Cerebral Hemodynamics and Levosimendan Use in Patients with Cerebral Vasospasm and Subarachnoid Hemorrhage: An Observational Perfusion CT-Based Imaging Study.

BACKGROUND: Delayed cerebral ischemia associated with cerebral vasospasm (CVS) in aneurysmal subarachnoid hemorrhage significantly affects patient prognosis. Levosimendan has emerged as a potential treatment, but clinical data are lacking. The aim of this study is to decipher levosimendan's effect on cerebral hemodynamics by automated quantitative measurements of brain computed tomography perfusion (CTP). METHODS: We conducted a retrospective analysis of a database of a neurosurgical intensive care unit. All patients admitted from January 2018 to July 2022 for aneurysmal subarachnoid hemorrhage and treated with levosimendan for CVS who did not respond to other therapies were included. Quantitative measurements of time to maximum (Tmax), relative cerebral blood volume (rCBV), and relative cerebral blood flow (rCBF) were automatically compared with coregistered CTP before and after levosimendan administration in oligemic regions. RESULTS: Of 21 patients included, CTP analysis could be performed in 16. Levosimendan improved Tmax from 14.4 s (interquartile range [IQR] 9.1-21) before treatment to 7.1 s (IQR 5.5-8.1) after treatment (p < 0.001). rCBV (94% [IQR 79-103] before treatment and 89% [IQR 72-103] after treatment, p = 0.63) and rCBF (85% [IQR 77-90] before treatment and 87% [IQR 73-98] after treatment, p = 0.98) remained stable. The subgroup of six patients who did not develop cerebral infarction attributed to delayed cerebral ischemia showed an approximately 10% increase (rCBV 85% [IQR 79-99] before treatment vs. 95% [IQR 88-112] after treatment, p = 0.21; rCBF 81% [IQR 76-87] before treatment vs. 89% [IQR 84-99] after treatment, p = 0.4). CONCLUSIONS: In refractory CVS, levosimendan use was associated with a significant reduction in Tmax in oligemic regions. However, this value remained at an abnormal level, indicating the presence of a persistent CVS. Further analysis raised the hypothesis that levosimendan causes cerebral vasodilation, but other studies are needed because our design does not allow us to quantify the effect of levosimendan from that of the natural evolution of CVS.

Location-weighted versus Volume-weighted Mismatch at MRI for Response to Mechanical Thrombectomy in Acute Stroke.

Background A target mismatch profile can identify good clinical response to recanalization after acute ischemic stroke, but does not consider region specificities. Purpose To test whether location-weighted infarction core and mismatch, determined from diffusion and perfusion MRI performed in patients with acute stroke, could improve prediction of good clinical response to mechanical thrombectomy compared with a target mismatch profile. Materials and Methods In this secondary analysis, two prospectively collected independent stroke data sets (2012-2015 and 2017-2019) were analyzed. From the brain before stroke (BBS) study data (data set 1), an eloquent map was computed through voxel-wise associations between the infarction core (based on diffusion MRI on days 1-3 following stroke) and National Institutes of Health Stroke Scale (NIHSS) score. The French acute multimodal imaging to select patients for mechanical thrombectomy (FRAME) data (data set 2) consisted of large vessel occlusion-related acute ischemic stroke successfully recanalized. From acute MRI studies (performed on arrival, prior to thrombectomy) in data set 2, target mismatch and eloquent (vs noneloquent) infarction core and mismatch were computed from the intersection of diffusion- and perfusion-detected lesions with the coregistered eloquent map. Associations of these imaging metrics with early neurologic improvement were tested in multivariable regression models, and areas under the receiver operating characteristic curve (AUCs) were compared. Results Data sets 1 and 2 included 321 (median age, 69 years [IQR, 58-80 years]; 207 men) and 173 (median age, 74 years [IQR, 65-82 years]; 90 women) patients, respectively. Eloquent mismatch was positively and independently associated with good clinical response (odds ratio [OR], 1.14; 95% CI: 1.02, 1.27; P = .02) and eloquent infarction core was negatively associated with good response (OR, 0.85; 95% CI: 0.77, 0.95; P = .004), while noneloquent mismatch was not associated with good response (OR, 1.03; 95% CI: 0.98, 1.07; P = .20). Moreover, adding eloquent metrics improved the prediction accuracy (AUC, 0.73; 95% CI: 0.65, 0.81) compared with clinical variables alone (AUC, 0.65; 95% CI: 0.56, 0.73; P = .01) or a target mismatch profile (AUC, 0.67; 95% CI: 0.59, 0.76; P = .03). Conclusion Location-weighted infarction core and mismatch on diffusion and perfusion MRI scans improved the identification of patients with acute stroke who would benefit from mechanical thrombectomy compared with the volume-based target mismatch profile. Clinical trial registration no. NCT03045146 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Nael in this issue.

Visualization of the saccule and utricle with non-contrast-enhanced FLAIR sequences.

OBJECTIVES: 3D-fluid attenuation inversion recovery (FLAIR) collected 4 h after intravenous gadolinium injection can delineate the perilymphatic space (PLS) from the endolymphatic space (ELS) to capture endolymphatic hydrops, the pathological counterpart of Ménière's disease. We aimed to optimize visualization of such inner ear internal anatomy using 3D-FLAIR without injection. METHODS: 3D-FLAIR signal from different fluid compartments such as PLS and ELS was first simulated. Then, twenty-two healthy subjects were scanned at 3.0-T MRI with non-injected 3D-FLAIR using variable T2 preparations (T2Preps) (OFF, 200, 400, and 600 ms) and variable inversion times (TIs) (from 224 to 5000 ms) and different resolutions (1.0 × 1.0 × 1.5, 0.6 × 0.6 × 0.8, and 0.6 × 0.6 × 0.6 mm3). The relative contrast between PLS and ELS and the visibility of the saccule and utricle were assessed. Additionally, non-injected 3D-FLAIR with the optimal setting was tested in a Ménière patient and compared with gadolinium-injected 3D-FLAIR. RESULTS: The PLS and ELS were differentiated when T2Prep was used but not without. The relative contrast was larger with T2Prep at 400 ms than at 200 or 600 ms (0.72 ± 0.22 vs. 0.44 ± 0.11, p = 0.019; and 0.72 ± 0.22 vs. 0.46 ± 0.28, p = 0.034, respectively). The saccule and utricle were best delineated in 87. % cases with T2Prep = 400 and TI = 2100 ms at the highest resolution. Visualization of the saccule and utricle in the optimized non-injected 3D-FLAIR was similar to conventional injected 3D-FLAIR in a patient. CONCLUSIONS: Combining a specific T2Prep and TI in non-injected 3D-FLAIR could separate PLS and ELS and even the saccule and utricle, paving the way toward future application to diagnose Ménière's disease. KEY POINTS: • MRI can capture the internal anatomy of inner ear without injection of contrast media. • Specific parameters consisting of a T2 preparation of 400 ms and an inversion time of 2100 ms must be used to visualize the saccule and utricle on non-injected 3D-FLAIR.

Brain imaging determinants of functional prognosis after severe endocarditis: a multicenter observational study.

OBJECTIVE: We developed a detailed imaging phenotype of the cerebral complications in critically ill patients with infective endocarditis (IE) and determine whether any specific imaging pattern could impact prognostic information. METHODS: One hundred ninety-two patients admitted to the intensive care units of seven tertiary centers with severe, definite left IE and neurological complications were included. All underwent cerebral imaging few days after admission to define the types of lesions, their volumes, and their locations using voxel-based lesion-symptom mapping (VLSM). We employed uni- and multi-variate logistic regression analyses to explore the associations among imaging features and other prognostic variables and the 6-month modified Rankin Scale (mRS) score. RESULTS: Ischemic lesions were the most common lesions (75%; mean volume, 15.3 ± 33 mL) followed by microbleeds (50%; mean number, 4 ± 7.5), subarachnoidal hemorrhages (20%), hemorrhagic strokes (16%; mean volume, 14.6 ± 21 mL), and hemorrhagic transformations (10%; mean volume, 5.6 ± 11 mL). The volume of hemorrhagic transformations, the severity of leukopathy, and the compromises of certain locations on the motor pathway from the VLSM were associated with a poor 6-month mRS score on univariate analyses. However, upon multivariate analyses, no such specific imaging pattern independently predicted the mRS; this was instead influenced principally by age (OR = 1.03 [1.004-1.06]) and cardiac surgery status (OR = 0.06 [0.02-0.16]) in the entire cohort, and by age (OR = 1.04 [1.01-1.08]) and Staphylococcus aureus status (OR = 2.86 [1.19-6.89]) in operated patients. CONCLUSIONS: In a cohort of severely ill IE patients with neurological complications, no specific imaging pattern could be highlighted as a reliable predictor of prognosis.

Neurite imaging reveals microstructural variations in human cerebral cortical gray matter.

We present distinct patterns of neurite distribution in the human cerebral cortex using diffusion magnetic resonance imaging (MRI). We analyzed both high-resolution structural (T1w and T2w images) and diffusion MRI data in 505 subjects from the Human Connectome Project. Neurite distributions were evaluated using the neurite orientation dispersion and density imaging (NODDI) model, optimized for gray matter, and mapped onto the cortical surface using a method weighted towards the cortical mid-thickness to reduce partial volume effects. The estimated neurite density was high in both somatosensory and motor areas, early visual and auditory areas, and middle temporal area (MT), showing a strikingly similar distribution to myelin maps estimated from the T1w/T2w ratio. The estimated neurite orientation dispersion was particularly high in early sensory areas, which are known for dense tangential fibers and are classified as granular cortex by classical anatomists. Spatial gradients of these cortical neurite properties revealed transitions that colocalize with some areal boundaries in a recent multi-modal parcellation of the human cerebral cortex, providing mutually supportive evidence. Our findings indicate that analyzing the cortical gray matter neurite morphology using diffusion MRI and NODDI provides valuable information regarding cortical microstructure that is related to but complementary to myeloarchitecture.

Deciphering the microstructure of hippocampal subfields with in vivo DTI and NODDI: Applications to experimental multiple sclerosis.

The hippocampus contains distinct populations of neurons organized into separate anatomical subfields and layers with differential vulnerability to pathological mechanisms. The ability of in vivo neuroimaging to pinpoint regional vulnerability is especially important for better understanding of hippocampal pathology at the early stage of neurodegenerative disorders and for monitoring future therapeutic strategies. This is the case for instance in multiple sclerosis whose neurodegenerative component can affect the hippocampus from the early stage. We challenged the capacity of two models, i.e. the classical diffusion tensor imaging (DTI) model and the neurite orientation dispersion and density imaging (NODDI) model, to compute quantitative diffusion MRI that could capture microstructural alterations in the individual hippocampal layers of experimental-autoimmune encephalomyelitis (EAE) mice, the animal model of multiple sclerosis. To achieve this, the hippocampal anatomy of a healthy mouse brain was first explored ex vivo with high resolution DTI and NODDI. Then, 18 EAE mice and 18 control mice were explored 20 days after immunization with in vivo diffusion MRI prior to sacrifice for the histological quantification of neurites and glial markers in each hippocampal layer. Fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) maps were computed from the DTI model while the orientation dispersion index (ODI), the neurite density index (NDI) and the volume fraction of isotropic diffusivity (isoVF) maps were computed from the NODDI model. We first showed in control mice that color-coded FA and ODI maps can delineate three main hippocampal layers. The quantification of FA, AD, RD, MD, ODI, NDI and isoVF presented differences within these 3 layers, especially within the molecular layer of the dentate gyrus which displayed a specific signature based on a combination of AD (or MD), ODI and NDI. Then, the comparison between EAE and control mice showed a decrease of AD (p = 0.036) and of MD (p = 0.033) selectively within the molecular layer of EAE mice while NODDI indices did not present any difference between EAE and control mice in any layer. Histological analyses confirmed the differential vulnerability of the molecular layer of EAE mice that exhibited decreased dendritic length and decreased dendritic complexity together with activated microglia. Dendritic length and intersections within the molecular layer were independent contributors to the observed decrease of AD (R2 = 0.37 and R2 = 0.40, p < 0.0001) and MD (R2 = 0.41 and R2 = 0.42, p < 0.0001). We therefore identified that NODDI maps can help to highlight the internal microanatomy of the hippocampus but NODDI still presents limitations in grey matter as it failed to capture selective dendritic alterations occurring at early stages of a neurodegenerative disease such as multiple sclerosis, whereas DTI maps were significantly altered.

Dynamic Evolution of Infarct Volumes at MRI in Ischemic Stroke Due to Large Vessel Occlusion.

BACKGROUND AND OBJECTIVES: The typical infarct volume trajectories in stroke patients, categorized as slow or fast progressors, remain largely unknown. This study aimed to reveal the characteristic spatiotemporal evolutions of infarct volumes caused by large vessel occlusion (LVO) and show that such growth charts help anticipate clinical outcomes. METHODS: We conducted a secondary analysis from prospectively collected databases (FRAME, 2017-2019; ETIS, 2015-2022). We selected acute MRI data from anterior LVO stroke patients with witnessed onset, which were divided into training and independent validation datasets. In the training dataset, using Gaussian mixture analysis, we classified the patients into 3 growth groups based on their rate of infarct growth (diffusion volume/time-to-imaging). Subsequently, we extrapolated pseudo-longitudinal models of infarct growth for each group and generated sequential frequency maps to highlight the spatial distribution of infarct growth. We used these charts to attribute a growth group to the independent patients from the validation dataset. We compared their 3-month modified Rankin scale (mRS) with the predicted values based on a multivariable regression model from the training dataset that used growth group as an independent variable. RESULTS: We included 804 patients (median age 73.0 years [interquartile range 61.2-82.0 years]; 409 men). The training dataset revealed nonsupervised clustering into 11% (74/703) slow, 62% (437/703) intermediate, and 27% (192/703) fast progressors. Infarct volume evolutions were best fitted with a linear (r = 0.809; p < 0.001), cubic (r = 0.471; p < 0.001), and power (r = 0.63; p < 0.001) function for the slow, intermediate, and fast progressors, respectively. Notably, the deep nuclei and insular cortex were rapidly affected in the intermediate and fast groups with further cortical involvement in the fast group. The variable growth group significantly predicted the 3-month mRS (multivariate odds ratio 0.51; 95% CI 0.37-0.72, p < 0.0001) in the training dataset, yielding a mean area under the receiver operating characteristic curve of 0.78 (95% CI 0.66-0.88) in the independent validation dataset. DISCUSSION: We revealed spatiotemporal archetype dynamic evolutions following LVO stroke according to 3 growth phenotypes called slow, intermediate, and fast progressors, providing insight into anticipating clinical outcome. We expect this could help in designing neuroprotective trials aiming at modulating infarct growth before EVT.

Diffusion Tensor Model links to Neurite Orientation Dispersion and Density Imaging at high b-value in Cerebral Cortical Gray Matter.

Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are widely used models to infer microstructural features in the brain from diffusion-weighted MRI. Several studies have recently applied both models to increase sensitivity to biological changes, however, it remains uncertain how these measures are associated. Here we show that cortical distributions of DTI and NODDI are associated depending on the choice of b-value, a factor reflecting strength of diffusion weighting gradient. We analyzed a combination of high, intermediate and low b-value data of multi-shell diffusion-weighted MRI (dMRI) in healthy 456 subjects of the Human Connectome Project using NODDI, DTI and a mathematical conversion from DTI to NODDI. Cortical distributions of DTI and DTI-derived NODDI metrics were remarkably associated with those in NODDI, particularly when applied highly diffusion-weighted data (b-value = 3000 sec/mm2). This was supported by simulation analysis, which revealed that DTI-derived parameters with lower b-value datasets suffered from errors due to heterogeneity of cerebrospinal fluid fraction and partial volume. These findings suggest that high b-value DTI redundantly parallels with NODDI-based cortical neurite measures, but the conventional low b-value DTI is hard to reasonably characterize cortical microarchitecture.

Magnetic resonance angiography with compressed sensing: An evaluation of moyamoya disease.

Compressed sensing (CS) reconstructions of under-sampled measurements generate missing data based on assumptions of image sparsity. Non-contrast time-of-flight MR angiography (TOF-MRA) is a good candidate for CS based acceleration, as MRA images feature bright trees of sparse vessels over a well-suppressed anatomical background signal. A short scan time derived from CS is beneficial for patients of moyamoya disease (MMD) because of the frequency of MR scans. The purpose of this study was to investigate the reliability of TOF-MRA with CS in the evaluation of MMD. Twenty-two patients were examined using TOF-MRA with CS (CS-TOF) and parallel imaging (PI-TOF). The acceleration factors were 3 (CS3) and 5 (CS5) for CS-TOF, and 3 (PI3) for PI-TOF. Two neuroradiologists evaluated the MMD grading according to stenosis/occlusion scores using the modified Houkin's system, and the visibility of moyamoya vessels (MMVs) using a 3-point scale. Concordance was calculated with Cohen's κ. The numbers of MMVs in the basal ganglia were compared using Bland-Altman analysis and Wilcoxon's signed-rank tests. MRA scan times were 4:07, 3:53, and 2:42 for PI3, CS3, and CS5, respectively. CS-reconstruction completed within 10 minutes. MMD grading and MMV visibility scales showed excellent correlation (κ > .966). Although the number of MMVs was significantly higher in CS3 than in PI3 (p < .0001) and CS5 (p < .0001), Bland-Altman analysis showed a good agreement between PI3, CS3, and CS5. Compressed sensing can accelerate TOF-MRA with improved visualization of small collaterals in equivalent time (CS3) or equivalent results in a shorter scan time (CS5).