Search

Purpose Imaging of brain involvement in infective endocarditis can drive the clinical management of this serious condition. MRI is very sensitive, but CT is more readily available. In this retrospective study, we compared the detection rates of CT and MRI. Methods After Ethics Committee approval, we retrospectively reviewed a series of 20 patients (13 males, median age 64 years) who underwent both CT and MRI either before or after cardiac surgery for definite infective endocarditis. Plain CT and MRI were evaluated for acute ischemic lesions, both punctuate and large, intraparenchymal hemorrhages, cerebral microbleeds, subarachnoid hemorrhages, abscesses, microabscesses, and meningitis. Qualitative assessment and McNemar test were performed. The value of contrast-enhanced scans (MRI, n = 14; CT, n = 9) and cognitive status were also assessed. Results A total of 166 lesions were identified on either technique: 137 (83%) on MRI only, 4 (2%) on CT only, and 25 (15%) on both techniques (p Conclusion While CT rapidly excludes large hemorrhages in patients with infective endocarditis, MRI accurately distinguishes the whole spectrum of brain lesions, including small ischemic lesions, microbleeds, and microabscesses.

Background Although white matter hyperintensities (WMH) volumetric assessment is now customary in research studies, inconsistent WMH measures among homogenous populations may prevent the clinical usability of this biomarker. Purpose To determine whether a point estimate and reference standard for WMH volume in the healthy aging population could be determined. Study Type Systematic review and meta-analysis. Population In all, 9716 adult subjects from 38 studies reporting WMH volume were retrieved following a systematic search on EMBASE. Field Strength/Sequence 1.0T, 1.5T, or 3.0T/fluid-attenuated inversion recovery (FLAIR) and/or proton density/T2-weighted fast spin echo sequences or gradient echo T1-weighted sequences. Assessment After a literature search, sample size, demographics, magnetic field strength, MRI sequences, level of automation in WMH assessment, study population, and WMH volume were extracted. Statistical Tests The pooled WMH volume with 95% confidence interval (CI) was calculated using the random-effect model. The I2 statistic was calculated as a measure of heterogeneity across studies. Meta-regression analysis of WMH volume on age was performed. Results Of the 38 studies analyzed, 17 reported WMH volume as the mean and standard deviation (SD) and were included in the meta-analysis. Mean and SD of age was 66.11 ± 10.92 years (percentage of men 50.45% ± 21.48%). Heterogeneity was very high (I2 = 99%). The pooled WMH volume was 4.70 cm3 (95% CI: 3.88–5.53 cm3). At meta-regression analysis, WMH volume was positively associated with subjects' age (β = 0.358 cm3 per year, P Data Conclusion The lack of standardization in the definition of WMH together with the high technical variability in assessment may explain a large component of the observed heterogeneity. Currently, volumes of WMH in healthy subjects are not comparable between studies and an estimate and reference interval could not be determined. Level of Evidence 1 Technical Efficacy Stage 1

Background: Vascular lesions may be a common finding also in Alzheimer's dementia, but their role on cognitive status is uncertain. Objective: The study aims to investigate their distribution in patients with Alzheimer's, vascular or mixed dementia and detect any distinctive neuroradiological profiles. Methods: Seventy-six subjects received a diagnosis of Alzheimer’s (AD=32), vascular (VD=26) and mixed (MD=18) dementia. Three independent raters assessed the brain images acquired with an optimized 3T MRI protocol (including (3D FLAIR, T1, SWI, and 2D coronal T2 sequences) using semiquantitative scales for vascular lesions (periventricular lesions (PVL), deep white matter lesions (DWML), deep grey matter lesions (DGML), enlarged perivascular spaces (PVS), and microbleeds (MB)) and brain atrophy (medial temporal atrophy (MTA), posterior atrophy (PA), global cortical atrophy- frontal (GCA-F) and Evans’ index). Results: Raters reached a good-to-excellent agreement for all scales (ICC ranging from 0.78-0.96). A greater number of PVL (p Conclusion: The proposed MRI protocol represents a useful advancement in the diagnostic assessment of patients with cognitive impairment by more accurately detecting vascular lesions, mainly microbleeds, without a significant increase in time and resource expenditure. Our findings confirm that white and grey matter lesions predominate in vascular and mixed dementia, whereas deep and juxtacortical microbleeds predominate in mixed dementia, suggesting that cerebral amyloid angiopathy could be the main underlying pathology.

We characterize the associations of total cerebral small vessel disease (SVD) burden with brain structure, trajectories of vascular risk factors, and cognitive functions in mid-to-late life. Participants were 623 community-dwelling adults from the Whitehall II Imaging Sub-study with multi-modal MRI (mean age 69.96 SD=5.18, 79% men). We used linear mixed-effects models to investigate associations of SVD burden with up to 25-year retrospective trajectories of vascular risk and cognitive performance. General linear modelling was used to investigate concurrent associations with grey matter (GM) density and white matter (WM) microstructure, and whether these associations were modified by cognitive status (Montreal Cognitive Assessment, MoCA). Severe SVD burden in older age was associated with higher mean arterial pressure throughout midlife (β=3.36, 95% CI [0.42-6.30]), and faster 25-year cognitive decline in letter fluency (β=-0.07, 95% CI [-0.13–-0.01]), and verbal reasoning (β=-0.05, 95% CI [-0.11–-0.001]). Moreover, SVD burden was related to lower GM volumes in 9.7% of total GM, and widespread WM microstructural decline (FWE-corrected pF3,608=2.14, p=0.007). These findings highlight the importance of managing midlife vascular health to preserve brain structure and cognitive function in old age.

Highlights • Heterogeneous measures of WMHs are used in research and clinical practice. • Location and image intensity should be considered in the assessment of WMHs. • T1-hypointense WMHs were found to be associated with poorer cognitive performance. • Sub-classes of WMHs provide promising results for translation into the clinic., White matter hyperintensities (WMHs) on T2-weighted images are radiological signs of cerebral small vessel disease. As their total volume is variably associated with cognition, a new approach that integrates multiple radiological criteria is warranted. Location may matter, as periventricular WMHs have been shown to be associated with cognitive impairments. WMHs that appear as hypointense in T1-weighted images (T1w) may also indicate the most severe component of WMHs. We developed an automatic method that sub-classifies WMHs into four categories (periventricular/deep and T1w-hypointense/nonT1w-hypointense) using MRI data from 684 community-dwelling older adults from the Whitehall II study. To test if location and intensity information can impact cognition, we derived two general linear models using either overall or subdivided volumes. Results showed that periventricular T1w-hypointense WMHs were significantly associated with poorer performance in the trail making A (p = 0.011), digit symbol (p = 0.028) and digit coding (p = 0.009) tests. We found no association between total WMH volume and cognition. These findings suggest that sub-classifying WMHs according to both location and intensity in T1w reveals specific associations with cognitive performance.

Highlights • We harmonised measures of WMHs across two studies on healthy ageing. • Specific pre-processing strategies can increase comparability across studies. • Modelling of biological differences is crucial to provide calibrated measures., Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise study sample differences contributing to differences in WMH variations across studies. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data.

Purpose The aim of the study is to quantify the susceptibility in deep grey nuclei that are affected by pathological processes related to iron accumulation in patients with Parkinson's disease and primary atypical parkinsonisms such as Progressive Supranuclear Palsy, Multiple System Atrophy and Cortico-Basal Degeneration, in order to assist the differential diagnosis among parkinsonian syndromes. Methods We enrolled 49 patients with Parkinson’s disease and 26 patients with primary atypical parkinsonisms. Automatic segmentation of putamen, globus pallidus, caudate nucleus and thalamus and manual segmentation of red nuclei and substantia nigra were performed, and region of interest-based Quantitative Susceptibility Mapping analysis were performed. Statistical comparisons of the mean susceptibility values in the segmented brain regions were performed among primary atypical parkinsonisms and Parkinson's disease. Results Susceptibility values in red nuclei were increased in Progressive Supranuclear Palsy patients compared to parkinsonian phenotype Multiple System Atrophy (p = 0.004), and Parkinson's disease patients (p = 0.006). Susceptibility in thalamus was decreased in Cortico-Basal Degeneration patients compared to Parkinson's disease (p = 0.006), Multiple System Atrophy with cerebellar phenotype (p = 0.031) and parkinsonian phenotype (p = 0.001) patients, and in Progressive Supranuclear Palsy patients compared to Multiple System Atrophy with parkinsonian phenotype patients (p = 0.012). Conclusions Quantitative Susceptibility Mapping allows the depiction and quantification of different patterns of iron deposition in the deep gray nuclei occurring in primary atypical parkinsonisms and Parkinson's disease and it may help as a non-invasive tool in the differential diagnosis between parkinsonian syndromes.

Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise sample differences contributing to study-level differences in WMH variations. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data.HIGHLIGHTSWe harmonised measures of WMHs across two studies on healthy ageingSpecific pre-processing strategies can increase comparability across studiesModelling of biological differences is crucial to provide calibrated measures

We report the results of a survey conducted among ESR members in November and December 2018, asking for expectations about artificial intelligence (AI) in 5–10 years. Of 24,000 ESR members contacted, 675 (2.8%) completed the survey, 454 males (67%), 555 (82%) working at academic/public hospitals. AI impact was mostly expected (≥ 30% of responders) on breast, oncologic, thoracic, and neuro imaging, mainly involving mammography, computed tomography, and magnetic resonance. Responders foresee AI impact on: job opportunities (375/675, 56%), 218/375 (58%) expecting increase, 157/375 (42%) reduction; reporting workload (504/675, 75%), 256/504 (51%) expecting reduction, 248/504 (49%) increase; radiologist’s profile, becoming more clinical (364/675, 54%) and more subspecialised (283/675, 42%). For 374/675 responders (55%) AI-only reports would be not accepted by patients, for 79/675 (12%) accepted, for 222/675 (33%) it is too early to answer. For 275/675 responders (41%) AI will make the radiologist-patient relation more interactive, for 140/675 (21%) more impersonal, for 259/675 (38%) unchanged. If AI allows time saving, radiologists should interact more with clinicians (437/675, 65%) and/or patients (322/675, 48%). For all responders, involvement in AI-projects is welcome, with different roles: supervision (434/675, 64%), task definition (359/675, 53%), image labelling (197/675, 29%). Of 675 responders, 321 (48%) do not currently use AI, 138 (20%) use AI, 205 (30%) are planning to do it. According to 277/675 responders (41%), radiologists will take responsibility for AI outcome, while 277/675 (41%) suggest shared responsibility with other professionals. To summarise, responders showed a general favourable attitude towards AI.

Less information is available on brain integrity in adults with congenital heart disease than on brain changes in newborns and children with heart defects. Nevertheless, the number of adults with congenital heart disease is increasing rapidly and it has been shown that adults with congenital heart disease develop dementia almost twice as frequently as adults in the general population.In the context of a rapidly growing congenital heart disease population, neuroradiological-oriented investigations of biomarkers distinctive for vascular damage, brain aging, and possible cognitive impairment is a crucial challenge. We provide an overview of the existing literature on neuroimaging studies in adults with congenital heart disease and discuss methodology issues to further investigate this subject. Overall, we aim to raise awareness of the importance of brain health studies in adults with congenital heart disease given the likely increasing impact on social and healthcare systems. Keywords: Brain aging, Cerebral small vessel disease, MRI, Congenital heart disease

The impact of congenital heart disease on brain aging has not been extensively investigated. We evaluated cerebral microbleeds and white matter hyperintensities on brain magnetic resonance imaging in adult patients with tetralogy of Fallot (ToF). Ten ToF patients (6 women, 4 men; aged 21–58 years; New York Heart Association [NYHA] class 1–2) were prospectively enrolled and underwent a T1-weighted, a T2-weighted dark fluid, and a T2*-weighted scans. Ten age- and sex-matched controls were prospectively recruited and subjected to the same acquisition protocol. Cerebral microbleeds (CMBs) were manually counted while white matter hyperintensities (WMHs) were segmented using ITK-Snap. Wilcoxon signed-rank test, Spearman correlation, and Bland-Altman statistics were used. The median (interquartile range [IQR]) age was 45.0 (30.5–49.5) years in ToF patients and 46.0 (30.5–49.8) years in controls. The median (IQR) of the number of CMBs was 6.0 (4.0–7.8) in ToF patients and 0 (0.0–0.0) in controls (p = 0.002). The WMHs burden was 2,506 (1,557–2,900) mm3 for ToF patients and 2,212 (1,860–2,586) mm3 for controls (p = 0.160). Moreover, a positive significant correlation was found between the WMHs burden and the NYHA class (ρ = 0.80, p = 0.005). Inter-operator concordance rate for the presence/ absence of CMBs was 90%; the reproducibility for the WMHs burden was 77%. In conclusion, we found more cerebral microbleeds and a higher WMHs burden in adult ToF patients than in controls. This preliminary comparison supports the hypothesis of an early brain aging in ToF patients. Larger studies are warranted.