case_images: PMC10210162_01
case_images: Information derived from 4D Flow-MRI and CT data for the aortic dissection patient, illustrating (A) 4D Flow-MRI acquisition, (B) visualisation of velocity streamlines on Circle Cardiovascular Imaging Software  at multiple time points throughout the cardiac cycle (t = 0.06, 0.12, 0.18, 0.36, and 0.42 s), (C) reconstructed geometry of the dissected thoracic aorta, illustrating the true lumen (solid color) and the false lumen (transparent), and (D) branch flow waveforms and pulse wave velocity (PWV) extraction which were used to generate patient-specific 3-Element Windkessel boundary conditions.
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case_images: Flow-MRI images of the aortae of two patients, one healthy (33 year old male) and one with a dissected aorta (55 year old male), were acquired via a Siemens research 4D Flow-MRI sequence (WIP 785A). Figure 1 summarizes this methodology, showing the dissected aorta as an example. This sequence employed an acquired resolution of 3.6 x 2.4 x 2.6 mm3, an imaging volume of 80 x 160 x 60 mm3, and VENC of 1.5 ms-1 (Figures 1A, B). In brief, a 3D magnetic resonance angiogram was created by deriving contrast from the instantaneous velocity magnitude of blood during systolic acceleration, peak systole, and systolic deceleration (Figures 1A, B). AD case, multi-VENC MRI imaging is required to allow for the precise segmentation of the false lumen. As this was not available, the geometry of the dissected aorta was reconstructed from CT images (Figure 1C). Arterial PWV (Figure 1D) is defined as the propagation speed of the systolic flow velocity wave front, or propagation speed of the pressure wave as it traverses the vasculature.
case_images: Flowchart of the methodology and software used to generate patient specific CFD models of the thoracic aortae including image processing, boundary condition calibration, and numerical analysis.
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case_images: Flow-MRI-derived in vivo blood flow data at each branch of the thoracic aorta. Figure 2 details a flowchart of the calibration methodology and CFD analysis.
case_images: (A) Velocity magnitude streamlines extracted from cvi42  at peak systole within the thoracic aorta of the aortic dissection patient and healthy volunteer. (B) 4D Flow-MRI derived flow waveform within the ascending aorta for each case.
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case_images: These MRI-derived flow waveforms (Figure 3) were converted to a velocity profile, interpolated to generate a constant time step size (dt = 0.001 s), and repeated for five cardiac cycles.
case_images: (A) Detailed flow diagram of the methodology used to calibrate impedance (Z), resistance (R), and compliance (C) of the 3EWMS BCs. (B) 0D-3D CFD model set-up, where each branch was coupled with a 3EWM. At the inlet, a 4D Flow-MRI derived flow waveform was converted to a parabolic velocity profile. The discretised 3EWM equation describes the pressure (P) and flow (Q) relationship at each branch, where n denotes the current iteration.
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case_images: The Windkessel parameters were then iteratively changed to minimise  by employing the fminsearch routine via an in-house Matlab  script at each branch of the thoracic aorta (Figure 4). Finally, the calibrated, patient specific 3EWM BCs were coupled to the 3D model (Figure 4).