Aneurysm risk metrics and hemodynamics are associated with greater vessel wall enhancement in intracranial aneurysms

Veeturi, Sricharan S.; Rajabzadeh-Oghaz, Hamidreza; Pintér, Nándor; Waqas, Muhammad; Hasan, David; Snyder, Kenneth V.; Siddiqui, Adnan H.; Tutino, Vincent M.

doi:10.1098/rsos.211119

Cited by 11 publications

(3 citation statements)

References 43 publications

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“…Hadad et al (17) reported 23 cases of saccular aneurysm wall enhancement with low WSS, and confirmed that the hemodynamic environment and enhancement patterns varied in different parts of the aneurysm, with high WSS and high WSSG near the inflow tract of the aneurysm neck, while sites in the body and dome further from the inflow tract were associated with heterogeneous low WSS and enhancement. Sricharan et al (13) detected a weak relationship between normalized WSS and wall enhancement, and no correlation between OSI and wall enhancement, which may have been affected by their statistical method. As the hemodynamic parameters were unevenly distributed, ranked array would have been helpful to reflect the relationship between the parameters (11).…”

Section: Discussionmentioning

confidence: 98%

Wall enhancement predictive of abnormal hemodynamics and ischemia in vertebrobasilar non-saccular aneurysms: a pilot study

Jiang

Lü

et al. 2023

Front. Neurol.

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ObjectiveTo analyze how wall enhancement affects hemodynamics and cerebral ischemic risk factors in vertebrobasilar non-saccular intracranial aneurysms (VBNIAs).Materials and methodsTen consecutive non-saccular aneurysms were collected, including three transitional vertebrobasilar dolichoectasia (TVBD). A wall enhancement model was quantitatively constructed to analyze how wall enhancement interacts with hemodynamics and cerebral ischemic factors.ResultsEnhanced area revealed low wall shear stress (WSS) and wall shear stress gradient (WSSG), with high oscillatory shear index (OSI), relative residence time (RRT), and gradient oscillatory number (GON) while the vortex and slow flow region in fusiform aneurysms are similar to TVBD fusiform aneurysms. With low OSI, high RRT and similar GON in the dilated segment, the enhanced area still manifests low WSS and WSSG in the slow flow area with no vortex. In fusiform aneurysms, wall enhancement was negatively correlated with WSS (except for case 71, all p values < 0.05, r = −0.52 ~ −0.95), while wall enhancement was positively correlated with OSI (except for case 5, all p values < 0.05, r = 0.50 ~ 0.83). For the 10 fusiform aneurysms, wall enhancement is significantly positively correlated with OSI (p = 0.0002, r = 0.75) and slightly negatively correlated with WSS (p = 0.196, r = −0.30) throughout the dataset. Aneurysm length, width, low wall shear stress area (LSA), high OSI, low flow volume (LFV), RRT, and high aneurysm-to-pituitary stalk contrast ratio (CRstalk) area plus proportion may be predictive of cerebral ischemia.ConclusionA wall enhancement quantitative model was established for vertebrobasilar non-saccular aneurysms. Low WSS was negatively correlated with wall enhancement, while high OSI was positively correlated with wall enhancement. Fusiform aneurysm hemodynamics in TVBD are similar to simple fusiform aneurysms. Cerebral ischemia risk appears to be correlated with large size, high OSI, LSA, and RRT, LFV, and wall enhancement.

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Section: Discussionmentioning

confidence: 98%

Wall enhancement predictive of abnormal hemodynamics and ischemia in vertebrobasilar non-saccular aneurysms: a pilot study

Jiang

Lü

et al. 2023

Front. Neurol.

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“…The combined effect of hemodynamic factors has the most negative impact on arterial branches, which in most cases causes the formation of aneurysms at the bifurcations of cerebral vessels (Gao et al, 2022). The causes of aneurysms are high BP, atherosclerosis, acquired traumas, heredity and abnormal blood flow at the junction where the arteries meet (Wedro, 2022;Veeturi et al, 2021).…”

Section: Review and Discussion 1 Etiology Pathogenesis Risk Factors F...mentioning

confidence: 99%

The value of diagnostic procedures for essential hypertension control after an early recovery period of hemorrhagic stroke (literature review)

Tkachyshyn,

Bespalova

2024

USMYJ

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hemorrhagic stroke is the most severe type of stroke, which is more likely to lead to death and severe disability. Although there are many causes and risk factors for hemorrhagic stroke – both congenital and acquired, and mostly their combination – essential hypertension is the most common and leading cause of intracranial hemorrhage. This study aimed to make a comprehensive overview on the value of diagnostic procedures for essential hypertension control after an early recovery period of hemorrhagic stroke. The analyzed data were observed on PubMed and Ukrainian scientific sources concerning essential hypertension course in post-hemorrhagic stroke patients in relation to further prophylaxis in stable phase. All the observed manuscripts were published within the period 2014-2024. The focus of attention was made predominantly on the essential hypertension as the key factor for hemorrhagic stroke development. From the literature review, it could be concluded that the problem of essential hypertension control after hemorrhagic stroke is relevant today and needs a thorough solution to prevent the re-occurrence of cerebrovascular events. For a rational approach to the management of hypertensive patients with hemorrhagic stroke medical history, it would be necessary to perform a set of diagnostic procedures, which would include ultrasound examination of the major cervical arteries, echocardiography, 24-hour ambulatory blood pressure and electrocardiogram monitoring, determination of plasma cholesterol panel indices and blood coagulation tests.

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“…6 Such challenges pose a significant problem for imagebased computational pipelines that are being developed to analyze IA morphology and hemodynamics (via computational fluid dynamics), toward treatment planning, risk assessment, and outcome prediction. 7,8 Intrinsically, the first step in such pipelines involves segmentation of the cerebral vasculature to create a 3D vessel model. Currently, vessel segmentation is performed manually, making it unreasonable for clinical implementation due to the high time/resource cost, subjectivity, and inaccuracy associated with manual input.…”

Section: Patel Et Almentioning

confidence: 99%

Evaluating a 3D deep learning pipeline for cerebral vessel and intracranial aneurysm segmentation from computed tomography angiography–digital subtraction angiography image pairs

Patel

Veeturi

et al. 2023

Neurosurgical Focus

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OBJECTIVE Computed tomography angiography (CTA) is the most widely used imaging modality for intracranial aneurysm (IA) management, yet it remains inferior to digital subtraction angiography (DSA) for IA detection, particularly of small IAs in the cavernous carotid region. The authors evaluated a deep learning pipeline for segmentation of vessels and IAs from CTA using coregistered, segmented DSA images as ground truth. METHODS Using 50 paired CTA-DSA images, the authors trained (n = 27), validated (n = 3), and tested (n = 20) a deep learning model (3D DeepMedic) for cerebrovasculature segmentation from CTA. A landmark-based coregistration algorithm was used for registration and upsampling of CTA images to paired DSA images. Segmented vessels from the DSA were used as the ground truth. Accuracy of the model for vessel segmentation was evaluated using conventional metrics (dice similarity coefficient [DSC]) and vessel segmentation–specific metrics, like connectivity-area-length (CAL). On the test cases (20 IAs), 3 expert raters attempted to detect and segment IAs. For each rater, the authors recorded the rate of IA detection, and for detected IAs, raters segmented and calculated important IA morphology parameters to quantify the differences in IA segmentation by raters to segmentations by DeepMedic. The agreement between raters, DeepMedic, and ground truth was assessed using Krippendorf’s alpha. RESULTS In testing, the DeepMedic model yielded a CAL of 0.971 ± 0.007 and a DSC of 0.868 ± 0.008. The model prediction delineated all IAs and resulted in average error rates of < 10% for all IA morphometrics. Conversely, average IA detection accuracy by the raters was 0.653 (undetected IAs were present to a significantly greater degree on the ICA, likely due to those in the cavernous region, and were significantly smaller). Error rates for IA morphometrics in rater-segmented cases were significantly higher than in DeepMedic-segmented cases, particularly for neck (p = 0.003) and surface area (p = 0.04). For IA morphology, agreement between the raters was acceptable for most metrics, except for the undulation index (α = 0.36) and the nonsphericity index (α = 0.69). Agreement between DeepMedic and ground truth was consistently higher compared with that between expert raters and ground truth. CONCLUSIONS This CTA segmentation network (DeepMedic trained on DSA-segmented vessels) provides a high-fidelity solution for CTA vessel segmentation, particularly for vessels and IAs in the carotid cavernous region.

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Aneurysm risk metrics and hemodynamics are associated with greater vessel wall enhancement in intracranial aneurysms

Cited by 11 publications

References 43 publications

Wall enhancement predictive of abnormal hemodynamics and ischemia in vertebrobasilar non-saccular aneurysms: a pilot study

Wall enhancement predictive of abnormal hemodynamics and ischemia in vertebrobasilar non-saccular aneurysms: a pilot study

The value of diagnostic procedures for essential hypertension control after an early recovery period of hemorrhagic stroke (literature review)

Evaluating a 3D deep learning pipeline for cerebral vessel and intracranial aneurysm segmentation from computed tomography angiography–digital subtraction angiography image pairs

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