The ability to systematically characterize effects in aortic disease patients and healthy controls is important to improve disease diagnosis. Time-resolved 3D PC-MRI with three-directional velocity encoding (4D flow MRI) has been successfully applied for the analysis of altered hemodynamics in cardiovascular disease¹. However, data analysis can be time consuming and the inherent volumetric 3D coverage provided by 4D flow MRI is often not fully utilized by analysis based on 2D planes. Recently, a number of studies have demonstrated that velocity distribution analysis can overcome these limitations by providing an efficient data analysis workflow that exploits the full volumetric coverage and can detect changes in vascular hemodynamics^{2, 3}. However, it is important to evaluate altered hemodynamics in aortic disease relative to normal blood flow dynamics which are known to be influenced by age and gender. It was thus the aim of this study to systematically apply flow distribution analysis in the entire volume of the aorta to establish normative reference values across a wide age range from pediatric to adult subjects.101 control subjects (age=39±17 years, age range=9-78, female=42) were identified via an IRB-approved retrospective chart review. 4D flow MRI was performed at 1.5T and 3T with full 3D coverage of the thoracic aorta (spatial resolution=2.5×2.1×3.2 mm3; temporal resolution=40-50 ms) using prospective ECG and respiratory navigator gating. Pulse sequence parameters were as follows: 1.5 T scan parameters ranged from TE/TR=2.3–3.4/4.8–6.6 ms, flip angle α=7–15° and a field of view of 340–400×200–300 mm; 3T scans used TE/TR =2.5/5.1 ms, flip angle α=7–15°, and a field of view of 400×308 mm. 3D PC-MR angiograms (3D PC-MRA) were computed and segmented to obtain a 3D volume of the aorta (Mimics, Materialise, Leuven, Belgium) which was used to compute a masked 4D velocity field (3 spatial dimensions + time). The masked aorta velocity field was used to generate a maximum intensity projection (MIP) in an oblique sagittal plane using the three peak systolic velocity phases to extract the aortic peak velocity. The velocities from all voxels within the first 8 time steps in systole, determinate optimal by a sensitivity analysis between groups², inside an aorta segmentation were plotted in histogram form and normalized by the total number of voxels in order to be compared across subjects and cohorts (Matlab, Natick, MA, USA). In addition, mean, median, standard deviation, skewness, kurtosis, and the normalized number of voxels >1m/s (incidence) were calculated for each case. Aortic diameter was extracted using a volume centerline of the 3D PC-MRA in the ascending aorta⁴. Each subject was classified into four groups of age (group 1: <20 years, group 2: 20-39 years; group 3: 40-59 years; group 4: >60 years) and gender (female and male). Association between computed parameters was assessed by regressions analysis and comparison between groups by ANOVA (P-value<0.05 was considered significant).Table 1 summarizes subject demographics by group. Examples of velocity MIPs in figure 1A illustrate visual differences in the aortic velocity patterns and distribution. The velocity distribution analysis for one subject is shown in figure 1C. Spider plots summarized for each cohort are shown in figure 1D providing a visual impression of the flow characteristics. Notice that peak velocity, aortic diameter, skewness, and kurtosis were able to differentiate significant changes between groups (P<0.05 using ANOVA) whereas mean velocities did not. Significant correlations with age are listed in Table 2, the three highest correlations plotted in figure 2. Notice that a quadratic fit was also found for age and aortic diameter. Significant inter-group analysis from spider plots for age and gender are presented in figure 3.The volumetric velocity distribution analysis presented here has demonstrated: 1) 3D blood flow velocity distributions can identify hemodynamic differences in healthy subjects across age and genders via basic statistical descriptors; 2) peak velocity, aortic diameter, skewness, and kurtosis enabled better identification of hemodynamic changes due to age and gender than mean and standard deviation. Our results indicate which blood flow parameters are best to establish normative values to assess flow disturbances in the thoracic aorta.Systematic velocity distribution analysis of 4D flow velocity data may identify and differentiate hemodynamic characteristics due to age, gender, and aortic peak velocity. Further studies are needed to evaluate the association of velocity distribution derived descriptors with patient prognosis.