Abstract

Abdominal aortic aneurysms (AAA) is a dilation of the abdominal aorta, typically within the infra-renal segment of the vessel that cause an expansion of at least 1.5 times the normal vessel diameter. It is becoming a leading cause of death in the United States and around the world, and consequentially, in 2009, the Society for Vascular Surgery (SVS) practice guidelines expressed the critical need to further investigate the factors associated with the risk of AAA rupture, along with potential treatment methods. For decades, the maximum diameter (Dmax) was introduced as the main parameter used to assess AAA behavior and its rupture risk. However, it has been shown that three main categories of parameters including geometrical indices, such as the maximum transverse diameter, biomechanical parameters, such as material properties, and historical clinical parameters, such as age, gender, hereditary history and life-style affect AAA and its rupture risk.

Therefore, despite all efforts that have been undertaken to study the relationship among different parameters affecting AAA and its rupture, there are still limitations that require further investigation and modeling; the challenges associated with the traditional, clinical quality images represent one class of these limitations. The other limitation is the use of the homogenous hyper-elastic material property model to study the entire AAA, when, in fact, there is evidence that different degrees of degradation of the elastin and collagen network of the AAA wall lead to different regions of the AAA exhibiting different material properties, which, in turn, affect its biomechanical behavior and rupture. Moreover, the effects of all three main categories of parameters need to be considered simultaneously and collectively when studying the AAAs and their rupture, so once again, the field can further benefit from such studies. Therefore, in this work, we describe a comprehensive pipeline consisting of three main components to overcome some of these existing limitations.

The first component of the proposed method focuses on the reconstruction and analysis of both synthetic and human subject-specific 3D models of AAA, accompanied by a full geometric parameter analysis and their effects on wall stress and peak wall stress. The second component investigates the effect of various biomechanical parameters, specifically the use of various homogeneous and heterogeneous material properties to model the behavior of the AAA wall. To this extent, we introduce two different patient-specific regional material property models to better mimic the physiological behavior of the AAA wall. Finally, the third component utilizes machine learning methods to develop a comprehensive predictive model that incorporates the effect of the geometrical, biomechanical and historical clinical data to predict the rupture severity of AAA in a patient-specific manner. This is the first comprehensive semi-automated method developed for the assessment of AAA.

Our findings illustrate that using a regional material property model that mimics the realistic heterogeneity of the vessel’s wall leads to more reliable and accurate predictions of AAA severity and associated rupture risk. Additionally, our results indicate that using only Dmax as an indicator for the rupture risk is insufficient, while a combination of parameters from different sources along with PWS could serve as a more reliable rupture assessment. These methods can help better characterize the severity of AAAs, better predict their associated rupture risk, and, in turn, help clinicians with earlier, patient-customized diagnosis and patient-customized treatment planning approaches, such as stent grafting.

Library of Congress Subject Headings

Abdominal aneurysm--Imaging; Abdominal aorta--Imaging; Three-dimensional imaging systems in medicine; Machine learning

Publication Date

11-3-2020

Document Type

Dissertation

Student Type

Graduate

Degree Name

Imaging Science (Ph.D.)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Cristian A. Linte

Advisor/Committee Member

Maria Helguera

Advisor/Committee Member

Simona C Hodis

Campus

RIT – Main Campus

Plan Codes

IMGS-PHD

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