Quantitative analysis of flow vortices: differentiation of unruptured and ruptured medium-sized middle cerebral artery aneurysms

Sunderland, Kevin; Wang, Min; Pandey, Aditya S.; Gemmete, Joseph J.; Huang, Qinghai; Goudge, A; Jiang, Jingfeng

doi:10.1007/s00701-020-04616-y

Cited by 12 publications

(11 citation statements)

References 55 publications

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“…The CFD workflow was verified with both phasecontrast magnetic resonance angiography (PC-MRA) [40,41] and ultrasound Doppler [42]. More detailed protocols are previously published and can be found elsewhere [15,19,43,44], and thus, we only provide essential information below.…”

Section: Volumetric Model and Cfd Simulationmentioning

confidence: 99%

“…In a few recent studies [12][13][14][15], when hemodynamic metric, morphological features, and patient health information were combined using machine learning, the prediction performance of AAAs' growth status was encouraging (i.e., the area under the curve [AUC] of receiving operating curve [ROC] > 0.8). In 'imagebased' computational hemodynamics [16][17][18][19], medical imaging data (e.g., computed tomography angiography [CTA], digital subtraction angiography [DSA], etc) from individual patients are used to create 'patient-specific' geometrical models.…”

Section: Introductionmentioning

confidence: 99%

“…A typical computational hemodynamics protocol uses manual segmentation to obtain vascular geometry. All four studies mentioned above used manual segmentation [12][13][14][15]. Nevertheless, manual segmentation is labor-intensive, timeconsuming, and affected by the operator's subjectivity.…”

Section: Introductionmentioning

confidence: 99%

“…(2) We investigate how varying structures of deep-learning image segmentation methods (i.e., two modifications of the classic U-Net model [27]) influence vessel geometry delineation and subsequent CFD results derived from those geometries. Since those simulated hemodynamic metrics are often used for predicting AAAs' growth status and intracranial aneurysm (IA) rupture status [12,15,28], changes in those hemodynamic metrics may have consequences.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study

Lyu,

King,

Rezaeitaleshmahalleh

et al. 2023

Biomed. Phys. Eng. Express

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Computational hemodynamics is increasingly used to quantify hemodynamic characteristics in and around abdominal aortic aneurysms (AAA) in a patient-specific fashion. However, the time-consuming manual annotation hinders the clinical translation of computational hemodynamic analysis. Thus, we investigate the feasibility of using deep-learning-based image segmentation methods to reduce the time required for manual segmentation.Two of the latest deep-learning-based image segmentation methods, ARU-Net and CACU-Net, were used to test the feasibility of automated computer model creation for computational hemodynamic analysis. Morphological features and hemodynamic metrics of 30 computed tomography angiography (CTA) scans were compared between pre-dictions and manual models.The DICE score for both networks was 0.916, and the correlation value was above 0.95, indicating their ability to generate models comparable to human segmentation. The Bland-Altman analysis shows a good agreement between deep learning and manual segmentation results. Compared with manual (computational hemodynamics) model recreation, the time for automated computer model generation was significantly reduced (from ~2hr to ~10min).Automated image segmentation can significantly reduce time expenses on the recreation of patient-specific AAA models. Moreover, our study showed that both CACU-Net and ARU-Net could accomplish AAA segmentation, and CACU-Net outperformed ARU-Net in terms of accuracy and time-saving.

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Section: Volumetric Model and Cfd Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study

Lyu,

King,

Rezaeitaleshmahalleh

et al. 2023

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

show abstract

“…Also, WSS only identifies near‐wall flow characteristics, overlooking bulk flow patterns in the aneurysmal sac. Disturbed flow analysis aims to improve both predictions of areas likely to develop aneurysms and rupture risk assessment by understanding the hemodynamic environment indicative of this pathology (Byrne et al, 2014; Fukazawa et al, 2015; Long et al, 2015; Sunderland & Jiang, 2019; Sunderland et al, 2020; Varble et al, 2017).…”

Section: Applying Knowledge Of Disturbed Flow Toward Clinical Outcomesmentioning

confidence: 99%

Disturbed flow's impact on cellular changes indicative of vascular aneurysm initiation, expansion, and rupture: A pathological and methodological review

Sunderland

Jiang

Zhao

2021

Journal Cellular Physiology

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Aneurysms are malformations within the arterial vasculature brought on by the structural breakdown of the microarchitecture of the vessel wall, with aneurysms posing serious health risks in the event of their rupture. Blood flow within vessels is generally laminar with high, unidirectional wall shear stressors that modulate vascular endothelial cell functionality and regulate vascular smooth muscle cells. However, altered vascular geometry induced by bifurcations, significant curvature, stenosis, or clinical interventions can alter the flow, generating low stressor disturbed flow patterns. Disturbed flow is associated with altered cellular morphology, upregulated expression of proteins modulating inflammation, decreased regulation of vascular permeability, degraded extracellular matrix, and heightened cellular apoptosis. The understanding of the effects disturbed flow has on the cellular cascades which initiate aneurysms and promote their subsequent growth can further elucidate the nature of this complex pathology. This review summarizes the current knowledge about the disturbed flow and its relation to aneurysm pathology, the methods used to investigate these relations, as well as how such knowledge has impacted clinical treatment methodologies. This information can contribute to the understanding of the development, growth, and rupture of aneurysms and help develop novel research and aneurysmal treatment techniques.

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Impact of spatial and temporal stability of flow vortices on vascular endothelial cells

Sunderland

Jia²,

He³

et al. 2022

Biomech Model Mechanobiol

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Quantitative analysis of flow vortices: differentiation of unruptured and ruptured medium-sized middle cerebral artery aneurysms

Cited by 12 publications

References 55 publications

Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study

Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study

Disturbed flow's impact on cellular changes indicative of vascular aneurysm initiation, expansion, and rupture: A pathological and methodological review

Impact of spatial and temporal stability of flow vortices on vascular endothelial cells

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