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Longitudinal study on 3D ultrasound-based rupture risk assessment of Abdominal Aortic Aneurysms

医学 放射科 腹主动脉瘤 超声波 血栓 管腔(解剖学) 主动脉破裂 动脉瘤 外科 主动脉瘤
作者
Arjet Nievergeld,Judith Fonken,Mirunalini Thirugnanasambandam,Esther Maas,Marc R.H.M. van Sambeek,Richard G. P. Lopata
出处
期刊:European Journal of Echocardiography [Oxford University Press]
标识
DOI:10.1093/ehjci/jeaf030
摘要

Abstract Aims Image-based, patient-specific rupture risk analysis of AAAs is promising but it is limited by invasive and costly imaging modalities. Ultrasound (US) offers a safe, more affordable alternative, allowing multiple assessments during follow-up and enabling longitudinal studies on AAA rupture risk. Methods and results This study used time-resolved three-dimensional US to assess AAA rupture risk parameters over time, based on vessel and intraluminal thrombus (ILT) geometry. The locations and magnitude of peak wall rupture index (PWRI), peak wall stress (PWS), and maximum ILT thickness for varying AAA growth rates, and the correlation between PWRI, PWS, and geometric parameters were investigated. This study demonstrated that US-based biomechanical analysis is suitable for assessing rupture potential, providing insights into the evolution through various phases in AAA development. For the fast-growing AAAs, the location of PWRI moved closer to the locations of maximum ILT thickness, whereas the location of PWS moved further away. The newly introduced parameter, i.e., percentage of ILT expected for an aneurysm with a healthy lumen, showed a stronger correlation with PWS and PWRI and was less dependent on AAA size, indicating the potential for further research on ILT's impact on AAA rupture risk. Conclusion This study represents a step towards clinical introduction of US-based AAA rupture risk analysis. Further research and randomized trials are required to directly correlate PWRI with rupture risk. Further improvement of ILT visibility and personalization of the US-based models will be required to achieve clinical acceptance of model-based rupture risk predictions.

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