Estimation of wall properties and wall strength of aortic aneurysms using modern imaging techniques. One more step towards a patient-specific assessment of aneurysm rupture risk

摘要

Abdominal aortic aneurysmal disease is a major health problem with rupture representing its main complication accompanied by great mortality. Elective repair is currently performed with mortality rates <3%, based upon size or expansion rate, with a recommended threshold of 5.5. cm maximum diameter or >1. cm/year enlargement. It is well established that even small AAAs without indication for surgical repair can experience rupture with catastrophic outcomes whereas larger aneurysms often remain intact for a long period. It is recognized, therefore, that the currently used, maximum diameter criterion can not accurately predict AAAs evolution. There is increasing interest in the role of patient-specific biomechanical profiling of AAA development and rupture. Biomechanically, rupture of a vessel occurs when intravascular forces exceed vessel wall structural endurance. Peak Wall Stress (PWS) has been previously shown to better identify AAAs prone to rupture than maximum diameter, but currently stress analysis takes into account several assumptions that influence results to a large extent and limit their use. Moreover stress represents only one of two determinants of rupture risk according to the biomechanical perspective. Wall strength and mechanical properties on the other hand cannot be assessed in vivo but only ex vivo through mechanical studies with mean values of these parameters taken into account for rupture risk estimations. New possibilities in the field of aortic imaging offer promising tools for the validation and advancement of stress analysis and the in vivo evaluation of AAAs' wall properties and wall strength. Documentation of aortic wall motion during cardiac cycle is now feasible through ECG-gated multi-detector CT imaging offering new possibilities towards an individualized method for rupture risk and expansion-rate predictions based on data acquired in vivo.