Low and Oscillatory Wall Shear Stress Is Not Related to Aortic Dilation in Patients With Bicuspid Aortic Valve: A Time-Resolved 3-Dimensional Phase-Contrast Magnetic Resonance Imaging Study

Changes in aorta hemodynamics in Left-Right Type 1 bicuspid aortic valve patients after replacement with bioprosthetic valves: An in-silico study

Bicuspid aortic valve (BAV) is the most common cardiac congenital abnormality with a high rate of concomitant aortic valve and ascending aorta (AAo) pathologic changes throughout the patient's lifetime. The etiology of BAV-related aortopathy was historically believed to be genetic. However, recent studies theorize that adverse hemodynamics secondary to BAVs also contribute to aortopathy, but their precise role, specifically, that of wall shear stress (WSS) magnitude and directionality remains controversial. Moreover, the primary therapeutic option for BAV patients is aortic valve replacement (AVR), but the role of improved post-AVR hemodynamics on aortopathy progression is also not well-understood. To address these issues, this study employs a computational fluid dynamics model to simulate personalized AAo hemodynamics before and after TAVR for a small cohort of 6 Left-Right fused BAV patients. Regional distributions of five hemodynamic metrics, namely, time-averaged wall shear stress (TAWSS) and oscillating shear index (OSI), divergence of wall shear (DWSS), helicity flux integral & endothelial cell activation potential (ECAP), which are hypothesized to be associated with potential aortic injury are computed in the root, proximal and distal ascending aorta. BAVs are characterized by strong, eccentric jets, with peak velocities exceeding 4 m/s and axially circulating flow away from the jets. Such conditions result in focused WSS loading along jet attachment regions on the lumen boundary and weaker, oscillating WSS on other regions. The jet attachment regions also show alternating streaks of positive and negative DWSS, which may increase risk for local tissue stretching. Large WSS magnitudes, strong helical flows and circumferential WSS have been previously implicated in the progression of BAV aortopathy. Post-intervention hemodynamics exhibit weaker, less eccentric jets. Significant reductions are observed in flow helicity, TAWSS and DWSS in localized regions of the proximal AAo. On the other hand, OSI increases post-intervention and ECAP is observed to be low in both pre- and post-intervention scenarios, although significant increases are also observed in this ECAP. These results indicate a significant alleviation of pathological hemodynamics post AVR.

Virtual Planning and Patient-Specific Graft Design for Aortic Repairs

PURPOSE

Patients presenting with coarctation of the aorta (CoA) may also suffer from co-existing transverse arch hypoplasia (TAH). Depending on the risks associated with the surgery and the severity of TAH, clinicians may decide to repair only CoA, and monitor the TAH to see if it improves as the patient grows. While acutely successful, eventually hemodynamics may become suboptimal if TAH is left untreated. The objective of this work aims to develop a patient-specific surgical planning framework for predicting and assessing postoperative outcomes of simple CoA repair and comprehensive repair of CoA and TAH.

METHODS

The surgical planning framework consisted of virtual clamp placement, stenosis resection, and design and optimization of patient-specific aortic grafts that involved geometrical modeling of the graft and computational fluid dynamics (CFD) simulation for evaluating various surgical plans. Time-dependent CFD simulations were performed using Windkessel boundary conditions at the outlets that were obtained from patient-specific non-invasive pressure and flow data to predict hemodynamics before and after the virtual repairs. We applied the proposed framework to investigate optimal repairs for six patients (n = 6) diagnosed with both CoA and TAH. Design optimization was performed by creating a combination of a tubular graft and a waterslide patch to reconstruct the aortic arch. The surfaces of the designed graft were parameterized to optimize the shape.

RESULTS

Peak systolic pressure drop (PSPD) and time-averaged wall shear stress (TAWSS) were used as performance metrics to evaluate surgical outcomes of various graft designs and implantation. The average PSPD improvements were 28% and 44% after the isolated CoA repair and comprehensive repair, respectively. Maximum values of TAWSS were decreased by 60% after CoA repair and further improved by 22% after the comprehensive repair. The oscillatory shear index was calculated and the values were confirmed to be in the normal range after the repairs.

CONCLUSION

The results showed that the comprehensive repair outperforms the simple CoA repair and may be more advantageous in the long term in some patients. We demonstrated that the surgical planning and patient-specific flow simulations could potentially affect the selection and outcomes of aorta repairs.

Machine Learning-Based Segmentation of the Thoracic Aorta with Congenital Valve Disease Using MRI

Subjects with bicuspid aortic valves (BAV) are at risk of developing valve dysfunction and need regular clinical imaging surveillance. Management of BAV involves manual and time-consuming segmentation of the aorta for assessing left ventricular function, jet velocity, gradient, shear stress, and valve area with aortic valve stenosis. This paper aims to employ machine learning-based (ML) segmentation as a potential for improved BAV assessment and reducing manual bias. The focus is on quantifying the relationship between valve morphology and vortical structures, and analyzing how valve morphology influences the aorta's susceptibility to shear stress that may lead to valve incompetence. The ML-based segmentation that is employed is trained on whole-body Computed Tomography (CT). Magnetic Resonance Imaging (MRI) is acquired from six subjects, three with tricuspid aortic valves (TAV) and three functionally BAV, with right-left leaflet fusion. These are used for segmentation of the cardiovascular system and delineation of four-dimensional phase-contrast magnetic resonance imaging (4D-PCMRI) for quantification of vortical structures and wall shear stress. The ML-based segmentation model exhibits a high Dice score (0.86) for the heart organ, indicating a robust segmentation. However, the Dice score for the thoracic aorta is comparatively poor (0.72). It is found that wall shear stress is predominantly symmetric in TAVs. BAVs exhibit highly asymmetric wall shear stress, with the region opposite the fused coronary leaflets experiencing elevated tangential wall shear stress. This is due to the higher tangential velocity explained by helical flow, proximally of the sinutubal junction of the ascending aorta. ML-based segmentation not only reduces the runtime of assessing the hemodynamic effectiveness, but also identifies the significance of the tangential wall shear stress in addition to the axial wall shear stress that may lead to the progression of valve incompetence in BAVs, which could guide potential adjustments in surgical interventions.

A clinician's guide to understanding aortic 4D flow MRI

Four-dimensional flow magnetic resonance imaging is an emerging technique which may play a role in diagnosis and risk-stratification of aortic disease. Some knowledge of flow dynamics and related parameters is necessary to understand and apply this technique in clinical workflows. The purpose of the current review is to provide a guide for clinicians to the basics of flow imaging, frequently used flow-related parameters, and their relevance in the context of aortic disease.Clinical relevance statement Understanding normal and abnormal aortic flow could improve clinical care in patients with aortic disease.

Controlled Comparison of Simulated Hemodynamics Across Tricuspid and Bicuspid Aortic Valves

Bicuspid aortic valve is the most common congenital heart defect, affecting 1-2% of the global population. Patients with bicuspid valves frequently develop dilation and aneurysms of the ascending aorta. Both hemodynamic and genetic factors are believed to contribute to dilation, yet the precise mechanism underlying this progression remains under debate. Controlled comparisons of hemodynamics in patients with different forms of bicuspid valve disease are challenging because of confounding factors, and simulations offer the opportunity for direct and systematic comparisons. Using fluid-structure interaction simulations, we simulate flows through multiple aortic valve models in a patient-specific geometry. The aortic geometry is based on a healthy patient with no known aortic or valvular disease, which allows us to isolate the hemodynamic consequences of changes to the valve alone. Four fully-passive, elastic model valves are studied: a tricuspid valve and bicuspid valves with fusion of the left- and right-, right- and non-, and non- and left-coronary cusps. The resulting tricuspid flow is relatively uniform, with little secondary or reverse flow, and little to no pressure gradient across the valve. The bicuspid cases show localized jets of forward flow, excess streamwise momentum, elevated secondary and reverse flow, and clinically significant levels of stenosis. Localized high flow rates correspond to locations of dilation observed in patients, with the location related to which valve cusps are fused. Thus, the simulations support the hypothesis that chronic exposure to high local flow contributes to localized dilation and aneurysm formation.

Machine learning for the automatic assessment of aortic rotational flow and wall shear stress from 4D flow cardiac magnetic resonance imaging

OBJECTIVE

Three-dimensional (3D) time-resolved phase-contrast cardiac magnetic resonance (4D flow CMR) allows for unparalleled quantification of blood velocity. Despite established potential in aortic diseases, the analysis is time-consuming and requires expert knowledge, hindering clinical application. The present research aimed to develop and test a fully automatic machine learning-based pipeline for aortic 4D flow CMR analysis.

METHODS

Four hundred and four subjects were prospectively included. Ground-truth to train the algorithms was generated by experts. The cohort was divided into training (323 patients) and testing (81) sets and used to train and test a 3D nnU-Net for segmentation and a Deep Q-Network algorithm for landmark detection. In-plane (IRF) and through-plane (SFRR) rotational flow descriptors and axial and circumferential wall shear stress (WSS) were computed at ten planes covering the ascending aorta and arch.

RESULTS

Automatic aortic segmentation resulted in a median Dice score (DS) of 0.949 and average symmetric surface distance of 0.839 (0.632-1.071) mm, comparable with the state of the art. Aortic landmarks were located with a precision comparable with experts in the sinotubular junction and first and third supra-aortic vessels (p = 0.513, 0.592 and 0.905, respectively) but with lower precision in the pulmonary bifurcation (p = 0.028), resulting in precise localisation of analysis planes. Automatic flow assessment showed excellent (ICC > 0.9) agreement with manual quantification of SFRR and good-to-excellent agreement (ICC > 0.75) in the measurement of IRF and axial and circumferential WSS.

CONCLUSION

Fully automatic analysis of complex aortic flow dynamics from 4D flow CMR is feasible. Its implementation could foster the clinical use of 4D flow CMR.

KEY POINTS

• 4D flow CMR allows for unparalleled aortic blood flow analysis but requires aortic segmentation and anatomical landmark identification, which are time-consuming, limiting 4D flow CMR widespread use. • A fully automatic machine learning pipeline for aortic 4D flow CMR analysis was trained with data of 323 patients and tested in 81 patients, ensuring a balanced distribution of aneurysm aetiologies. • Automatic assessment of complex flow characteristics such as rotational flow and wall shear stress showed good-to-excellent agreement with manual quantification.

Fully Three-Dimensional Hemodynamic Characterization of Altered Blood Flow in Bicuspid Aortic Valve Patients With Respect to Aortic Dilatation: A Finite Element Approach

Background and Purpose

Prognostic models based on cardiovascular hemodynamic parameters may bring new information for an early assessment of patients with bicuspid aortic valve (BAV), playing a key role in reducing the long-term risk of cardiovascular events. This work quantifies several three-dimensional hemodynamic parameters in different patients with BAV and ranks their relationships with aortic diameter.

Materials and Methods

Using 4D-flow CMR data of 74 patients with BAV (49 right-left and 25 right-non-coronary) and 48 healthy volunteers, aortic 3D maps of seventeen 17 different hemodynamic parameters were quantified along the thoracic aorta. Patients with BAV were divided into two morphotype categories, BAV-Non-AAoD (where we include 18 non-dilated patients and 7 root-dilated patients) and BAV-AAoD (where we include the 49 patients with dilatation of the ascending aorta). Differences between volunteers and patients were evaluated using MANOVA with Pillai's trace statistic, Mann–Whitney U test, ROC curves, and minimum redundancy maximum relevance algorithm. Spearman's correlation was used to correlate the dilation with each hemodynamic parameter.

Results

The flow eccentricity, backward velocity, velocity angle, regurgitation fraction, circumferential wall shear stress, axial vorticity, and axial circulation allowed to discriminate between volunteers and patients with BAV, even in the absence of dilation. In patients with BAV, the diameter presented a strong correlation (> |+/−0.7|) with the forward velocity and velocity angle, and a good correlation (> |+/−0.5|) with regurgitation fraction, wall shear stress, wall shear stress axial, and vorticity, also for morphotypes and phenotypes, some of them are correlated with the diameter. The velocity angle proved to be an excellent biomarker in the differentiation between volunteers and patients with BAV, BAV morphotypes, and BAV phenotypes, with an area under the curve bigger than 0.90, and higher predictor important scores.

Conclusions

Through the application of a novel 3D quantification method, hemodynamic parameters related to flow direction, such as flow eccentricity, velocity angle, and regurgitation fraction, presented the best relationships with a local diameter and effectively differentiated patients with BAV from healthy volunteers.

Identification of hemodynamic biomarkers for bicuspid aortic valve induced aortic dilation using machine learning

Recent advances in medical imaging have confirmed the presence of altered hemodynamics in bicuspid aortic valve (BAV) patients. Therefore, there is a need for new hemodynamic biomarkers to refine disease monitoring and improve patient risk stratification. This research aims to analyze and extract multiple correlation patterns of hemodynamic parameters from 4D Flow MRI data and find which parameters allow an accurate classification between healthy volunteers (HV) and BAV patients with dilated and non-dilated ascending aorta using machine learning. Sixteen hemodynamic parameters were calculated in the ascending aorta (AAo) and aortic arch (AArch) at peak systole from 4D Flow MRI. We used sequential forward selection (SFS) and principal component analysis (PCA) as feature selection algorithms. Then, eleven machine-learning classifiers were implemented to separate HV and BAV patients (non- and dilated ascending aorta). Multiple correlation patterns from hemodynamic parameters were extracted using hierarchical clustering. The linear discriminant analysis and random forest are the best performing classifiers, using five hemodynamic parameters selected with SFS (velocity angle, forward velocity, vorticity, and backward velocity in AAo; and helicity density in AArch) a 96.31 ± 1.76% and 96.00 ± 0.83% accuracy, respectively. Hierarchical clustering revealed three groups of correlated features. According to this analysis, we observed that features selected by SFS have a better performance than those selected by PCA because the five selected parameters were distributed according to 3 different clusters. Based on the proposed method, we concluded that the feature selection method found five potentially hemodynamic biomarkers related to this disease.

Valve disease and aortopathy associations of bicuspid aortic valve phenotypes differ between men and women

OBJECTIVE

Determine whether associations between bicuspid aortic valve (BAV) phenotypes, valve disease and aortopathy differ between sexes.

METHODS

1045 patients with BAV (76.0% men, n=794) from two surgical centres were included in this cross-sectional study. Valve phenotype was classified intraoperatively as right-left (RL), right-non-coronary (RN), left-non-coronary (LN) or 2-sinus BAV. Echocardiography was used to determine type and degree of valve disease, and aortic dimensions. Aortic dilatation was defined as diameter ≥4.5 cm.

RESULTS

RL was the most common phenotype (73.6%), followed by RN (16.2%), 2-sinus BAV (9.2%) and LN (1.1%), with no difference in phenotype distribution between men and women (p=0.634). Aortic valve insufficiency (AI) prevalence differed significantly with valve phenotype in men (p=0.047), with RL and LN having the highest prevalence (34.1% and 44.0%, respectively). In women, RN had a higher proportion of AI than RL (21.3% vs 7.3%, p=0.017). Men with RL had larger root dimensions, in particular at the sinus (mean difference 0.24 cm compared with RN, p=0.002). Men with 2-sinus BAV had the highest prevalence of root phenotype dilatation (7.0%, other phenotypes ≤2.3%, p=0.031), whereas women with 2-sinus BAV did not have root dilatation and smaller sinus dimensions (mean difference: 0.35 cm compared with RL, p=0.021). Aortic root segments were larger in men with AI compared with aortic stenosis (sinus mean difference: 0.40 cm, p<0.001). The difference was even larger in women (mean difference: 0.78 cm, p<0.001), and women with AI also had larger tubular segments (mean difference: 0.61 cm, p=0.001).

CONCLUSIONS

There are significant sex differences in clinical associations of BAV phenotypes, which should be considered in further studies on the role of phenotypes in individualised patient management.

Leaflet fusion length is associated with aortic dilation and flow alterations in non-dysfunctional bicuspid aortic valve

OBJECTIVE

Bicuspid aortic valve (BAV), the most common congenital valve defect, is associated with increased risk of aortic dilation and related complications; however, current risk assessment is not effective. Most of BAV have three leaflets with a fusion between two of them of variable length. This study aimed to ascertain whether the extent of leaflet fusion (often called raphe) is related to aortic dilation and flow abnormalities in BAV with no significant valvular dysfunction.

METHODS

One hundred and twenty BAV patients with no significant valvular dysfunction or history of surgical repair or aortic valve replacement were consecutively and prospectively enrolled (September 2014-October 2018). Cardiac magnetic resonance protocol included a 4D flow sequence for haemodynamic assessment. Moreover, a stack of double-oblique cine images of the aortic valve were used to quantify fusion length (in systole) and leaflet length (diastole). Inter- and intra-observer reproducibility was tested in 30 randomly selected patients.

RESULTS

Aortic valve leaflet fusion was measurable in 112 of 120 (93%) cases with good reproducibility (ICC = 0.826). Fusion length varied greatly (range: 2.3-15.4 mm; mean: 7.8 ± 3.2 mm). After correction for demographic and clinical conditions, fusion length was independently associated with diameter and z-score at the sinus of Valsalva (p = 0.002 and p = 0.002, respectively) and ascending aorta (p = 0.028 and p = 0.046). Fusion length was positively related to flow asymmetry, vortices and circumferential wall shear stress, thereby possibly providing a pathophysiological link with aortic dilation.

CONCLUSIONS

Aortic valve fusion length is related to aortic dilation and flow abnormalities in BAV patients.

KEY POINTS

• The length of the fusion between leaflets in non-dysfunctional bicuspid aortic valves varies substantially and can be reliably measured by cine CMR. • Aortic valve leaflet fusion length is independently related to aortic sinus and ascending aorta diameter. • Increased flow asymmetry, circumferential wall shear stress and presence of vortices are positively related to aortic valve leaflet fusion length.

4D flow MRI: impact of region of interest size, angulation and spatial resolution on aortic flow assessment

Objectives.In cardiovascular magnetic resonance, the 3D time-resolved phase-contrast technique, also known as 4D flow, is gaining increasing attention due to applications that exploit three-directional velocity encoding throughout the cardiac cycle. Blood flow volume assessment usually requires an expert to draw regions of interest (ROI) around the vessel cross section, whereas the errors involved in this estimation have not been thoroughly investigated. Our objective is to quantify the influence of ROI sizing, angulation and spatial resolution of the reconstructed plane employed in blood flow measurements using 4D flow.Approach.Three circular ROIs were drawn around the ascending, arch and descending aorta of healthy volunteers (n= 27) and patients with a dilated ascending aorta or bicuspid valve (n= 37). We applied systematic changes of ROI diameter (up to ±10%), tilt angle (up to ±25°) and spatial resolution (from 0.25 to 2 mm) of the reconstructed oblique planes, calculating the effects on net, forward and backward blood flow volumes.Main results.Patients had a larger ascending aorta than healthy volunteers with similar ages and male sex proportion (60 ± 15 y.o. vs 58 ± 16 y.o. and 84% vs 70%, respectively). Higher forward and backward flow volumes were observed in the ascending aorta and the aortic arch of the patients with respect to controls (p< 0.001), whereas net volumes were similar: 74.0 ± 20.8 ml versus 75.7 ± 21.8 ml (p= 0.37), respectively. The ascending aorta was the most sensitive to ROI modifications. Changes of ±10% in the ROI diameter and ±25° in tilt angles produced flow volume differences of up to 9 ml (10%) and 18 ml (15%) in controls and patients, respectively. Modifying the reconstructed planes spatial resolution produced flow volume changes below 2 ml.Significance.Since the setting of the ROI size and plane angle could produce errors that represent up to 20% of the forward and/or backward aortic flow volume, a good standardization for vessel segmentation and plane positioning is desirable.

Fluid-structure interaction simulations outperform computational fluid dynamics in the description of thoracic aorta haemodynamics and in the differentiation of progressive dilation in Marfan syndrome patients

Abnormal fluid dynamics at the ascending aorta may be at the origin of aortic aneurysms. This study was aimed at comparing the performance of computational fluid dynamics (CFD) and fluid-structure interaction (FSI) simulations against four-dimensional (4D) flow magnetic resonance imaging (MRI) data; and to assess the capacity of advanced fluid dynamics markers to stratify aneurysm progression risk. Eight Marfan syndrome (MFS) patients, four with stable and four with dilating aneurysms of the proximal aorta, and four healthy controls were studied. FSI and CFD simulations were performed with MRI-derived geometry, inlet velocity field and Young's modulus. Flow displacement, jet angle and maximum velocity evaluated from FSI and CFD simulations were compared to 4D flow MRI data. A dimensionless parameter, the shear stress ratio (SSR), was evaluated from FSI and CFD simulations and assessed as potential correlate of aneurysm progression. FSI simulations successfully matched MRI data regarding descending to ascending aorta flow rates (R 2 = 0.92) and pulse wave velocity (R 2 = 0.99). Compared to CFD, FSI simulations showed significantly lower percentage errors in ascending and descending aorta in flow displacement (-46% ascending, -41% descending), jet angle (-28% ascending, -50% descending) and maximum velocity (-37% ascending, -34% descending) with respect to 4D flow MRI. FSI- but not CFD-derived SSR differentiated between stable and dilating MFS patients. Fluid dynamic simulations of the thoracic aorta require fluid-solid interaction to properly reproduce complex haemodynamics. FSI- but not CFD-derived SSR could help stratifying MFS patients.