Aneurysm Wall Stress and Tendency to Rupture Are Features of Physical Wall Properties: An Experimental Study

Flora, Harpaul S.; Talei-Faz, Bijan; Ansdell, Leslie E.; Chaloner, Eddie; Sweeny, Aaron; Grass, Anthony J.; Adiseshiah, M

doi:10.1583/1545-1550(2002)009<0665:awsatt>2.0.co;2

Cited by 18 publications

(12 citation statements)

References 23 publications

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“…A recent experimental study using a compliant polymer as an arterial representation showed that the value of PWS in the presence of systemic pressurization or endoleak reached only a very small percentage of the material failure strength. 14 In this AAA model with a reinforced wall, the peak stress was significantly greater, and wall strength contributed more significantly to wall stress than increasing pressurization within the AAA sac. The authors proposed that AAA wall mechanics could theoretically contribute more significantly to PWS than pressure variations within the system.…”

Section: Role Of Aortic Wall Strengthmentioning

confidence: 85%

Computational Evaluation of Aortic Aneurysm Rupture Risk: What Have We Learned So Far?

Georgakarakos

Ioannou

Papaharilaou

et al. 2011

Journal of Endovascular Therapy

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In current clinical practice, aneurysm diameter is one of the primary criteria used to decide when to treat a patient with an abdominal aortic aneurysm (AAA). It has been shown that simple association of aneurysm diameter with the probability of rupture is not sufficient, and other parameters may also play a role in causing or predisposing to AAA rupture. Peak wall stress (PWS), intraluminal thrombus (ILT), and AAA wall mechanics are the factors most implicated with rupture risk and have been studied by computational risk evaluation techniques. The objective of this review is to examine these factors that have been found to influence AAA rupture. The prediction rate of rupture among computational models depends on the level of model complexity and the predictive value of the biomechanical parameters used to assess risk, such as PWS, distribution of ILT, wall strength, and the site of rupture. There is a need for simpler geometric analogues, including geometric parameters (e.g., lumen tortuosity and neck length and angulation) that correlate well with PWS, conjugated with clinical risk factors for constructing rupture risk predictive models. Such models should be supported by novel imaging techniques to provide the required patient-specific data and validated through large, prospective clinical trials.

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Section: Role Of Aortic Wall Strengthmentioning

confidence: 85%

Computational Evaluation of Aortic Aneurysm Rupture Risk: What Have We Learned So Far?

Georgakarakos

Ioannou

Papaharilaou

et al. 2011

Journal of Endovascular Therapy

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“…This questions the theory that rupture simply occurs at the point of maximum mechanical stretch where the diameter is greatest [58]. Diameter was first used in 1966 to predict AAA at high risk of rupture and has since been modified in the light of experience and more accurate imaging [59].…”

Section: Finite Element Analysismentioning

confidence: 99%

“…Diameter was first used in 1966 to predict AAA at high risk of rupture and has since been modified in the light of experience and more accurate imaging [59]. The simple observation that not all AAAs rupture at a specific diameter indicates that other patient-specific and aneurysm-specific variables may have an effect, for example: obstructive lung disease, smoking, hypertension, a widened pulse pressure, the ratio of the aneurysm diameter to the diameter in the adjacent normal aorta, and aortic wall tension [58]. Presently, it is not possible to obtain exact peak wall stress or accurate local wall strength measurements.…”

Section: Finite Element Analysismentioning

confidence: 99%

Can We Predict Abdominal Aortic Aneurysm (AAA) Progression and Rupture by Non-Invasive Imaging?—A Systematic Review

Abbas¹,

Attia²,

Smith³

et al. 2011

IJCM

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“…Because of our fast-aging society, this is a problem calling for immediate attention. Previous studies have reported cardiovascular diseases to be closely related to blood flow [1][2][3][4][5] , and much research has been performed from this point of view, most of which has been carried out by experimental measurement [6][7][8][9][10] (in vitro and in vivo) or, recently, by computational fluid dynamics (CFD). Even with substantial improvement in measurement technology, it is still difficult to accurately measure the detailed structure of 3D blood flow, including the vessel wall shear stress and the wall pressure, important factors of cardiovascular diseases, by using existing measurement techniques, such as MRI, CT, X-ray, and ultrasonic measurement.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Color Doppler Velocity Measurement for Ultrasonic-Measurement-Integrated Simulation of Blood Flow

Liu

Hayase

Ohta

et al. 2008

JBSE

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Cardiovascular diseases are closely related to blood flow. Ultrasonic-Measurement-Integrated (UMI) simulation, in which results of ultrasonic measurement are fed back to the flow simulation, was proposed in a previous study in order to reproduce the real blood flow accurately and efficiently. The usability of the UMI simulation was confirmed by numerical experiment, but the effectiveness of this simulation was strongly affected by the accuracy of the ultrasonic measurement. In this paper, we examined the accuracy of a commercial ultrasonic measurement device by an experiment with a PVA-H straight tube phantom. By analyzing the measured color Doppler images for a developed laminar flow inside the phantom, we obtained the relationship between the color Doppler value and the Doppler velocity (C-V relationship). It was revealed that the original C-V relationship provided in the device as a color bar was not suitable for quantitative evaluation of Doppler velocity to be used in UMI simulation. Compared with the original C-V relationship, the present C-V relationship results are in far better agreement with the analytic solution. Investigation of the normalized error confirmed that the result obtained with the present C-V relationship was reliable in cases of relatively high Reynolds number in the flow domain except near the wall. The two signal conditioning factors of the device had little influence on the Doppler velocity. Finally, we investigated the effect of temporal and spatial averaging of ultrasonic measurement data to clarify the relation between the number of averagings and the level of agreement with the analytic solution.

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Aneurysm Wall Stress and Tendency to Rupture Are Features of Physical Wall Properties: An Experimental Study

Cited by 18 publications

References 23 publications

Computational Evaluation of Aortic Aneurysm Rupture Risk: What Have We Learned So Far?

Computational Evaluation of Aortic Aneurysm Rupture Risk: What Have We Learned So Far?

Can We Predict Abdominal Aortic Aneurysm (AAA) Progression and Rupture by Non-Invasive Imaging?—A Systematic Review

Experimental Validation of Color Doppler Velocity Measurement for Ultrasonic-Measurement-Integrated Simulation of Blood Flow

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