## 4 Discussion
Furthermore, developing targeted educational programs and workshops could be instrumental in equipping clinicians with the necessary skills to interpret HDF-based biomarkers effectively. Integrating these advanced metrics into existing clinical workflows can be challenging, as it requires not only technical proficiency but also adjustments to established diagnostic and treatment paradigms. Consideration for the contrast agent. Recently, non-contrast MRA examinations have attracted increased attention after the emergence of nephrogenic systemic fibrosis (NSF) and the discovery of a connection to gadolinium-based MRA [54]. Thus, the cardiac magnetic resonance (CMR) community is moving towards non-contrasted acquisitions [50]. Though this technique is increasingly being explored, gadolinium-enhanced 4D-flow MRI remains the gold standard for better visualization of flow patterns and more precise quantification of hemodynamic parameters. For non-contrasted 4D-flow MRI, the standard FA should be set around 7\({}^{\circ}\) for adults and 12\({}^{\circ}\) for neonates [55], and TR and TE should be as short as possible [50]. By increasing the temporal and spatial resolution of 4D-flow MRI, there has been a better visualization of hemodynamic properties, as demonstrated in previous studies [56]. This may offer a valuable alternative to contrast-enhanced MRA. However, it is important to note that the HDF assessment is not impacted by the choice of 4D-flow MRI acquisition as the calculation relies on the velocity and fundamental fluid components. Potential uncertainty. Time-resolved 3D phase-contrast (PC) MRI, also known as 4D-flow MRI, is widely utilized for hemodynamic assessment due to its capacity to offer patient-specific vessel geometries and flow data. This information is valuable for establishing fully personalized conditions at boundaries. A recent study conducted by [PERSON] et al. has brought attention to the potential for inaccuracies in aortic hemodynamics when using PC-MRI for flow data measurements [57]. In a recent study, [PERSON] et al. delved into the uncertainty's origins and outlined its quantification methodologies. Through the implementation of Monte Carlo simulations with a steady-state Navier-Stokes solver, they measured the uncertainty of blood velocity, pressure, vorticity, and wall shear stress distribution along the aorta. The analysis revealed that the uncertainty in peak systolic pressure exhibited the highest magnitude, followed by velocity and vorticity, prompting a notable concern within the scope of our study. They also emphasized that the uncertainty does not depend either on the anatomical location or on the flow regime, which again imposes the limitation on our LV-based study. The work, however, is limited by its assumption of steady flow. Nonetheless, it remains applicable in evaluating hemodynamic uncertainty [58]. They also indicated that the VENC strategy may impact uncertainty propagation in computational hemodynamics, especially during the late diastole, when blood velocity is minimal. It has been suggested that achieving higher signal-to-noise ratio levels may be possible by utilizing different VENC values for the three-phase velocity components. This principle remains applicable to the examination of uncertainty propagation. Consequently, it is advisable to conduct further research involving varying VENC values and the measurement of uncertainty in HDF calculation using the same cohort. On the contrary, a very recent study by [PERSON] et al. demonstrated a similar finding on the anatomical location (Aorta). They demonstrated stroke volume and cardiac cycle have a significant influence on wall shear stresses (WSSs) and the velocity distribution in vessel regions. They also emphasized the spatial distribution of the inlet velocity as another source of uncertainty [59]. Therefore, patient-specific inlet conditions are recommended to obtain reliable hemodynamic predictions. A previous study by [PERSON] et al.