling for confounders. Furthermore, SAA levels were associated with periventricular Fazekas score, deep Fazekas score, and Fazekas grades. Threshold effect and saturation effect analyses demonstrated a nonlinear relationship between SAA levels and periventricular white matter hyperintensity (PVWMH) volumes, with SAA levels (2.12-19.89mg/L) having significant dose-dependent relationships with periventricular WMH volumes (adjusted β = 1.98, 95% CI = 1.12-2.84, p < 0.001). SAA level ranging from 2.12 to 19.89mg/L is dose-dependently associated with periventricular WMH development. These findings point the way forward for future research into the pathophysiology of WMH.

Online hard example mining vs. fixed oversampling strategy for segmentation of new multiple sclerosis lesions from longitudinal FLAIR MRI

Detecting new lesions is a key aspect of the radiological follow-up of patients with Multiple Sclerosis (MS), leading to eventual changes in their therapeutics. This paper presents our contribution to the MSSEG-2 MICCAI 2021 challenge. The challenge is focused on the segmentation of new MS lesions using two consecutive Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). In other words, considering longitudinal data composed of two time points as input, the aim is to segment the lesional areas, which are present only in the follow-up scan and not in the baseline. The backbone of our segmentation method is a 3D UNet applied patch-wise to the images, and in which, to take into account both time points, we simply concatenate the baseline and follow-up images along the channel axis before passing them to the 3D UNet. Our key methodological contribution is the use of online hard example mining to address the challenge of class imbalance. Indeed, there are very few voxels belonging to new lesions which makes training deep-learning models difficult. Instead of using handcrafted priors like brain masks or multi-stage methods, we experiment with a novel modification to online hard example mining (OHEM), where we use an exponential moving average (i.e., its weights are updated with momentum) of the 3D UNet to mine hard examples. Using a moving average instead of the raw model should allow smoothing of its predictions and allow it to give more consistent feedback for OHEM.