Aortic aneurysm is a silent, asymptomatic enlargement of the aorta that can lead to life-threatening events. Current clinical guidelines rely on aortic diameter to assess the risk of complications and to time prophylactic interventions. However, the aorta may grow slowly for years or expand rapidly just before the occurrence of adverse events. Size-based criteria are a poor measure of disease progression, so new markers are needed to improve risk stratification in patients with aortic aneurysm.

Advanced magnetic resonance imaging (MRI) permits the non-invasive assessment of cardiovascular anatomy and function. Four-dimensional (4D) Flow MRI measures the three-dimensional blood velocity field over the cardiac cycle, and enables the quantification of blood forces acting on the aortic wall. These forces induce remodelling processes associated with physiological changes and diseases. One such force is the frictional force on the aortic wall, known as wall shear stress (WSS). WSS regulates normal vascular function, but its role in disease progression is unclear. Understanding blood flow forces in both healthy and diseased aortas may provide new insights into the mechanisms of aortic disease, and may help in identifying novel markers to assist clinical decision-making. This thesis aims to enhance the analysis of WSS with 4D Flow MRI in the context of aortic disease.

First, aortic wall motion is incorporated into the WSS analysis. 4D Flow MR image processing methods are mostly manual and time-consuming. Consequently, WSS analysis is mainly restricted to one cardiac phase or neglects aortic wall motion when assessing WSS metrics encompassing the whole cardiac cycle. This thesis tailored semi-automated atlas-based segmentation and surface registration methods to aortic applications, to obtain a moving aortic wall surface.

Second, a robust framework for statistical analysis to compare cohorts based on WSS metrics is proposed. WSS patterns can be visualised and quantified on three-dimensional WSS maps representing the aortic wall surface. These maps are used to infer local differences in WSS metrics between cohorts. This thesis introduces permutation tests in cardiovascular MRI to facilitate such local inter-cohort comparisons.

Third, a deep learning method is proposed to reduce the acquired data in cardiovascular 4D Flow MRI. Increasing the spatial resolution of 4D Flow MR images would be beneficial for estimating WSS metrics. By reducing the acquired data, the lengthy acquisition time of 4D Flow can be shortened or the temporal resolution improved. Alternatively, the time saved can be reinvested to increase the spatial resolution of the images, thereby enhancing WSS assessment.

Finally, the results obtained throughout the work performed in this thesis demonstrate the utility of 4D Flow MRI-based WSS markers in selected cohorts. Markers of altered blood flow were identified in patients with abdominal aortic aneurysm, and in dilated patients with bicuspid valves and aortic regurgitation. Moreover, 4D Flow MRI-based WSS metrics correlated to circulating biomarkers of inflammation and collagen synthesis in patients with mildly dilated aorta and tricuspid valves. These findings showcase the potential of 4D Flow MRI in improving the mechanistic understanding behind aortic diseases and their risk assessment.

Four-dimensional flow (4D Flow) CMR affords comprehensive 3D maps of advanced hemodynamics parameters such as wall shear stress (WSS). However, the evaluation of these data is often restricted to spatial averages in large regions of interests, such as the ascending aorta. Recent studies have explored ways of analyzing local intercohort WSS differences by using basic statistical tests with a p-value of 0.05 for determining significance, thus not accounting for the large number of comparisons made when exploring differences for multiple locations across the ascending aorta surface.

Permutation tests, frequently used in brain MRI, permit statistical analysis on a local level while controlling for the family-wise error rate by constructing the null hypothesis distribution based on the maximum statistic over the voxels at each permutation. We sought to use permutation tests to identify local regions of abnormal WSS in the ascending aorta in patients with aortic dilation.

Aims Understanding the mechanisms underlying ascending aortic dilation is imperative for refined risk stratification of these patients, particularly among incidentally identified patients, most commonly presenting with tricuspid valves. The aim of this study was to explore associations between ascending aortic haemodynamics, assessed using four-dimensional flow cardiovascular magnetic resonance imaging (4D flow CMR), and circulating biomarkers in aortic dilation. Methods and results Forty-seven cases with aortic dilation (diameter >= 40 mm) and 50 sex-and age-matched controls (diameter < 40 mm), all with tricuspid aortic valves, underwent 4D flow CMR and venous blood sampling. Associations between flow displacement, wall shear stress (WSS), and oscillatory shear index in the ascending aorta derived from 4D flow CMR, and biomarkers including interleukin-6, collagen type I alpha 1 chain, metalloproteinases (MMPs), and inhibitors of MMPs derived from blood plasma, were investigated. Cases with dilation exhibited lower peak systolic WSS, higher flow displacement, and higher mean oscillatory shear index compared with controls without dilation. No significant differences in biomarkers were observed between the groups. Correlations between haemodynamics and biomarkers were observed, particularly between maximum time-averaged WSS and interleukin-6 (r = 0.539, P < 0.001), and maximum oscillatory shear index and collagen type I alpha 1 chain (r = -0.575, P < 0.001 in cases). Conclusion Significant associations were discovered between 4D flow CMR derived whole-cardiac cycle WSS and circulating biomarkers representing inflammation and collagen synthesis, suggesting an intricate interplay between haemodynamics and the processes of inflammation and collagen synthesis in patients with early aortic dilation and tricuspid aortic valves.