Semiautomated 3D mapping of aneurysmal wall enhancement with 7T-MRI

Imaging of Intracranial Aneurysms: A Review of Standard and Advanced Imaging Techniques

The treatment of intracranial aneurysms is dictated by its risk of rupture in the future. Several clinical and radiological risk factors for aneurysm rupture have been described and incorporated into prediction models. Despite the recent technological advancements in aneurysm imaging, linear length and visible irregularity with a bleb are the only radiological measure used in clinical prediction models. The purpose of this article is to summarize both the standard imaging techniques, including their limitations, and the advanced techniques being used experimentally to image aneurysms. It is expected that as our understanding of advanced techniques improves, and their ability to predict clinical events is demonstrated, they become an increasingly routine part of aneurysm assessment. It is important that neurovascular specialists understand the spectrum of imaging techniques available.

Hemodynamic analysis of thrombosed intracranial aneurysms: a comparative correlation study

A small fraction of intracranial aneurysms (IA) contains intrasaccular thrombosis (IST). This study explores the hemodynamic causes of IST formation in IAs. We performed computational hemodynamic analysis in 26 IAs: 13 thrombosed and 13 non-thrombosed. The IAs in the two cohorts were matching in size and anatomical location. The computational hemodynamic analysis used "patient-specific" IA geometries derived from 3D magnetic resonance imaging. A comprehensive hemodynamic analysis was conducted using commonly used hemodynamic metrics (e.g., wall shear stress [WSS] and its derivative), flow vortex analysis, and velocity informatics. We observed that more flow eddies and endothelial cell activation potential (ECAP) are present in thrombosed IAs. The flow eddies in thrombosed IAs change size and location over a cardiac cycle more significantly than those in non-thrombosed IAs. These two factors, coupled with more flow stagnation and positive WSS divergence in the thrombosed IAs, promoted thrombus formation in the thrombosed IA cohort.

Beyond Size: Advanced MRI Breakthroughs in Predicting Intracranial Aneurysm Rupture Risk

Intracranial aneurysms (IAs) are present in approximately 3-5% of the global population and carry a significant risk of rupture, leading to subarachnoid haemorrhage (SAH), a condition associated with high morbidity and mortality. Even with developments in neuroimaging, fundamental clinical difficulty remains in precisely predicting which aneurysms will rupture. Although aneurysm size, location, and patient history define traditional risk assessment, these elements by themselves have insufficient predictive ability. Key elements in rupture risk are aneurysm wall biology, haemodynamics, and inflammation; recent developments in magnetic resonance imaging (MRI) including high-resolution vascular wall imaging (VWI), 4D flow MRI, and quantitative susceptibility mapping (QSM) provide fresh insights on these aspects. The present evidence on these sophisticated MRI techniques is synthesised in this review of the literature, which also analyses their clinical relevance and addresses newly developed computational methods like machine learning for better risk stratification. I underline important studies showing the diagnostic and prognostic worth of MRI-based biomarkers, discuss present constraints, and suggest future lines of research. Personalised aneurysm care could benefit from the combination of multiparametric MRI data with artificial intelligence (AI), hence improving patient outcomes.

There Is Poor Agreement between the Subjective and Quantitative Adjudication of Aneurysm Wall Enhancement

BACKGROUND AND PURPOSE

The determination of aneurysm wall enhancement (AWE) by human readers on visual inspection alone is subjective and prone to error. A 3D method for quantifying the signal intensity (SI) of the aneurysm enables objective determination of AWE. Interreader agreement and agreement between subjective and objective determination of AWE were assessed in this study.

MATERIALS AND METHODS

Patients with saccular intracranial aneurysms (IAs) were imaged with high-resolution MRI. In the subjective assessment, 2 internal adjudicators visually determined AWE if the degree of enhancement was equal to or higher than that of the pituitary stalk. An experienced internal neuroradiologist resolved disagreements. This internal adjudication was compared with an external adjudication to assess interrater agreement among centers. In the objective assessment, the distribution of SI across the aneurysm wall after normalizing the SI to the corpus callosum was determined with an in-house code. The normalized mean SI on postcontrast T1 MRI was defined as 3D-circumferential AWE (3D-CAWE). If the 3D-CAWE value was higher than 1, an IA was defined as objectively "enhancing." Interrater agreement was analyzed with κ coefficients. Intertechnique agreement between the subjective and objective assessments was performed using κ statistics. Univariate regressions were used to identify which morphologic characteristics influenced subjective adjudication of enhancement.

RESULTS

A total of 113 IAs were analyzed. The agreement of the internal assessment was moderate (κ = 0.63), 49.5% of IAs (56) were classified as "enhancing;" and 50.5% (57) as "nonenhancing" after consensus. Interrater agreement between internal and external adjudication was weak (κ = 0.52) for the presence of AWE. There was no agreement between the subjective assessment of AWE and objective 3D-CAWE (κ = 0.16, P = .02). Subjective assessment was less likely to reliably adjudicate enhancement when assessing multiple aneurysms (OR, 0.4; 95% CI, 0.16-0.97; P = .04) and IAs larger than >7 mm (OR, 0.22; 95% CI, 0.09-0.55; P = .002) despite being objectively nonenhancing.

CONCLUSIONS

Subjective adjudication of AWE has poor interrater agreement, and no agreement with an objective 3D method of determining AWE. It is also less likely than objective quantification to identify enhancement in aneurysms of >7 mm or when multiple aneurysms are present. Objective 3D quantification, such as the technique used in this study, should, therefore, be considered when assessing AWE, especially in patients with multiple aneurysms and aneurysms of >7 mm in size.

Radiomics-Based Predictive Nomogram for Assessing the Risk of Intracranial Aneurysms

Aneurysm wall enhancement (AWE) has the potential to be used as an imaging biomarker for the risk stratification of intracranial aneurysms (IAs). Radiomics provides a refined approach to quantify and further characterize AWE's textural features. This study examines the performance of AWE quantification combined with clinical information in detecting symptomatic IAs. Ninety patients harboring 104 IAs (29 symptomatic and 75 asymptomatic) underwent high-resolution magnetic resonance imaging (HR-MRI). The assessment of AWE was performed using two different methods: 3D-AWE mapping and composite radiomics-based score (RadScore). The dataset was split into training and testing subsets. The testing set was used to build two different nomograms using each modality of AWE assessment combined with patients' clinical information and aneurysm morphological data. Finally, each nomogram was evaluated on an independent testing set. A total of 22 radiomic features were significantly different between symptomatic and asymptomatic IAs. The 3D-AWE mapping nomogram achieved an area under the curve (AUC) of 0.77 (63% accuracy, 78% sensitivity, and 58% specificity). The RadScore nomogram exhibited a better performance, achieving an AUC of 0.83 (77% accuracy, 89% sensitivity, and 73% specificity). The comprehensive analysis of IAs with the quantification of AWE data through radiomic analysis, patient clinical information, and morphological aneurysm metrics achieves a high accuracy in detecting symptomatic IA status.

Statins may Decrease Aneurysm wall Enhancement of Unruptured Fusiform Intracranial Aneurysms: A high-resolution 3T MRI Study

Inflammation plays an integral role in the formation, growth, and progression to rupture of unruptured intracranial aneurysms. Aneurysm wall enhancement (AWE) in high-resolution magnetic resonance imaging (HR-MRI) has emerged as a surrogate biomarker of vessel wall inflammation and unruptured intracranial aneurysm instability. We investigated the correlation between anti-inflammatory drug use and three-dimensional AWE of fusiform intracranial aneurysms (FIAs). We retrospectively analyzed consecutive patients with FIAs in our database who underwent 3T HR-MRI at three Chinese centers. FIAs were classified as fusiform-type, dolichoectatic-type, or transitional-type. AWE was objectively defined using the aneurysm-to-pituitary stalk contrast ratio in three-dimensional space by determining the contrast ratio of the average signal intensity in the aneurysmal wall and pituitary stalk on post-contrast T1-weighted images. Data on aneurysm size, morphology, and location, as well as patient demographics and comorbidities, were collected. Univariate and multivariate logistic regression analyses were performed to determine factors independently associated with AWE of FIAs on HR-MRI. In total, 127 FIAs were included. In multivariate analysis, statin use (β = -0.236, P = 0.007) was the only independent factor significantly associated with decreased AWE. In the analysis of three FIA subtypes, the fusiform and transitional types were significantly associated with statin use (rs = -0.230, P = 0.035; and rs = -0.551, P = 0.010; respectively). It establishes an incidental correlation between the use of statins daily for ≥ 6 months and decreased AWE of FIAs. The findings also indicate that the pathophysiology may differ among the three FIA subtypes.

Intensity Score of Vessel Wall Contrast Enhancement in MRI Allows Prediction of Disease Progression in Moyamoya Angiopathy

BACKGROUND AND OBJECTIVES

The underlying pathophysiological cause of moyamoya angiopathy (MMA) is still unclear. High-resolution vessel wall imaging has become a useful tool. The aim was to study vessel wall contrast-enhancement (VW-CE) as an imaging marker to predict disease progression in MMA.

METHODS

Patients with MMA, who had undergone serial contrast-enhanced high-resolution MRI with concomitant and follow-up digital subtraction angiography, were analyzed retrospectively. VW-CE was semiquantified by measurement of the signal intensity of the vessel wall in in contrast-enhanced high-resolution MRI. A comparative quotient with the contrast-intensity of the pituitary stalk was calculated and graded accordingly from grade 1 to 5. VW-CE status was correlated with disease status, stroke, cerebrovascular reactivity in CO2-triggered blood-oxygen level-dependent MRI, angiographic disease progression, revascularization surgery, and follow-up imaging.

RESULTS

Forty eight patients met the inclusion criteria. N = 56 MRI and digital subtraction angiography time-intervals were evaluated for 12 vessel sections per hemisphere each (N = 1344). N = 38 (79%) patients showed VW-CE and N = 10 (21%) did not. VW-CE was only observed in the terminal internal carotid artery and the proximal circle of Willis (N = 96/1344). Notably, patients with VW-CE significantly more often presented with acute infarction in the concomitant MRI. The incidence of angiographically proven disease progression was significantly associated with the incidence of VW-CE, and time to disease progression was earlier in higher grades of VW-CE compared with lower grades.

CONCLUSION

VW-CE is a semiquantifiable marker for disease activity in patients with MMA and associated with disease progression and increased risk of stroke. VW-CE analysis can be routinely performed in patients with MMA to estimate the risk for disease progression and stroke.

Radiomics-Based Predictive Nomogram for Assessing the Risk of Intracranial Aneurysms

Background

Aneurysm wall enhancement (AWE) has the potential to be used as an imaging biomarker for the risk stratification of intracranial aneurysms (IAs). Radiomics provides a refined approach to quantify and further characterize AWE's textural features. This study examines the performance of AWE quantification combined with clinical information in detecting symptomatic IAs.

Methods

Ninety patients harboring 104 IAs (29 symptomatic and 75 asymptomatic) underwent high-resolution magnetic resonance imaging (HR-MRI). The assessment of AWE was performed using two different methods: 3D-AWE mapping and composite radiomics-based score (RadScore). The dataset was split into training and testing subsets. The testing set was used to build two different nomograms using each modality of AWE assessment combined with patients' demographic information and aneurysm morphological data. Finally, each nomogram was evaluated on an independent testing set.

Results

A total of 22 radiomic features were significantly different between symptomatic and asymptomatic IAs. The 3D-AWE Mapping nomogram achieved an area under the curve (AUC) of 0.77 (63% accuracy, 78% sensitivity and 58% specificity). The RadScore nomogram exhibited a better performance, achieving an AUC of 0.83 (77% accuracy, 89% sensitivity and 73% specificity).

Conclusions

Combining AWE quantification through radiomic analysis with patient demographic data in a clinical nomogram achieved high accuracy in detecting symptomatic IAs.

MR Imaging of the Cerebral Aneurysmal Wall for Assessment of Rupture Risk

The evaluation of unruptured intracranial aneurysms requires a comprehensive and multifaceted approach. The comprehensive analysis of aneurysm wall enhancement through high-resolution MRI, in tandem with advanced processing techniques like finite element analysis, quantitative susceptibility mapping, and computational fluid dynamics, has begun to unveil insights into the intricate biology of aneurysms. This enhanced understanding of the etiology, progression, and eventual rupture of aneurysms holds the potential to be used as a tool to triage patients to intervention versus observation. Emerging tools such as radiomics and machine learning are poised to contribute significantly to this evolving landscape of diagnostic refinement.

3D aneurysm wall enhancement is associated with symptomatic presentation

BACKGROUND

Aneurysm wall enhancement (AWE) is a potential surrogate biomarker for aneurysm instability. Previous studies have assessed AWE using 2D multiplanar methods, most of which were conducted qualitatively.

OBJECTIVE

To use a new quantitative tool to analyze a large cohort of saccular aneurysms with 3D-AWE maps METHODS: Saccular aneurysms were imaged prospectively with 3T high resolution MRI. 3D-AWE maps of symptomatic (defined as ruptured or presentation with sentinel headache/cranial nerve neuropathy) and asymptomatic aneurysms were created by extending orthogonal probes from the aneurysm lumen into the wall. Three metrics were used to characterize enhancement: 3D circumferential AWE (3D-CAWE), aneurysm-specific contrast uptake (SAWE), and focal AWE (FAWE). Aneurysms with a circumferential AWE higher than the corpus callosum (3D-CAWE ≥1) were classified as 3D-CAWE+. Symptomatic presentation was analyzed with univariate and multivariate logistic models. Aneurysm size, size ratio, aspect ratio, irregular morphology, and PHASES and ELAPSS scores were compared with the new AWE metrics. Bleb and microhemorrhage analyses were also performed.

RESULTS

Ninety-three aneurysms were analyzed. 3D-CAWE, SAWE, and FAWE were associated with symptomatic status (OR=1.34, 1.25, and 1.08, respectively). A multivariate model including aneurysm size, 3D-CAWE+, age, female gender, and FAWE detected symptomatic status with 80% specificity and 90% sensitivity (area under the curve=0.914, =0.967). FAWE was also associated with irregular morphology and high-risk location (p=0.043 and p=0.001, respectively). In general, blebs enhanced 56% more than the aneurysm body. Areas of microhemorrhage co-localized with areas of increased SAWE (p=0.047).

CONCLUSIONS

3D-AWE mapping provides a new set of metrics that could potentially improve the identification of symptomatic aneurysms.

Three-dimensional aneurysm wall enhancement in fusiform intracranial aneurysms is associated with aneurysmal symptoms

Background and purpose

Aneurysm wall enhancement (AWE) in high-resolution magnetic resonance imaging (HR-MRI) is a potential biomarker for evaluating unstable aneurysms. Fusiform intracranial aneurysms (FIAs) frequently have a complex and curved structure. We aimed to develop a new three-dimensional (3D) aneurysmal wall enhancement (AWE) characterization method to enable comprehensive FIA evaluation and to investigate the ability of 3D-AWE to predict symptomatic FIA.

Methods

We prospectively recruited patients with unruptured FIAs and received 3 T HR-MRI imaging from September 2017 to January 2019. 3D models of aneurysms and parent arteries were generated. Boundaries of the FIA were determined using 3D vessel diameter measurements. D_max was the greatest diameter in the cross-section, while L_max was the length of the centerline of the aneurysm. Signal intensity of the FIA was normalized to the pituitary stalk and then mapped onto the 3D model, then the average enhancement (3D-AWE_avg), maximum enhancement (3D-AWE_max), enhancement area (AWE_area), and enhancement ratio (AWE_ratio) were calculated as AWE indicators, and the surface area of the entire aneurysm (A_area) was also calculated. Areas with high AWE were defined as those with a value >0.9 times the signal intensity of the pituitary stalk. Multivariable logistic regression analyses were performed to determine independent predictors of aneurysm-related symptoms. FIA subtypes were defined as fusiform, dolichoectasia, and transitional. Differences between the three FIA subtypes were also examined.

Results

Forty-seven patients with 47 FIAs were included. Mean patient age was 55 ± 12.62 years and 74.5% were male. Twenty-nine patients (38.3%) were symptomatic. After adjusting for baseline differences in age, hypertension, L_max, and FIA subtype, the multivariate logistics regression models showed that 3D-AWE_avg (odds ratio [OR], 4.029; p = 0.019), 3D-AWE_max (OR, 3.437; p = 0.022), AWE_area (OR, 1.019; p = 0.008), and AWE_ratio (OR, 2.490; p = 0.045) were independent predictors of aneurysm-related symptoms. D_max and A_area were larger and 3D-AWE_avg, 3D-AWE_max, AWE_area, and AWE_ratio were higher with the transitional subtype than the other two subtypes.

Conclusion

The new 3D AWE method, which enables the use of numerous new metrics, can predict symptomatic FIAs. Different 3D-AWE between the three FIA subtypes may be helpful in understanding the pathophysiology of FIAs.

Comprehensive morphomechanical analysis of brain aneurysms

OBJECTIVE

Brain aneurysms comprise different compartments that undergo unique biological processes. A detailed multimodal analysis incorporating 3D aneurysm wall enhancement (AWE), computational fluid dynamics (CFD), and finite element analysis (FEA) data can provide insights into the aneurysm wall biology.

METHODS

Unruptured aneurysms were prospectively imaged with 7 T high-resolution MRI (HR-MRI). 3D AWE color maps of the entire aneurysm wall were generated and co-registered with contour plots of morphomechanical parameters derived from CFD and FEA. A multimodal analysis of the entire aneurysm was performed using 3D circumferential AWE (3D-CAWE), wall tension (WT), time-averaged wall shear stress (TAWSS), wall shear stress gradient (WSSG), and oscillatory shear index (OSI). A detailed compartmental analysis of each aneurysm's dome, bleb, and neck was also performed.

RESULTS

Twenty-six aneurysms were analyzed. 3D-CAWE + aneurysms had higher WT (p = 0.03) and higher TAWSS (p = 0.045) than 3D-CAWE- aneurysms. WT, TAWSS, and WSSG were lower in areas of focal AWE in the aneurysm dome compared to the neck (p = 0.009, p = 0.049, and p = 0.040, respectively), whereas OSI was higher in areas of focal AWE compared to the neck (p = 0.020). When compared to areas of no AWE of the aneurysm sac (AWE = 0.92 vs. 0.49, p = 0.001), blebs exhibited lower WT (1.6 vs. 2.45, p = 0.010), lower TAWSS (2.6 vs. 6.34), lower OSI (0.0007 vs. 0.0010), and lower WSSG (2900 vs. 5306). Fusiform aneurysms had a higher 3D-CAWE and WT than saccular aneurysms (p = 0.046 and p = 0.003, respectively).

CONCLUSIONS

Areas of focal high AWE in the sac and blebs are associated with low wall tension, low wall shear stress, and low flow conditions (TAWSS and WSSG). Conversely, the neck had average AWE, high wall tension, high wall shear stress, and high flow conditions. The aneurysm dome and the aneurysm neck have different morphomechanical environments, with increased mechanical load at the neck.

3D Enhancement Color Maps in the Characterization of Intracranial Atherosclerotic Plaques

BACKGROUND AND PURPOSE

High-resolution MR imaging allows the identification of culprit symptomatic plaques after the administration of gadolinium. Current high-resolution MR imaging methods are limited by 2D multiplanar views and manual sampling of ROIs. We analyzed a new 3D method to objectively quantify gadolinium plaque enhancement.

MATERIALS AND METHODS

Patients with stroke due to intracranial atherosclerotic disease underwent 7T high-resolution MR imaging. 3D segmentations of the plaque and its parent vessel were generated. Signal intensity probes were automatically extended from the lumen into the plaque and the vessel wall to generate 3D enhancement color maps. Plaque gadolinium (Gd) uptake was quantified from 3D color maps as gadolinium uptake = (µPlaque T1 + Gd -µPlaque T1/SDPlaque T1). Additional metrics of enhancement such as enhancement ratio, variance, and plaque-versus-parent vessel enhancement were also calculated. Conventional 2D measures of enhancement were collected for comparison.

RESULTS

Thirty-six culprit and 44 nonculprit plaques from 36 patients were analyzed. Culprit plaques had higher gadolinium uptake than nonculprit plaques (P < .001). Gadolinium uptake was the most accurate metric for identifying culprit plaques (OR, 3.9; 95% CI 2.1-8.3). Gadolinium uptake was more sensitive (86% versus 70%) and specific (71% versus 68%) in identifying culprit plaques than conventional 2D measurements. A multivariate model, including gadolinium uptake and plaque burden, identified culprit plaques with an 83% sensitivity and 86% specificity.

CONCLUSIONS

The new 3D color map method of plaque-enhancement analysis is more accurate for identifying culprit plaques than conventional 2D methods. This new method generates a new set of metrics that could potentially be used to assess disease progression.

Comparisons between cross-section and long-axis-section in the quantification of aneurysmal wall enhancement of fusiform intracranial aneurysms in identifying aneurysmal symptoms

Background

To investigate the quantification of aneurysmal wall enhancement (AWE) in fusiform intracranial aneurysms (FIAs) and to compare AWE parameters based on different sections of FIAs in identifying aneurysm symptoms.

Methods

Consecutive patients were prospectively recruited from February 2017 to November 2019. Aneurysm-related symptoms were defined as sentinel headache and oculomotor nerve palsy. All patients underwent high resolution magnetic resonance imaging (HR-MRI) protocol, including both pre and post-contrast imaging. CR_stalk (signal intensity of FIAs' wall divided by pituitary infundibulum) was evaluated both in the cross-section (CR_{stalk−cross}) and the long-axis section (CR_stalk−long) of FIAs. Aneurysm characteristics include the maximal diameter of the cross-section (D_max), the maximal length of the long-axis section (L_max), location, type, and mural thrombus. The performance of parameters for differentiating symptomatic and asymptomatic FIAs was obtained and compared by a receiver operating characteristic (ROC) curve.

Results

Forty-three FIAs were found in 43 patients. Eighteen (41.9%) patients who presented with aneurysmal symptoms were classified in the symptomatic group. In univariate analysis, male sex (P = 0.133), age (P = 0.013), FIAs type (P = 0.167), mural thrombus (P = 0.130), L_max (P = 0.066), CR_{stalk−cross} (P = 0.027), and CR_stalk−long (P = 0.055) tended to be associated with aneurysmal symptoms. In the cross-section model of multivariate analysis, male (P = 0.038), age (P = 0.018), and CR_{stalk−cross} (P = 0.048) were independently associated with aneurysmal symptoms. In the long-axis section model of multivariate analysis, male (P = 0.040), age (P = 0.010), CR_stalk−long (P = 0.046), and L_max (P = 0.019) were independently associated with aneurysmal symptoms. In the combination model of multivariate analysis, male (P = 0.027), age (P = 0.011), CR_{stalk−cross} (P = 0.030), and L_max (P = 0.020) were independently associated with aneurysmal symptoms. CR_{stalk−cross} has the highest accuracy in predicting aneurysmal symptoms (AUC = 0.701). The combination of CR_{stalk−cross} and L_max exhibited the highest performance in discriminating symptomatic from asymptomatic FIAs (AUC = 0.780).

Conclusion

Aneurysmal wall enhancement is associated with symptomatic FIAs. CR_{stalk−cross} and L_max were independent risk factors for aneurysmal symptoms. The combination of these two factors may improve the predictive performance of aneurysmal symptoms and may also help to stratify the instability of FIAs in future studies.

Topographical Analysis of Aneurysm Wall Enhancement With 3-Dimensional Mapping

BACKGROUND

Aneurysm wall enhancement has been identified as a potential biomarker for aneurysm instability. Enhancement has been determined by different approaches on 2D multiplanar views. This study describes a new method to quantify enhancement through 3D heatmaps and histograms.

METHODS

A custom algorithm was developed using orthogonal probes extending from the aneurysm lumen into the wall to create 3D heatmaps and histograms of wall enhancement on 7T-MRI. Three quantitative metrics for general, specific, and focal wall enhancement were generated from the histograms.

RESULTS

Thirty-two aneurysms were analyzed and classified based on 3D heatmaps and histograms. Larger aneurysms were more enhancing (Spearman's r=0.472, p=0.006), and had more heterogeneous enhancement (Spearman's r=0.557, p<0.001) than smaller aneurysms. Patterns of enhancement differed between saccular, fusiform, and thrombosed aneurysms. Fusiform aneurysms were larger (p=0.015) and had more heterogenous enhancement compared to saccular aneurysms. Fusiform aneurysms had more areas of focal enhancement (p<0.001) and right skewed histograms (p=0.003).

CONCLUSIONS

The 3D analysis of aneurysm wall enhancement provides topographic data of the entire aneurysm wall. New metrics developed based on this method showed that large and fusiform aneurysms have heterogenous enhancement.

Aneurysmal wall enhancement and hemodynamics: pixel-level correlation between spatial distribution

BACKGROUND

Inflammation and hemodynamics are interrelated risk factors for intracranial aneurysm rupture. This study aimed to identify the relationship between these risk factors from an individual-patient perspective using biomarkers of aneurysm wall enhancement (AWE) derived from high-resolution magnetic resonance imaging (HR-MRI) and hemodynamic parameters by four-dimensional flow MRI (4D-flow MRI).

METHODS

A total of 29 patients with 29 unruptured intracranial aneurysms larger than 4 mm were included in this prospective cross-sectional study. A total of 24 aneurysms had AWE and 5 did not have AWE. A three-dimensional (3D) vessel model of each individual aneurysm was generated with 3D time-of-flight magnetic resonance angiography (3D TOF-MRA). Quantification of AWE was sampled with HR-MRI. Time-averaged wall shear stress (WSS) and oscillatory shear index (OSI) were calculated from the 4D-flow MRI. The correlation between spatial distribution of AWE and hemodynamic parameters measured at pixel-level was evaluated for each aneurysm.

RESULTS

In aneurysms with AWE, the spatial distribution of WSS was negatively correlated with AWE in 100% (24/24) of aneurysms, though 2 had an absolute value of the correlation coefficient <0.1. The OSI was positively correlated with AWE in 91.7% (22/24) of aneurysms; the other 2 aneurysms showed a negative correlation with AWE. In aneurysms with no AWE, there was no correlation between WSS (100%, 5/5), OSI (80%, 4/5), and wall inflammation.

CONCLUSIONS

The spatial distribution of WSS was negatively correlated with AWE in aneurysms with AWE, and OSI was positively correlated with AWE in most aneurysms with AWE. While aneurysms that did not contain AWE showed no correlation between hemodynamics and wall inflammation.

Updates on aneurysmal subarachnoid hemorrhage: is there anything really new?

Aneurysmal subarachnoid hemorrhage (aSAH) is a severe disease, with systemic involvement and complex diagnosis and treatment. Since the current guidelines were published by the AHA/ASA, Neurocritical Care Society and the European Stroke Organization in 2012-2013,there has been an evolution in the comprehension of SAH-associated brain injury and its multiple underlying mechanisms. As a result, several clinical and translational trials were developed or are underway. Objective: The aim of this article is to review some updates in the diagnosis and treatment of neurological complications of SAH. Methods: A review of PubMed (May, 2010 to February, 2022) was performed. Data was summarized. Results: Content of five meta-analyses, nine review articles and 23 new clinical trials, including pilots, were summarized. Conclusions:Advances in the comprehension of pathophysiology and improvements in critical care have been reflected in the reduction of mortality in SAH. However, despite the number of publications, the only treatments shown to be effective in adequate, well-controlled clinical trials are nimodipine and repair of the ruptured aneurysm. Thus, doubts about the optimal management of SAH still persist.