PURPOSE:To design a deep learning pipeline for automated, time-resolved segmentation of the left atrium (LA) from 4D flow MRI data. METHODS:We studied 100 individuals including 65 patients with atrial fibrillation (AF) and 35 healthy subjects (HS) resulting in 2530 4D flow MRI time-volumes acquired in different centers and scanners. In AF patients, expert data annotation was initialized on a single frame of highly resolved computed tomography data and then registered (initialization frame), propagated through time and corrected manually on 4D flow MRI, while it was performed manually in HS. Segmentation pipeline was based on a two-stage nnU-Net and was evaluated through segmentation performance metrics, as well as concordance of LA volumes through time and inner LA hemodynamics between prediction and reference segmentations. RESULTS:Testing on 35 AF patients and 15 HS revealed high segmentation performances (overall Dice score = 0.89 ± 0.03 for the initialization frame and 0.86 ± 0.04 for all time frames; AF patient/HS- and vendor-based Dice scores ≥ 0.86 ± 0.03 for the initialization frame and ≥ 0.83 ± 0.04 for all time frames). Strong associations and low Bland-Altman biases were obtained between predicted and reference LA volumes (r ≥ 0.89), velocities (r ≥ 0.96), stasis (r ≥ 0.99), vorticity (r ≥ 0.97), and kinetic energy (r ≥ 0.94). Interestingly, associations remained strong when comparing prediction against 4D flow-independent cine SSFP-derived LA volumes (r ≥ 0.87). CONCLUSION:The proposed time-resolved nnU-Net pipeline robustly and accurately segments the LA from 4D flow MRI images demonstrating excellent segmentation performance, strong agreement with reference volumes and velocity-based indices, as well as reliable generalization across vendors and patient populations.

Cardiac MRI late gadolinium enhancement (LGE) enables non-invasive identification of left atrial (LA) scar, whose spatial distribution is strongly associated with atrial fibrillation (AF) severity and recurrence. However, automatic LA scar segmentation remains challenging due to low contrast, annotation variability, and the lack of anatomical constraints, often leading to non-reliable predictions. Accordingly, our aim was to propose a progressive learning strategy to segment LA scar from LGE images inspired from a clinical workflow. A 3-stage framework based on SwinUNETR was implemented, comprising: 1) a first LA cavity pre-learning model, 2) dual-task model which further learns spatial relationship between LA geometry and scar patterns, and 3) fine-tuning on precise segmentation of the scar. Furthermore, we introduced an anatomy-aware spatially weighted loss that incorporates prior clinical knowledge by constraining scar predictions to anatomically plausible LA wall regions while mitigating annotation bias. Our preliminary results obtained on validation LGE volumes from LASCARQS public dataset after 5-fold cross validation, LA segmentation had Dice score of 0.94, LA scar segmentation achieved Dice score of 0.50, Hausdorff Distance of 11.84 mm, Average Surface Distance of 1.80 mm, outperforming only a one-stage scar segmentation with 0.49, 13.02 mm, 1.96 mm, repectively. By explicitly embedding clinical anatomical priors and diagnostic reasoning into deep learning, the proposed approach improved the accuracy and reliability of LA scar segmentation from LGE, revealing the importance of clinically informed model design.

Time-resolved segmentation of the aorta from 4D flow MRI is challenged by limited spatiotemporal resolution, complex aortic motion, and the absence of ground-truth labels across all cardiac phases. We present a deep learning framework that combines state-of-the-art static segmentation with unsupervised motion estimation to enforce temporal consistency without requiring phase-wise annotations. A multi-center dataset comprising 187 subjects scanned across different vendors and field-strengths was partitioned into internal training (N=100), internal testing (N=42), and external testing (N=45) cohorts. Expert reference segmentations were generated on all cardiac phases for testing sets only. 3D deeply supervised (DS) U-Net, 3D DenseNet-based (DN) U-Net and nnU-Net models were trained to segment the aorta at peak systole. Best performing model was then combined with an unsupervised VoxelMorph-based registration network to propagate the systolic mask throughout the cardiac cycle. Static and time-resolved segmentations were benchmarked against 2D phase-contrast (PC) MRI and 4D flow reference measurements.,For peak-systolic segmentation, nnU-Net demonstrated superior performance over alternative 3D architectures across both internal and external datasets (p<0.005), with robust generalization across scanners and disease status. Static and time-resolved segmentations yielded comparable correlations with 2D PC reference for velocities and net flow volumes (r≥0.91). However, time-resolved segmentation significantly improved agreement for time-averaged wall shear stress (r≥0.98 vs. r≥0.90 for static) and produced physiologically consistent ascending aortic distensibility and normalized volume change, exhibiting expected age-related variations (|r|≥0.62) and disease-related differences (p<0.05).,Our unsupervised framework delivers robust, label-efficient time-resolved aortic segmentation across heterogeneous multi-center 4D flow MRI data, enabling reliable hemodynamic and biomechanical quantification without costly phase-wise annotations.

L’évaluation de l’insuffisance cardiaque (IC) repose sur un ensemble de tests et d’examens parmi lesquels l’imagerie non invasive tient un rôle prépondérant. L’échocardiographie est l’examen d’imagerie cardiaque de première intention alors que l’IRM (imagerie par résonance magnétique) se positionne en seconde ligne dans l’IC, du fait qu’elle soit moins disponible, moins économique et plus complexe à mettre en œuvre. Une panoplie de mesures, dont la valeur diagnostique et pronostique dans l’IC a été démontrée, est rendue accessible par ces modalités d’imagerie, comprenant des indices de géométrie, volumétrie, fonction systolique dont la fraction d’éjection (FE) est référence et permet de stratifier le degré d’atteinte de l’IC, et vitesses de remplissage ventriculaire, à la fois dans le cœur gauche et le cœur droit. La déformation myocardique qui serait plus robuste que la FE conventionnelle pour déceler des atteintes subtiles, puisqu’elle est souvent altérée dans l’IC à FE préservée, est notamment de plus en plus reconnue. Bien que l’échocardiographie 3D améliore aujourd’hui la précision des mesures volumétriques par rapport aux techniques 2D, un avantage de l’IRM réside dans ses excellentes résolution spatiale et couverture anatomique, la propulsant ainsi comme la méthode de référence pour l’évaluation des volumes du ventricule gauche (VG) et donc de la FE VG, ainsi que de la masse VG, les volumes et la fonction du ventricule droit. L’IRM offre, par ailleurs, un moyen unique d’explorer les modifications structurelles du myocarde, telles que l’étude de la fibrose focale de remplacement (rétention tardive après injection d’un produit de contraste gadoliné), la fibrose diffuse interstitielle et le volume extracellulaire (cartographies T1 acquises pré- et post-injection), et la perfusion myocardique réalisée au premier passage du produit de contraste. Enfin, le scanner cardiaque est utilisé pour évaluer le score calcique coronaire et la présence de sténoses, après injection d’un produit de contraste afin d’exclure l’existence d’une cardiopathie ischémique, ainsi que les volumes cardiaques, les lésions d’hypoperfusion et la fibrose (de remplacement et interstitielle) myocardiques. Les nouvelles pistes d’exploration dans l’imagerie de l’IC incluent l’évaluation de la rigidité myocardique, l’architecture des fibres myocardiques ou la cartographie de la microcirculation coronaire en échographie ultra-rapide, la surcharge en fer myocardique, l’imagerie 3D+t plus exhaustive des flux sanguins intracardiaques et artériels, la rigidité de la paroi aortique et pulmonaire, ainsi que le tissu adipeux épicardique et enfin la spectroscopie moléculaire et métabolique par IRM. L’ensemble de ces techniques émergentes et prometteuses pourraient permettre d’améliorer la compréhension des mécanismes en jeu dans l’IC, y compris aux stades infracliniques.

OBJECTIVE:To study associations of newly proposed noninvasive central arterial wave reflection timing indices with age and reference indices of reflection magnitude, using superimposed MRI aortic flow and applanation tonometry carotid pressure waveforms. METHODS:Consecutively acquired MRI flow and tonometry pressure waveforms were superimposed in 113 volunteers [56 women, age: 43 ± 16 (19-81) years] free from overt cardiovascular disease except for hypertension, after registration and interpolation to averaged cardiac cycle duration using a custom interface to derive time to peak flow ( TQmax ) and pressure ( TPmax ). Conventional time to return of reflected pressure wave ( Ti ), augmentation index (AIx) and reflection magnitude (RM) were further measured and used as reference. RESULTS:TQmax occurred slightly earlier, whereas TPmax appeared later in the cardiac cycle with age, resulting in significant, independent age-related decrease in TQmax to Ti ( r  = -0.63, P  < 0.0001) and increase in TQmax to TPmax ( r  = 0.61, P  < 0.0001) delays. Such delays were further significantly and independently related to reference AIx ( Ti  -  TQmax : r  = -0.66, P  < 0.0001; TPmax  -  TQmax : r  = 0.78, P  < 0.0001) and RM ( Ti  -  TQmax : r  = -0.55, P  < 0.0001; TPmax  -  TQmax : r  = 0.76, P  < 0.0001). Correlations obtained with TPmax  -  TQmax were overall higher than those obtained with conventional Ti timing (age: r  = -0.52, P  < 0.0001; AIx: r  = -0.77, P  < 0.0001; RM: r  = -0.42, P  < 0.0001). CONCLUSION:Combination of noninvasive flow and pressure time domain waveforms was able to reliably capture central wave reflection timing indices as demonstrated by strong and independent associations with age and gold standard measures of reflection magnitude. Such timing, which was based on straightforward peaks detection, could be used to explore and better understand left ventricular-aortic coupling in cardiovascular conditions presenting increased reflections.

Recent literature has proposed various methods for automatic segmentation of the thoracic aorta in 4D flow MRI at the systolic peak, often evaluated on homogeneous or single-center cohorts. In this study, we compared the performance of three state-of-the-art deep learning models, including the nn-UNet, on heterogeneous 4D flow MRI data acquired using two MRI scanners, two field strengths (1.5T and 3T), across two centers, including both healthy volunteers and patients with aortic diseases. The dataset comprised 143 individuals (55 women, age 57 ± 16 years: healthy = 55, type 2 diabetes = 30, myocardial infarction = 33, ascending aorta aneurysm = 25). Reference annotations (GT) were created in 3D using validated software (Mimosa, Sorbonne Université) on systolic peak angiograms from 4D flow MRI. The dataset was split into training (n = 100) and evaluation subsets (n = 43). The models were assessed based on Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and ascending (AAo) and descending (DAo) aorta mean and maximum diameters. The nn-UNet achieved the highest performances (DSC = 0.88 ± 0.05, ASSD = 1.52 ± 0.96 mm, AAo and DAo mean and max diameters Pearson correlation with GT r ≥ 0.97). While the nn-UNet demonstrated superior accuracy, all three models delivered competitive segmentation results, suitable for aortic morphology assessment.

Atrial fibrillation (AF) is characterized by rapid and irregular contraction of the left atrium (LA). Impacting LA haemodynamics, this increases the risk of thrombi development and stroke. Flow conditions preceding stroke in these patients are not well defined, partly due the limited resolution of 4D flow magnetic resonance imaging (MRI). In this study, we combine a high-resolution computed tomography (CT) LA reconstruction with motion and pulmonary inflows from 4D flow MRI to create a novel multimodal computational fluid dynamics (CFD) model, applying it to five AF patients imaged in sinus rhythm (24 ± 39 days between acquisitions). The dynamic model was compared with a rigid wall equivalent and the main flow structures were validated with 4D flow MRI. Point-by-point absolute differences between the velocity fields showed moderate differences given the sensitivity to registration. The rigid wall model significantly underestimated LA time-averaged wall shear stress (TAWSS) (p = 0.02) and oscillatory shear index (OSI) (p = 0.02) compared to the morphing model. Similarly, in the left atrial appendage (LAA), TAWSS (p = 0.003) and OSI (p < 0.001) were further underestimated. The morphing model yielded a more accurate mitral valve waveform and showed low TAWSS and high OSI in the LAA, both associated with thrombus formation. We also observed a positive correlation between indexed LA volume and endothelial cell activation potential (ECAP) (R2 = 0.83), as well as LAA volume and LAA OSI (R2 = 0.70). This work demonstrates the importance of LA motion in modelling LAA flow. Assessed in larger cohorts, LAA haemodynamic analysis may be beneficial to refine stroke risk assessment for AF.

Aims:Feature tracking (FT) is increasingly used on dynamic cardiac magnetic resonance (CMR) images for myocardial strain evaluation but often requires manual initialization, which is tedious and source of variability, especially on the challenging long-axis (LAX) images. Accordingly, we designed a pipeline combining deep learning (DL) with FT for left ventricular (LV) and left atrial (LA) longitudinal myocardial strain estimation. Methods and results:We studied a multivendor database of 684 individuals divided into: training = 845, tuning = 281, and testing = 116 LAX-CMR cine 2- and/or 4-chamber views. Images were centre cropped. Then, a 2D- and 3D-ResUnet, which considers time as the third dimension, were designed for LV/LA segmentation and used to (i) estimate LV and LA strains (Full 2D-/3D-DL) and (ii) initialize an FT algorithm and further derive LV and LA strains (FT-initialized by 2D-/3D-DL). Left ventricular and LA contours and strain peaks were compared against reference standard (RS) measures performed by an expert using a semiautomated software. Intraclass-correlation-coefficient (ICC) was used to study reproducibility. 3D-DL outperformed 2D-DL segmentation (Dice-scores: 0.94 ± 0.02 vs. 0.90 ± 0.09, P = 0.002) and was stable across vendors, field strengths and imaging views. The added value of combining DL with FT was revealed by higher correlations and lower Bland-Altman biases against RS for FT initialized by 3D-DL strains (r ≥ 0.91, |mean-bias|≤0.65%) than for full 3D-DL strains (r ≤ 0.80, |mean-bias|<3.07%). Semiautomated human vs. FT initialized by 3D-DL (ICC ≥ 0.76) and inter-human strain reproducibility was equivalent. Conclusion:Generalizable DL-based LV and LA segmentation on LAX-CMR images was proposed. Its combination with FT resulted in fully automated and reliable LV and LA strain measures, reaching human reproducibility.

Atrial fibrillation (AF), which is associated with fibrosis formation within the left atrial (LA) myocardium, induces haemodynamic changes in the LA cavity which increases the risk of thrombi development and subsequent stroke. Computational fluid dynamics (CFD) can be used to analyse LA flow in high spatial and temporal resolution. Late gadolinium enhancement (LGE) is an MRI technique used to evaluate the extent of fibrosis in the myocardium. Here we analyse 16 patients with a dynamic LA CFD model where patient-specific geometries are derived from CT. 4D flow MRI provides the inlet flows and LA wall motion for the model. We then compare the haemodynamics with the spatial distribution of fibrosis from LGE data. The results show that on a global level, greater fibrotic burden is correlated with decreased time-averaged wall shear stress (TAWSS, p = 0.045), increased relative residence time (RRT, p = 0.003) and endothelial cell activation potential (ECAP, p = 0.001). These results suggest that atrial wall fibrosis infiltration and extent in AF is associated with disturbed blood flow haemodynamics.

4D flow MRI is increasingly used for research exploration of patients with mitral regurgitation (MR), as it provides extensive time and space-resolved coverage of left atrial (LA) blood flow. Since LA is a major end-stage target chamber in MR, we aimed at designing 4D flow processing pipeline to quantify LA inner hemodynamics. Such pipeline comprised a segmentation step based on feature tracking (FT) and computation of flow indices such as high velocity weighed area and vorticity magnitude. FT contours were also used to calculate mitral annulus angulation. Results on 21 MR patients revealed that LA flow characteristics were associated to echocardiographic or 2D MRI regurgitant volume, while geometry-related mitral annulus angulation was associated to peak oxygen consumption, which is the standard measure of exercise capacity linked to prognosis in MR patients. Interestingly, none of the conventional strain indices were significantly correlated to regurgitant volume or to exercise capacity.

Mitral regurgitation (MR) is associated with morphological and functional alterations of left atrium (LA) and ventricle (LV), possibly inducing LA-LV misalignment. We aimed to: (1) characterize angulation between LA and mitral annulus from conventional cine MRI data and feature-tracking (FT) contours, (2) assess their associations with functional capacity in MR patients, as assessed by oxygen consumption (peak-VO2) and minute ventilation to carbon dioxide production (VE/VCO2) slope, in comparison with MRI LA/LV strain indices. Thirty-two asymptomatic primary MR patients (56 [40; 66] years, 12 women) underwent cardiac MRI resulting in LA/LV conventional FT-derived strain indices. Then, end-diastolic angles were derived from FT LA contours: (1) α, centered on the LA centre of mass and defined by mitral valve extremities, (2) γ, centered on the mitral ring anterior/lateral side, and defined by LA centre and the other extremity of the mitral ring. Cardiopulmonary exercise testing with simultaneous echocardiography were also performed; peak-VO2 and VE/VCO2 slope were measured. While peak-VO2 and VE/VCO2 slope were not correlated to LA/LV strains, they were significantly associated with angles (α: r = 0.50, p = 0.003 and r = - 0.52, p = 0.003; γ: r = - 0.53, p = 0.002 and r = 0.52, p = 0.003; respectively), independently of age and gender (R2 ≥ 0.29, p ≤ 0.03). In primary MR, the new LA/mitral annulus angles, computed directly from standard-of-care MRI, are better correlated to exercise tolerance than conventional LA/LV strain.

Feature tracking (FT) is increasingly used on dynamic magnetic resonance images for myocardial strain evaluation, but often requires manual initialization of heart chambers, which is tedious and source of variability, especially on the challenging long axis images. Accordingly, we combined a deep learning (DL) approach with FT (DL-FT) to provide fully automated time-resolved left ventricular (LV) and atrial (LA) delineation and strain analysis. This approach was tested on a multi-center and multi-vendor database of 684 healthy controls and patients. DL-initialization achieved Dice scores of 0.89 +/- 0.11 for LV endocardium, 0.93 +/- 0.07 for LV epicardium and 0.89 +/- 0.10 for LA on the testing set of 108 datasets (2-and 4-chambers). LA and LV DL-FT strain peaks were highly associated with expert strains as revealed by correlation coefficients=0.96 for LV and >= 0.70 for LA and mean Bland-Altman biases=0.62% for LV and <1% for LA. Results also revealed stability of our approach over vendors and field strengths.

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.

Abstract Background Coupling between left ventricle (LV) and left atrium (LA) plays a central role in the process of cardiac remodeling during aging and development of cardiac disease. The hydraulic force (HyF) is related to variation in size between LV and LA. The objectives of this study were to: (1) derive an estimate of left atrioventricular HyF using cine- Magnetic Resonance Imaging (MRI) in healthy subjects with a wide age range, and (2) study its relationship with age and conventional diastolic function parameters, as estimated by reference echocardiography. Methods We studied 119 healthy volunteers (mean age 44 ± 17 years, 58 women) who underwent Doppler echocardiography and MRI on the same day. Conventional transmitral flow early (E) and late (A) LV filling peak velocities as well as mitral annulus diastolic longitudinal peak velocity (E’) were derived from echocardiography. MRI cine SSFP images in longitudinal two and four chamber views were acquired, and analyzed using feature tracking (FT) software. In addition to conventional LV and LA strain measurements, FT-derived LV and LA contours were further used to calculate chamber cross-sectional areas. HyF was approximated as the difference between the LV and LA maximal cross-sectional areas in the diastasis phase corresponding to the lowest LV-LA pressure gradient. Univariate and multivariate analyses while adjusting for appropriate variables were used to study the associations between HyF and age as well as diastolic function and strain indices. Results HyF decreased significantly with age (R²=0.34, p < 0.0001). In addition, HyF was significantly associated with conventional indices of diastolic function and LA strain: E/A: R²=0.24, p < 0.0001; E’: R²=0.24, p < 0.0001; E/E’: R²=0.12, p = 0.0004; LA conduit longitudinal strain: R²=0.27, p < 0.0001. In multivariate analysis, associations with E/A (R2 = 0.39, p = 0.03) and LA conduit strain (R2 = 0.37, p = 0.02) remained significant after adjustment for age, sex, and body mass index. Conclusions HyF, estimated using FT contours, which are primarily used to quantify LV/LA strain on standard cardiac cine MRI, varied significantly with age in association with subclinical changes in ventricular filling. Its usefulness in cohorts of patients with left heart disease to detect LV-LA uncoupling remains to be evaluated.

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.

Background:It has been shown that increased aortic stiffness is related to type-2 diabetes (T2D) which is considered as a risk factor for cardiovascular disease. Among other risk factors is epicardial adipose tissue (EAT) which is increased in T2D and is a relevant biomarker of metabolic severity and adverse outcome.Purpose:To assess aortic flow parameters in T2D patients as compared to healthy individuals and to evaluate their associations with EAT accumulation as an index of cardiometabolic severity in T2D patients.Materials and methods:Thirty-six T2D patients as well as 29 healthy controls matched by age and sex were included in this study. Participants had cardiac and aortic MRI exams at 1.5 T. Imaging sequences included cine SSFP for left ventricle (LV) function and EAT assessment and aortic cine and phase-contrast imaging for strain and flow parameters quantification.Results:In this study, we found LV phenotype to be characterized by concentric remodeling with decreased stroke volume index despite global LV mass within a normal range. EAT was increased in T2D patients compared to controls (p<0.0001). Moreover, EAT, a biomarker of metabolic severity, was negatively correlated to ascending aortic (AA) distensibility (p=0.048) and positively to the normalized backward flow volume (p=0.001). These relationships remained significant after further adjustment for age, sex and central mean blood pressure. In a multivariate model, presence/absence of T2D and AA normalized backward flow (BF) to forward flow (FF) volumes ratio are both significant and independent correlates of EAT.Conclusion:In our study, aortic stiffness as depicted by an increased backward flow volume and decreased distensibility seems to be related to EAT volume in T2D patients. This observation should be confirmed in the future on a larger population while considering additional biomarkers specific to inflammation and using a longitudinal prospective study design.

Objectives:Non-invasive assessment of aortic hemodynamics using four dimensional (4D) flow magnetic resonance imaging (MRI) provides new information on blood flow patterns and wall shear stress (WSS). Aortic valve stenosis (AS) and/or bicuspid aortic valves (BAV) are associated with altered aortic flow patterns and elevated WSS. Aim of this study was to investigate changes in aortic hemodynamics over time in patients with AS and/or BAV with or without aortic valve replacement. Methods:We rescheduled 20 patients for a second 4D flow MRI examination, whose first examination was at least 3 years prior. A total of 7 patients received an aortic valve replacement between baseline and follow up examination (=operated group = OP group). Aortic flow patterns (helicity/vorticity) were assessed using a semi-quantitative grading approach from 0 to 3, flow volumes were evaluated in 9 planes, WSS in 18 and peak velocity in 3 areas. Results:While most patients had vortical and/or helical flow formations within the aorta, there was no significant change over time. Ascending aortic forward flow volumes were significantly lower in the OP group than in the NOP group at baseline (NOP 69.3 mL ± 14.2 mL vs. OP 55.3 mL ± 1.9 mL p = 0.029). WSS in the outer ascending aorta was significantly higher in the OP group than in the NOP group at baseline (NOP 0.6 ± 0.2 N/m2 vs. OP 0.8 ± 0.2 N/m2, p = 0.008). Peak velocity decreased from baseline to follow up in the aortic arch only in the OP group (1.6 ± 0.6 m/s vs. 1.2 ± 0.3 m/s, p = 0.018). Conclusion:Aortic valve replacement influences aortic hemodynamics. The parameters improve after surgery.