Rupture risk in abdominal aortic aneurysms: A realistic assessment of the explicit GPU approach

Štrbac, Vukašin; Pierce, David M.; Rodríguez-Vila, B.; Sloten, Jos Vander; Famaey, Nele

doi:10.1016/j.jbiomech.2017.02.019

Cited by 6 publications

(8 citation statements)

References 59 publications

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“…The rupture occurred at the site with high near-wall flow speeds, high WSSGs, and high local pressure, which indicated that the dominant flow impinged on the wall of the rupture site. This correlates with the prediction of most FE studies 38,39 ; and (c) Group 3 patients with ILTs thicker than 3 mm. The rupture occurred at the border of the dominant flow channel and recirculation zone with high TAWSSs, WSSGs, and low ECAPs, except for patient P 35 .…”

Section: Discussionsupporting

confidence: 83%

Role of intraluminal thrombus in abdominal aortic aneurysm ruptures: A hemodynamic point of view

et al. 2019

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Purpose Intraluminal thrombus (ILTs) are found in most abdominal aortic aneurysms (AAAs) of clinically relevant size; however, the role of ILTs in AAA ruptures remains unclear. This study investigated the role of the presence and thickness of ILTs in AAA ruptures by analyzing the hemodynamic environment in ruptured AAAs (RAAAs). Methods Three‐dimensional reconstructions from computed tomography scans were performed, and 13 RAAA cases were categorized into a no‐ILT group, a thin‐layered ILT group (thickness < 3 mm), and a thick‐layered ILT group. The hemodynamic features of the RAAAs were assessed using computational fluid dynamics simulation. Results The thin‐ and thick‐layered ILT groups showed significant differences in aneurysm diameters (P < 0.05). The three types of AAAs ruptured at different flow regions, with different hemodynamic features: (a) the no‐ILT AAAs ruptured at regions of flow recirculation where velocity and wall shear stresses (WSSs) were close to zero; (b) the thin‐layered ILT AAAs ruptured at sites at which the dominant flow impinged the wall; and (c) the thick‐layered ILT AAAs ruptured at the border of the dominant flow channel and recirculation zone where the flow velocity and pressure changed dramatically. Conclusions Hemodynamic characteristics influence the rupture mechanisms of particular AAAs differently on the basis of the presence and thickness of ILTs. Recirculation flows and low WSSs may have negative effects by inducing local rupture or positive effects by promoting the formation of thin‐layered ILTs. However, eccentrically located thick‐layered ILTs may increase the rupture risk of small AAAs because of their location in the sac lumen, which results in chaotic flow patterns and rapid increases in flow resistance.

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Section: Discussionsupporting

confidence: 83%

Role of intraluminal thrombus in abdominal aortic aneurysm ruptures: A hemodynamic point of view

et al. 2019

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“…We note that researchers have been developing GPU‐based computational methods to accelerate FEA for biomedical applications . However, regardless using CPU or GPU for FEA, numerical stability and convergence issues still exist .…”

Section: Discussionmentioning

confidence: 99%

“…We note that researchers have been developing GPU-based computational methods to accelerate FEA for biomedical applications. [48][49][50][51][52][53] However, regardless using CPU or GPU for FEA, numerical stability and convergence issues still exist. 49 Thus, for a single object study, a GPU-based FEA approach may be the best choice.…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach as a surrogate of finite element analysis–based inverse method to estimate the zero‐pressure geometry of human thoracic aorta

Liang

Liu

Martin

et al. 2018

Numer Methods Biomed Eng

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Advances in structural finite element analysis (FEA) and medical imaging have made it possible to investigate the in vivo biomechanics of human organs such as blood vessels, for which organ geometries at the zero-pressure level need to be recovered. Although FEA-based inverse methods are available for zero-pressure geometry estimation, these methods typically require iterative computation, which are time-consuming and may be not suitable for time-sensitive clinical applications. In this study, by using machine learning (ML) techniques, we developed an ML model to estimate the zero-pressure geometry of human thoracic aorta given 2 pressurized geometries of the same patient at 2 different blood pressure levels. For the ML model development, a FEA-based method was used to generate a dataset of aorta geometries of 3125 virtual patients. The ML model, which was trained and tested on the dataset, is capable of recovering zero-pressure geometries consistent with those generated by the FEA-based method. Thus, this study demonstrates the feasibility and great potential of using ML techniques as a fast surrogate of FEA-based inverse methods to recover zero-pressure geometries of human organs.

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“…Prepoznavanje rizičnih grupa populacije i njihovo praćenje predstavlja esencijalni deo uspešne terapije (3). Različite publikovane studije naglasile su značaj faktora rizika kako za razvoj aneurizmatske bolesti aorte tako i progresiju bolesti (4,5). U prvom redu ističe se najčešće višedecenijsko pušenje, zatim muški pol, hronična bolest pluća, životna dob i hipertenzija.…”

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Abdominal aortic aneurysm: Risk factors for diametric progression in non-operated patients

Petrović¹,

Končar²

2018

Medicinski podmladak

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Introduction: Abdominal aortic aneurysm (AAA) is a disease of modern age, which occurs mostly in elderly people, after the age of 65 years. Epidemiological studies have shown that the development of AAA is correlated with certain risk factors including smoking, as well as presence of comorbidities like hypertension and hyperlipidemia. In our country, the diagnosis of AAA is based on ultrasound examination. Aim: To analyze the speed and factors influencing the progression of AAA diameter. Material and methods: Our study included 52 patients who were not eligible for surgical treatment. Patients were examined successively and risk factors were observed as well as comorbidities were obtained and life habits of those patients. Results: Average age of patients was 71.5 years. 46 patients (86,5 %) were smokers. Average diameter of AAA was 42.5mm. The correlation between the speed of AAA diameter progression and comorbidities wasn't found. There wasn't found correlation between difference between time interval of next follow-up visit and AAA diameter (χ 2 = 0.4164, p = 0.549). Average growth of AAA between first and second measurement was 2.79 mm (X = 2.5, S.D. 3.1). There was correlation between initial diametric size and growth of aneurysm R =-0.329 (p = 0.017). Conclusion: In our study we didn't find the correlation between comorbidities and AAA growth. Concerning small number of patients, studies with this topic that analyze larger number of patients are recommended. Patients with smaller AAA showed slower progression in diameter, which require their less frequent follow-up visit.

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Rupture risk in abdominal aortic aneurysms: A realistic assessment of the explicit GPU approach

Cited by 6 publications

References 59 publications

Role of intraluminal thrombus in abdominal aortic aneurysm ruptures: A hemodynamic point of view

Role of intraluminal thrombus in abdominal aortic aneurysm ruptures: A hemodynamic point of view

A machine learning approach as a surrogate of finite element analysis–based inverse method to estimate the zero‐pressure geometry of human thoracic aorta

Abdominal aortic aneurysm: Risk factors for diametric progression in non-operated patients

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