The risk of aortic dissection is increased in patients with Marfan’s syndrome. Prophylactic surgery is based on the aortic diameter, but, more likely, dissection occurs a result of abnormal aortic biomechanics. Dissection may occur even after surgery. It is thus important that novel biomarkers for improved assessment of disease and timing of surgery are designed. Aortic diameter is a biomarker that does not fully describe aortic biomechanics. MRI allows for the mapping of potentially better biomarkers in 4D (3 spatial dimensions and time). I propose to combine anatomical balanced steady state free precession (bSSFP) and 4D flow MRI for mapping of aortic shear stress (SS), displacement and stiffness. First, we will develop novel respiratory-robust bSSFP and 4D flow MRI sequences with high spatial and temporal resolution for detailed assessment of biomechanics. Second, machine learning algorithms will be developed to segment the 3D aorta from the bSSFP images at all timeframes. These segmentations will be used 1) to calculate 4D SS and the 3D oscillatory shear index (OSI) on 4D flow MRI images, 2) to calculate 4D displacement by registration of time-resolved segmentations to the diastolic reference and 3) to calculate 3D local pulse wave velocity (a proxy for stiffness) with aortic centerlines and flow planes created for each timeframe. Displacement and stiffness will be validated by phantom and pig aorta experiments. By comparison with cohort-averaged maps of 50 volunteers, patient-specific maps of abnormal SS, OSI, displacement and stiffness will be created for 100 Marfan patients. In resected tissue of 50 patients that underwent surgery, the MRI-derived biomechanics will be compared with tensile stress tests and elastin staining. These techniques will improve disease assessment. The proposed research is supported by Philips, Pie Medical Imaging and the LifeTec Group and utilization will occur in collaboration with the department of Cardiology.