Abstract

Better predictions of the likelihood of an aortic event in patients are paramount to providing quality care in medicine. Ascending aortic aneurysms (AsAA) are typically asymptomatic until sudden complications, such as rupture or dissection, occur and are often fatal [1]. As a form of preventative care, physicians identify and monitor high-risk patients. Surgical interventions are also performed for high-risk patients, replacing the weakened segment of the ascending aorta before a catastrophic event occurs. Determining which patients require surgical intervention or close monitoring is therefore extremely important. These decisions are largely guided by maximum diameter measurements; for the ascending aorta, a common threshold is 5.5 cm [2]. A maximum diameter dilated beyond 5.5 cm flags the patient as high-risk for an aortic complication. In the case of a Type B aortic dissection (TBAD), a “false lumen” forms within the aortic wall when blood enters through a tear, separating the tissue layers [3]. Resulting complications include aortic rupture and blood pooling, both of which disrupt normal flow and can compromise blood supply to downstream organs [4]. Uncomplicated TBADs, defined as dissections without rupture or organ malperfusion, are treated with optimal medical therapy (OMT) [3]. Various geometric factors, such as a descending aortic diameter greater than 4–4.5 cm and lumen size, have been shown to be significant predictors of intervention and mortality; however, they do not account for three-dimensional shape features, and long-term outcomes for patients managed with OMT remain poor [3]. Identification of three-dimensional shape features may therefore improve patient risk prediction. Further understanding of these shape features could provide additional insight into the fac- Trani 2 tors that contribute to dilation, dissection, or rupture. For example, high curvature of the ascending aorta has been associated with greater wall forces, which may increase the risk of dissection [5]. The objective of this study is to analyze shape features using centerline data from a cohort of human patient aortic CT and MRI scans. Specific points of interest include curvature of the aortic arch and path lengths, the latter of which are useful for a variety of additional curvature calculations

Document Type

Final Report

Author's School

McKelvey School of Engineering

Author's Department

Mechanical Engineering and Materials Science

Class Name

Mechanical Engineering and Material Sciences Independent Study

Language

English (en)

Date of Submission

5-16-2026

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