BackgroundQuantification of aortic morphology plays an important role in the evaluation and follow‐up assessment of patients with aortic diseases, but often requires labor‐intensive and operator‐dependent measurements. Automatic solutions would help enhance their quality and reproducibility.PurposeTo design a deep learning (DL)‐based automated approach for aortic landmarks and lumen detection derived from three‐dimensional (3D) MRI.Study TypeRetrospective.PopulationThree hundred ninety‐one individuals (female: 47%, age = 51.9 ± 18.4) from three sites, including healthy subjects and patients (hypertension, aortic dilation, Turner syndrome), randomly divided into training/validation/test datasets (N = 236/77/78). Twenty‐five subjects were randomly selected and analyzed by three operators with different levels of expertise.Field Strength/Sequence1.5‐T and 3‐T, 3D spoiled gradient‐recalled or steady‐state free precession sequences.AssessmentReinforcement learning and a two‐stage network trained using reference landmarks and segmentation from an existing semi‐automatic software were used for aortic landmark detection and segmentation from sinotubular junction to coeliac trunk. Aortic segments were defined using the detected landmarks while the aortic centerline was extracted from the segmentation and morphological indices (length, aortic diameter, and volume) were computed for both the reference and the proposed segmentations.Statistical TestsSegmentation: Dice similarity coefficient (DSC), Hausdorff distance (HD), average symmetrical surface distance (ASSD); landmark detection: Euclidian distance (ED); model robustness: Spearman correlation, Bland–Altman analysis, Kruskal–Wallis test for comparisons between reference and DL‐derived aortic indices; inter‐observer study: Williams index (WI). A WI 95% confidence interval (CI) lower bound >1 indicates that the method is within the inter‐observer variability. A P‐value <0.05 was considered statistically significant.ResultsDSC was 0.90 ± 0.05, HD was 12.11 ± 7.79 mm, and ASSD was 1.07 ± 0.63 mm. ED was 5.0 ± 6.1 mm. A good agreement was found between all DL‐derived and reference aortic indices (r >0.95, mean bias <7%). Our segmentation and landmark detection performances were within the inter‐observer variability except the sinotubular junction landmark (CI = 0.96;1.04).Data ConclusionA DL‐based aortic segmentation and anatomical landmark detection approach was developed and applied to 3D MRI data for achieve aortic morphology evaluation.Evidence Level3Technical EfficacyStage 2

BackgroundAscending thoracic aortic aneurysm (ATAA) is a silent and threatening dilation of the ascending aorta (AscAo). Maximal aortic diameter which is currently used for ATAA patients management and surgery planning has been shown to inadequately characterize risk of dissection in a large proportion of patients. Our aim was to propose a comprehensive quantitative evaluation of aortic morphology and pressure-flow-wall associations from 4D flow MRI data in healthy aging and in patients with ATAA.MethodsWe studied 17 ATAA patients (64.7±14.3 years, 5 females) along with 17 age- and sex-matched healthy controls (59.7±13.3 years, 5 females) and 13 younger healthy subjects (33.5±11.1 years, 4 females). All subjects underwent an MRI exam including 4D flow and 3D anatomical images of the aorta. This latter dataset was used for aortic morphology measurements including AscAo maximal diameter (iDMAX) and volume, indexed to body surface area. 4D flow MRI data were used to estimate: 1) cross-sectional local AscAo spatial (∆PS) and temporal (∆PT) pressure changes as well as the distance (∆DPS) and time duration (∆TPT) between local pressure peaks, 2) AscAo maximal wall shear stress (WSSMAX) at peak systole, 3) AscAo flow vorticity amplitude (VMAX), duration (VFWHM) and eccentricity (VECC).ResultsConsistency of flow and pressure indices was demonstrated through their significant associations with AscAo iDMAX (WSSMAX: r=-0.49, p<0.001; VECC:r=-0.29, p=0.045; VFWHM:r=0.48, p<0.001; ∆DPS:r=0.37, p=0.010; ∆TPT:r=-0.52, p<0.001) and indexed volume (WSSMAX:r=-0.63, VECC:r=-0.51, VFWHM:r=0.53, ∆DPS:r=0.54, ∆TPT:r=-0.63, p<0.001 for all). Intra-AscAo cross-sectional pressure difference, ∆PS, was significantly and positively associated with both VMAX (r=0.55, p=0.002) and WSSMAX (r=0.59, p<0.001) in the 30 healthy subjects (48.3±18.0 years). Associations remained significant after adjustment for iDMAX, age and systolic blood pressure. Superimposition of ATAA patients to normal aging trends between ∆PS and WSSMAX as well as VMAX allowed identifying patients with substantially high pressure differences concomitant with AscAo dilation.ConclusionLocal variations in pressures within ascending aortic cross-sections derived from 4D flow MRI were associated with flow changes, as quantified by vorticity, and with stress exerted by blood on the aortic wall, as quantified by wall shear stress. Such flow-wall and pressure interactions might help for the identification of at-risk patients.

Background Ascending thoracic aortic aneurysm (ATAA) is a silent and threatening dilation of the ascending aorta (AscAo). Maximal aortic diameter which is currently used for ATAA patients management and surgery planning has been shown to inadequately characterize risk of dissection in a large proportion of patients. Our aim was to propose a comprehensive quantitative evaluation of aortic morphology and pressure-flow-wall associations from four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) data in healthy aging and in patients with ATAA. Methods We studied 17 ATAA patients (64.7 +/- 14.3 years, 5 females) along with 17 age- and sex-matched healthy controls (59.7 +/- 13.3 years, 5 females) and 13 younger healthy subjects (33.5 +/- 11.1 years, 4 females). All subjects underwent a CMR exam, including 4D flow and three-dimensional anatomical images of the aorta. This latter dataset was used for aortic morphology measurements, including AscAo maximal diameter (iD(MAX)) and volume, indexed to body surface area. 4D flow MRI data were used to estimate 1) cross-sectional local AscAo spatial (triangle P-S) and temporal (triangle P-T) pressure changes as well as the distance (triangle D-PS) and time duration (triangle T-PT) between local pressure peaks, 2) AscAo maximal wall shear stress (WSSMAX) at peak systole, and 3) AscAo flow vorticity amplitude (V-MAX), duration (V-FWHM), and eccentricity (V-ECC). Results Consistency of flow and pressure indices was demonstrated through their significant associations with AscAo iD(MAX) (WSSMAX:r = -0.49, p < 0.001; V-ECC:r = -0.29, p = 0.045; V-FWHM:r = 0.48, p < 0.001; triangle D-PS:r = 0.37, p = 0.010; triangle T-PT:r = -0.52, p < 0.001) and indexed volume (WSSMAX:r = -0.63, V-ECC:r = -0.51, V-FWHM:r = 0.53, triangle D-PS:r = 0.54, triangle T-PT:r = -0.63, p < 0.001 for all). Intra-AscAo cross-sectional pressure difference, triangle P-S, was significantly and positively associated with both V-MAX (r = 0.55, p = 0.002) and WSSMAX (r = 0.59, p < 0.001) in the 30 healthy subjects (48.3 +/- 18.0 years). Associations remained significant after adjustment for iD(MAX), age, and systolic blood pressure. Superimposition of ATAA patients to normal aging trends between triangle P-S and WSSMAX as well as V-MAX allowed identifying patients with substantially high pressure differences concomitant with AscAo dilation. Conclusion Local variations in pressures within ascending aortic cross-sections derived from 4D flow MRI were associated with flow changes, as quantified by vorticity, and with stress exerted by blood on the aortic wall, as quantified by wall shear stress. Such flow-wall and pressure interactions might help for the identification of at-risk patients.

We develop deep learning for full aortic wall shear stress assessment using 3D aortic shapes and ascending aortic waveforms as input flow. Technically, this would reduce the acquisition time to less than a minute and the post-processing time to a few seconds.

Purpose: The purpose of this study was to investigate the benefit of aortic volumes compared to diameters or cross-sectional areas on three-dimensional (3D) magnetic resonance imaging (MRI) in discriminating between patients with dilated aorta and matched controls. Materials and methods: Sixty-two patients (47 men and 15 women; median age, 66 years; age range: 33-86 years) with tricuspid aortic valve and ascending thoracic aorta aneurysm (TAV-ATAA) and 43 patients (35 men and 8 women; median age, 51 years; age range: 17-76 years) with bicuspid aortic valve and dilated ascending aorta (BAV) were studied. One group of 54 controls matched for age and sex to patients with TAV-ATAA (39 men and 15 women; median age, 68 years; age range: 33-81 years) and one group of 42 controls matched for age and sex to patients with BAV (34 men and 8 women; median age, 50 years; age range: 17-77 years) were identified. All participants underwent 3D MRI, used for 3D-segmentation for measuring aortic length, maximal diameter, maximal cross-sectional area (CSA) and volume for the ascending aorta. Results: An increase in ascending aorta volume (TAV-ATAA: +107%; BAV: +171% vs. controls; P < 0.001) was found, which was three times greater than the increase in diameter (TAV-ATAA: +29%; BAV: +40% vs. controls; P < 0.001). In differentiating patients with TAV-ATAA from their controls, the indexed ascending aorta volume showed better performances (AUC, 0.935 [95% confidence interval (CI): 0.882-0.989]; accuracy, 88.7% [95% CI: 82.9-94.5]) than indexed ascending aorta length (P < 0.001), indexed ascending aorta maximal diameter (P = 0.003) and indexed ascending aorta maximal CSA (P = 0.03). In differentiating patients with BAV from matched controls, indexed ascending aorta volume showed significantly better performances performance (AUC, 0.908 [95% CI: 0.829-0.987]; accuracy, 88.0% [95% CI: 80.9-95.0]) than indexed ascending aorta length (P = 0.02) and not different from indexed ascending aorta maximal diameter (P = 0.07) or from indexed ascending aorta maximal CSA (P = 0.27) CONCLUSION: Aortic volume measured by 3D-MRI integrates both elongation and luminal dilatation, resulting in greater classification performance than maximal diameter and length in differentiating patients with dilated ascending aorta or aneurysm from controls. Copyright (C) 2023 2023 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Background and objective: Aortic stiffness can be evaluated by aortic distensibility or pulse wave velocity (PWV) using applanation tonometry, 2D phase contrast (PC) MRI and the emerging 4D flow MRI. However, such MRI tools may reach their technical limitations in populations with cardiovascular disease. Accordingly, this work focuses on the diagnostic value of aortic stiffness evaluated either by applanation tonometry or MRI in high-risk coronary artery disease (CAD) patients. Methods: 35 patients with a multivessel CAD and a myocardial infarction treated 1 year before were prospectively recruited and compared with 18 controls with equivalent age and sex distribution. Ascending aorta distensibility and aortic arch 2D PWV were estimated along with 4D PWV. Furthermore, applanation tonometry carotid-to-femoral PWV (cf PWV) was recorded immediately after MRI. Results: While no significant changes were found for aortic distensibility; cf PWV, 2D PWV and 4D PWV were significantly higher in CAD patients than controls (12.7 ± 2.9 vs. 9.6 ± 1.1; 11.0 ± 3.4 vs. 8.0 ± 2.05 and 17.3 ± 4.0 vs. 8.7 ± 2.5 m·s−1 respectively, p < 0.001). The receiver operating characteristic (ROC) analysis performed to assess the ability of stiffness indices to separate CAD subjects from controls revealed the highest area under the curve (AUC) for 4D PWV (0.97) with an optimal threshold of 12.9 m·s−1 (sensitivity of 88.6% and specificity of 94.4%). Conclusions: PWV estimated from 4D flow MRI showed the best diagnostic performances in identifying severe stable CAD patients from age and sex-matched controls, as compared to 2D flow MRI PWV, cf PWV and aortic distensibility.

This study details application of deep learning for automatic segmentation of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging for automatic aortic analysis on the large MESA cohort with assessment on an external cohort of thoracic aortic aneurysm (TAA) patients. This study includes images and corresponding analysis of the ascending and descending aorta at the pulmonary artery bifurcation from the MESA study. Train, validation, and internal test sets consisted of 1123 studies (24,282 images), 374 studies (8067 images), and 375 studies (8069 images), respectively. The external test set of TAAs consisted of 37 studies (3224 images). CNN performance was evaluated utilizing a dice coefficient and concordance correlation coefficients (CCC) of geometric parameters. Dice coefficients were as high as 97.55% (CI: 97.47–97.62%) and 93.56% (CI: 84.63–96.68%) on the internal and external test of TAAs, respectively. CCC for maximum and minimum and ascending aortic area were 0.969 and 0.950, respectively, on the internal test set and 0.997 and 0.995, respectively, for the external test. The absolute differences between manual and deep learning segmentations for ascending and descending aortic distensibility were 0.0194 × 10−4 ± 9.67 × 10−4 and 0.002 ± 0.001 mmHg−1, respectively, on the internal test set and 0.44 × 10−4 ± 20.4 × 10−4 and 0.002 ± 0.001 mmHg−1, respectively, on the external test set. We successfully developed a U-Net-based aortic segmentation and analysis algorithm in both MESA and in external cases of TAA.

Background: We aimed to provide a comprehensive aortic stiffness description using magnetic resonance imaging (MRI) in patients with ascending thoracic aorta aneurysm and tricuspid (TAV-ATAA) or bicuspid (BAV) aortic valve. Methods: This case-control study included 18 TAV-ATAA and 19 BAV patients, with no aortic valve stenosis/severe regurgitation, who were 1:1 age-, gender- and central blood pressures (BP)-matched to healthy volunteers. Each underwent simultaneous aortic MRI and BP measurements. 3D anatomical MRI provided aortic diameters. Stiffness indices included: regional ascending (AA) and descending (DA) aorta pulse wave velocity (PWV) from 4D flow MRI: local AA and DA strain, distensibility and theoretical Bramwell-Hill (BH) model-based PWV, as well as regional arch PWV from 2D flow MRI. Results: Patient groups had significantly higher maximal M diameter (median[interquartile range], TAV-ATM: 47.5[42.0-51.3]mm, BAV: 45.0[41.0-47.0]mm) than their respective controls (29.1[26.8-31.8] and 28.1 [26.0-32.0]mm. p < 0.0001), while BP were similar (p >= 0.25). Stiffness indices were significantly associated with age (p >= 0.33), mean BP (arch PWV: rho = 025, p = 0.05; DA distensibility: rho = -0.30, p = 0.02) or AA diameter (arch PWV: rho = 0.28,p = 0.03; DA PWV: rho = 0.32, p = 0.009). None of them, however, was significantly different between TAV-ATAA or BAV patients and their matched controls. Finally, while direct PWV measures were significantly correlated to BH-PWV estimates in controls (rho >= 0.40), associations were non-significant in TAV-ATAA and BAV groups (p >= 0.18). Conclusions: The overlap of MRI-derived aortic stiffness indices between patients with TAV or BAV aortopathy and matched controls highlights another heterogeneous feature of aortopathy, and suggests the urgent need for more sensitive indices which might help better discriminate such diseases. (C) 2020 Elsevier B.V. All rights reserved.

Purpose: Understanding and prediction of ascending thoracic aortic aneurysms (ATAA) progression are not well established yet and aortic dissection is frequently occurring on normally sized and mildly dilated aortas. Despite known theoretical associations between pressures and blood flow patterns there are no studies focusing on their simultaneous evaluation. Our aim was to propose a comprehensive and quantitative evaluation of pressure-flow-wall interplay from 4D flow MRI in the setting of aortic dilation. Methods: We studied 12 patients with ATAA (67 ± 14 years, 7 males) and 12 healthy subjects (63 ± 12 years, 8 males) who underwent 4D flow MRI. The segmented velocity fields were used to estimate: 1) local ascending aorta (AA) pressure changes from Navier-Stokes-derived relative pressure maps (AADP, mmHg), 2) AA wall shear stress (AAWSS, Pa) by estimating local velocity derivatives at the aortic borders, 3) aortic flow vorticity using the λ2 method (AAV, s-1). Results: AA local pressure change (AADP) was significantly associated with both AAV (r = 0.55, p = 0.006) and AAWSS (r = 0.69, p < 0.001) and both associations remained significant after adjustment for diameter, age and BSA (p = 0.007 and p = 0.003 respectively). Such positive associations indicate that local pressure variations affect local blood flow, generating flow current from high to low pressures and subsequently vortices with the underlying stress exerted on the AA wall. Conclusion: Local variations in aortic pressures, measured using 4D flow MRI, are associated with flow disorganization as quantified by vorticity and with the increase in the stress exerted on the aortic wall, as quantified by wall shear stress.

Ascending thoracic aortic aneurysms (ATAA) are defined by a silent dilation of the ascending aorta (AA).Although maximal aortic diameter is currently used for surgery planning, a high proportion of patients with low diameters ending up with aortic dissection.Our purpose was to propose a fine and comprehensive quantitative evaluation of pressure-flow-wall interplay from 4D flow MRI data in the setting of aortic dilation.We studied 12 patients with ATAA (67±14 years, 7 male) and 12 healthy subjects (63±12 years, 8 male) who underwent 4D flow MRI acquisition.The segmented velocity fields were used to estimate: 1) local AA pressure changes from Navier-Stokes-derived relative pressure maps (AADP), 2) AA wall shear stress (AAWSS) by estimating local velocity derivatives at the aortic borders, 3) aortic flow vorticity using the λ2 method (AAV).AADP was significantly and positively associated with both AAV (r=0.55,p=0.006) and AAWSS (r=0.69 p<0.001).Such associations remained significant after adjustment for maximal diameter, age and BSA.Local variations in pressures within the aorta, rendered possible while using 4D flow MRI, are associated with flow disorganization as quantified by vorticity and with the increase in the stress exerted on the aortic wall, as quantified by wall shear stress.

Arterial pulse wave velocity (PWV) is associated with increased mortality in aging and disease. Several studies have shown the accuracy of applanation tonometry carotid-femoral PWV (Cf-PWV) and the relevance of evaluating central aorta stiffness using 2D cardiovascular magnetic resonance (CMR) to estimate PWV, and aortic distensibility-derived PWV through the theoretical Bramwell-Hill model (BH-PWV). Our aim was to compare various methods of aortic PWV (aoPWV) estimation from 4D flow CMR, in terms of associations with age, Cf-PWV, BH-PWV and left ventricular (LV) mass-to-volume ratio while evaluating inter-observer reproducibility and robustness to temporal resolution.

BackgroundAging‐related arterial stiffness is associated with substantial changes in global and local arterial pressures. The subsequent early return of reflected pressure waves leads to an elevated left ventricular (LV) afterload and ultimately to a deleterious concentric LV remodeling.PurposeTo compute aortic time‐resolved pressure fields of healthy subjects from 4D flow MRI and to define relevant pressure‐based markers while investigating their relationship with age, LV remodeling, as well as tonometric augmentation index (AIx) and pulse wave velocity (PWV).Study TypeRetrospective.PopulationForty‐seven healthy subjects (age: 49.5 ± 18 years, 24 women).Field Strength/Sequence3 T/4D flow MRI.AssessmentSpatiotemporal pressure fields were computed by integrating velocity‐derived pressure gradients using Navier–Stokes equations, while assuming zero pressure at the sino‐tubular junction. To quantify aortic pressure spatiotemporal variations, we defined the following markers: 1) volumetric aortic pressure propagation rates ΔP E1/ΔV and ΔP E2/ΔV, representing variations of early and late systolic relative pressure peaks along the aorta, respectively, according to the cumulated aortic volume; 2) ΔA PE1‐PE2 defined in four aortic regions as the absolute difference between early and late systolic relative pressure peaks amplitude.Statistical TestsLinear regression, Wilcoxon rank sum test, Bland–Altman analysis, and intraclass correlation coefficients (ICC).ResultsSpatiotemporal variations of aortic pressure peaks were moderately to highly reproducible (ICC ≥0.50) and decreased significantly with age, in terms of absolute magnitude: ΔP E1/ΔV (r = 0.70, P < 0.005), ΔP E2/ΔV (r = –0.45, P < 0.005) and ΔA PE1‐PE2 (|r| > 0.39, P < 0.005). ΔP E1/ΔV was associated with LV remodeling (r = 0.53, P < 0.001) and ascending aorta ΔA PE1‐PE2 was associated with AIx (r = –0.59, P < 0.001). Both associations were independent of age and systolic blood pressures. Only weak associations were found between pressure indices and PWV (r ≤ 0.40).Data Conclusion4D flow MRI relative aortic pressures were consistent with physiological knowledge as demonstrated by their significant volumetric and temporal variations with age and their independent association with LV remodeling and augmentation index.Level of Evidence 2Technical Efficacy Stage 3J. Magn. Reson. Imaging 2019;50:982–993.

Introduction: Direct measurement of central aortic stiffness by means of aortic distensibility has been shown to be an early marker of subclinical vascular alteration. However, current methods to m...

Background: Clinically, aortic geometry assessment is mainly based on the measurement of maximal diameters at different anatomic locations, which are subsequently used to indicate prophylactic aortic surgery. However, 3D evaluation of aortic morphology could provide volumetric quantification, which integrates both aortic dilatation and elongation and might thus be more sensitive to early geometric changes than diameters. Precise aortic morphology is also required for the calculation of pulse wave velocity (PWV), an established marker of aortic stiffness. Accordingly, we proposed a 3D semi-automated analysis of thoracic aorta MRI data optimizing morphological and subsequent stiffness assessment. Methods: We studied 74 individuals (40 males, 50 +/- 12 years): 21 healthy volunteers and 53 patients with hypertension in whom aortic 3D MRI angiography and 2D + t phase-contrast and cine imaging were performed. A semi-automated method was proposed for volumetric aortic segmentation and was evaluated by studying resulting measurements (length, diameters, volumes and PWVMRI) in terms of: 1) reproducibility, 2) correlations with well-established 2D aortic length and diameters, 3) associations with age, carotid-femoral PWV (cf-PWV) and presence of hypertension. Results: The measurements obtained with the proposed method were reproducible (coefficients of variation <= 5.1%) and were highly correlated with 2D measurements (arch length: r = 0.80, Bland-Altman mean bias [limits]: 2.7 mm [-25; 30]; PWVMRI = 0.95, 0.22 m/s [-1.9; 2.4]). Higher or similar correlations with age were found for the proposed 3D method compared to the 2D approach (arch length: r = 0.47 (2D), r = 0.60 (3D); PWVMRI = 0.63 (2D), r = 0.64 (3D)). Moreover, a significant association was found between PWV and cf-PWV (r = 0.49, p < 0.001). All aortic measurements increased with hypertension (p < 0.05) and with age: arch length (+ 9mm/decade); diameters: ascending (+1.2mL/decade) and descending aorta ( +1.0mm/decade); volumes: ascending (+ 2.6mL/decade) and descending aorta (+ 4.0mL/decade); PWV (+ 1.7 m s(-1)/decade). Conclusions: A semi-automated method based on cylindrical active surfaces was proposed for the 3D segmentation of the aorta using a single MRI dataset, providing aortic diameters at anatomical landmarks, aortic volumes and the aortic center-line length used for PWV estimation. Such measurements were reproducible and comparable to expert measurements, which required time-consuming center-line delineation. Furthermore, expected relationships with age and hypertension were found indicating the consistency of our measurements.

Aim: to 1) evaluate the ability of 4D flow MRI to assess left ventricular (LV) diastolic function, as compared to ultrasound (US), 2) analyse simultaneously LV filling and aortic stiffness in aging.Method: We studied 36 healthy subjects (52±18 years) who underwent MRI and US exams.A custom software was used for LV segmentation and calculation of transmitral flow-rate curves in a 4D flow MRI stack of slices located between the mitral valve and the apex and perpendicular to the LV long axis.Finally, the early (E) and late (A) flow-rate peaks were automatically detected and the E/A ratio was computed.Aortic pulse wave velocity (PWV) was also calculated from the 4D flow images.Results: A significant and high correlation was found between 4D flow and US E/A (R=0.71,p<0.001).Besides, a higher correlation was obtained between E/A and age when using 4D flow than US (E/A vs. age: R 4D =-0.87, p<0.001,R US =-0.69, p<0.001).Thanks to 4D flow MRI, simultaneous variation of aortic PWV and diastolic function with age was further illustrated.Conclusion: 4D flow MRI provided consistent LV E/A measurements in terms of comparison against reference US and associations with age.It also provided an insight into aorta/left heart coupling.

MRI 4D flow velocity is used to evaluate relative blood pressures in the aorta throughout the cardiac cycle.This (3D+t) pressure mapping is used to assess: 1) relationship between trans-aortic pressure gradient with age and aortic tapering (proximal to distal narrowing in lumen area), 2) effect of temporal resolution.We studied 47 healthy subjects (49±17.6 years, 24 men) who underwent 4D flow MRI, among them 20 were reconstructed in 20 and 50 phases per cardiac cycle.Pressure gradients maps were estimated from velocity fields using the Navier-Stokes equations.Relative pressures were calculated using an iterative algorithm while considering a zero pressure at the aortic valve.Distal (DA) to proximal (PA) aortic pressure gradient decreased with age (r=0.60,p<0.05) and was inversely related to DA/PA areas ratio (r=0.35,p<0.05).Such result is in line with the physiological evidence indicating that in aging DA area tends to equalize with PA area lowering the DA to PA pressure gradient.Peak systolic pressure was higher when considering the 50 phases data.Relative pressures calculated from 4D flow within the whole aortic volume through time are consistent with prior physiological knowledge as demonstrated by their variations with age and with aortic geometry.

Purpose: Compare various methods of transit time (TT) and consequently aortic Pulse Wave Velocity (aoPWV) estimation from 4D MRI (aoPWV=aortic length/TT), in terms of associations with age and Bramwell-Hill (BH aoPWV). Method: We studied 43 healthy subjects (48±17 yrs.) who had aortic 4DFlow MRI. Three strategies were used to estimate aoPWV: (S1) using flow curves in two aortic locations to calculate TT (ascending (AA) and distal descending aorta (dDA)) as a 2D-like strategy, (S2 and S3) using flow curves of the entire aortic path-line between dDA and AA to estimate TT with various methods: cross-correlation; Fourier and Wavelet or aoPVW by fitting a plan on the systolic upslope of flow curves. Results: Expected associations with age were found for the three strategies with strongest correlations for 3D-like strategies than 2D-like strategy (S2:r=0.75, S3:r=0.72, p<0.001, S1: r=0.55, p<0.001). Similar results were found for associations with BH aoPWV. Best results were obtained using the TT wavelet-based approach. Conclusion: Low temporal resolution of 4DFlow is compensated by aortic 3D coverage, leading to strong associations of aoPWV with age and BH aoPWV.