Comparison of Four-Dimensional Magnetic Resonance Imaging Analysis of Left Ventricular Fluid Dynamics and Energetics in Ischemic and Restrictive Cardiomyopathies.

BACKGROUND: Time-resolved three-directional velocity-encoded (4D flow) magnetic resonance imaging (MRI) enables the quantification of left ventricular (LV) intracavitary fluid dynamics and energetics, providing mechanistic insight into LV dysfunctions. Before becoming a support to diagnosis and patient stratification, this analysis should prove capable of discriminating between clearly different LV derangements. PURPOSE: To investigate the potential of 4D flow in identifying fluid dynamic and energetics derangements in ischemic and restrictive LV cardiomyopathies. STUDY TYPE: Prospective observational study. POPULATION: Ten patients with post-ischemic cardiomyopathy (ICM), 10 patients with cardiac light-chain cardiac amyloidosis (AL-CA), and 10 healthy controls were included. FIELD STRENGTH/SEQUENCE: 1.5 T/balanced steady-state free precession cine and 4D flow sequences. ASSESSMENT: Flow was divided into four components: direct flow (DF), retained inflow, delayed ejection flow, and residual volume (RV). Demographics, LV morphology, flow components, global and regional energetics (volume-normalized kinetic energy [KEV ] and viscous energy loss [ELV ]), and pressure-derived hemodynamic force (HDF) were compared between the three groups. STATISTICAL TESTS: Intergroup differences in flow components were tested by one-way analysis of variance (ANOVA); differences in energetic variables and peak HDF were tested by two-way ANOVA. A P-value of <0.05 was considered significant. RESULTS: ICM patients exhibited the following statistically significant alterations vs. controls: reduced KEV , mostly in the basal region, in systole (-44%) and in diastole (-37%); altered flow components, with reduced DF (-33%) and increased RV (+26%); and reduced basal-apical HDF component on average by 63% at peak systole. AL-CA patients exhibited the following alterations vs. controls: significantly reduced KEV at the E-wave peak in the basal segment (-34%); albeit nonstatistically significant, increased peaks and altered time-course of the HDF basal-apical component in diastole and slightly reduced HDF components in systole. DATA CONCLUSION: The analysis of multiple 4D flow-derived parameters highlighted fluid dynamic alterations associated with systolic and diastolic dysfunctions in ICM and AL-CA patients, respectively. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 3.

The Role of Multiple Re-Entry Tears in Type B Aortic Dissection Progression: A Longitudinal Study Using a Controlled Swine Model.

PURPOSE: False lumen (FL) expansion often occurs in type B aortic dissection (TBAD) and has been associated with the presence of re-entry tears. This longitudinal study aims to elucidate the role of re-entry tears in the progression of TBAD using a controlled swine model, by assessing aortic hemodynamics through combined imaging and computational modeling. MATERIALS AND METHODS: A TBAD swine model with a primary entry tear at 7 cm distal to the left subclavian artery was created in a previous study. In the current study, reintervention was carried out in this swine model to induce 2 additional re-entry tears of approximately 5 mm in diameter. Computed tomography (CT) and 4-dimensional (4D) flow magnetic resonance imaging (MRI) scans were taken at multiple follow-ups before and after reintervention. Changes in aortic volume were measured on CT scans, and hemodynamic parameters were evaluated based on dynamic data acquired with 4D-flow MRI and computational fluid dynamics simulations incorporating all available in vivo data. RESULTS: Morphological analysis showed FL growth of 20% following the initial TBAD-growth stabilized after the creation of additional tears and eventually FL volume reduced by 6%. Increasing the number of re-entry tears from 1 to 2 caused flow redistribution, with the percentage of true lumen (TL) flow increasing from 56% to 78%; altered local velocities; reduced wall shear stress surrounding the tears; and led to a reduction in FL pressure and pressure difference between the 2 lumina. CONCLUSION: This study combined extensive in vivo imaging data with sophisticated computational methods to show that additional re-entry tears can alter dissection hemodynamics through redistribution of flow between the TL and FL. This helps to reduce FL pressure, which could potentially stabilize aortic growth and lead to reversal of FL expansion. This work provides a starting point for further study into the use of fenestration in controlling undesirable FL expansion. CLINICAL IMPACT: Aortic growth and false lumen (FL) patency are associated with the presence of re-entry tears in type B aortic dissection (TBAD) patients. Guidelines on how to treat re-entry tears are lacking, especially with regards to the control and prevention of FL expansion. Through a combined imagining and computational hemodynamics study of a controlled swine model, we found that increasing the number of re-entry tears reduced FL pressure and cross lumen pressure difference, potentially stabilising aortic growth and leading to FL reduction. Our findings provide a starting point for further study into the use of fenestration in controlling undesirable FL expansion.

A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography.

Feasibility assessment and planning of thoracic endovascular aortic repair (TEVAR) require computed tomography (CT)-based analysis of geometric aortic features to identify adequate landing zones (LZs) for endograft deployment. However, no consensus exists on how to take the necessary measurements from CT image data. We trained and applied a fully automated pipeline embedding a convolutional neural network (CNN), which feeds on 3D CT images to automatically segment the thoracic aorta, detects proximal landing zones (PLZs), and quantifies geometric features that are relevant for TEVAR planning. For 465 CT scans, the thoracic aorta and pulmonary arteries were manually segmented; 395 randomly selected scans with the corresponding ground truth segmentations were used to train a CNN with a 3D U-Net architecture. The remaining 70 scans were used for testing. The trained CNN was embedded within computational geometry processing pipeline which provides aortic metrics of interest for TEVAR planning. The resulting metrics included aortic arch centerline radius of curvature, proximal landing zones (PLZs) maximum diameters, angulation, and tortuosity. These parameters were statistically analyzed to compare standard arches vs. arches with a common origin of the innominate and left carotid artery (CILCA). The trained CNN yielded a mean Dice score of 0.95 and was able to generalize to 9 pathological cases of thoracic aortic aneurysm, providing accurate segmentations. CILCA arches were characterized by significantly greater angulation (p = 0.015) and tortuosity (p = 0.048) in PLZ 3 vs. standard arches. For both arch configurations, comparisons among PLZs revealed statistically significant differences in maximum zone diameters (p < 0.0001), angulation (p < 0.0001), and tortuosity (p < 0.0001). Our tool allows clinicians to obtain objective and repeatable PLZs mapping, and a range of automatically derived complex aortic metrics.

Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI.

BACKGROUND AND OBJECTIVE: 4D flow magnetic resonance imaging provides time-resolved blood flow velocity measurements, but suffers from limitations in spatio-temporal resolution and noise. In this study, we investigated the use of sinusoidal representation networks (SIRENs) to improve denoising and super-resolution of velocity fields measured by 4D flow MRI in the thoracic aorta. METHODS: Efficient training of SIRENs in 4D was achieved by sampling voxel coordinates and enforcing the no-slip condition at the vessel wall. A set of synthetic measurements were generated from computational fluid dynamics simulations, reproducing different noise levels. The influence of SIREN architecture was systematically investigated, and the performance of our method was compared to existing approaches for 4D flow denoising and super-resolution. RESULTS: Compared to existing techniques, a SIREN with 300 neurons per layer and 20 layers achieved lower errors (up to 50% lower vector normalized root mean square error, 42% lower magnitude normalized root mean square error, and 15% lower direction error) in velocity and wall shear stress fields. Applied to real 4D flow velocity measurements in a patient-specific aortic aneurysm, our method produced denoised and super-resolved velocity fields while maintaining accurate macroscopic flow measurements. CONCLUSIONS: This study demonstrates the feasibility of using SIRENs for complex blood flow velocity representation from clinical 4D flow, with quick execution and straightforward implementation.

A fully automated deep learning approach for coronary artery segmentation and comprehensive characterization.

Coronary computed tomography angiography (CCTA) allows detailed assessment of early markers associated with coronary artery disease (CAD), such as coronary artery calcium (CAC) and tortuosity (CorT). However, their analysis can be time-demanding and biased. We present a fully automated pipeline that performs (i) coronary artery segmentation and (ii) CAC and CorT objective analysis. Our method exploits supervised learning for the segmentation of the lumen, and then, CAC and CorT are automatically quantified. 281 manually annotated CCTA images were used to train a two-stage U-Net-based architecture. The first stage employed a 2.5D U-Net trained on axial, coronal, and sagittal slices for preliminary segmentation, while the second stage utilized a multichannel 3D U-Net for refinement. Then, a geometric post-processing was implemented: vessel centerlines were extracted, and tortuosity score was quantified as the count of branches with three or more bends with change in direction forming an angle >45°. CAC scoring relied on image attenuation. CAC was detected by setting a patient specific threshold, then a region growing algorithm was applied for refinement. The application of the complete pipeline required <5 min per patient. The model trained for coronary segmentation yielded a Dice score of 0.896 and a mean surface distance of 1.027 mm compared to the reference ground truth. Tracts that presented stenosis were correctly segmented. The vessel tortuosity significantly increased locally, moving from proximal, to distal regions (p < 0.001). Calcium volume score exhibited an opposite trend (p < 0.001), with larger plaques in the proximal regions. Volume score was lower in patients with a higher tortuosity score (p < 0.001). Our results suggest a linked negative correlation between tortuosity and calcific plaque formation. We implemented a fast and objective tool, suitable for population studies, that can help clinician in the quantification of CAC and various coronary morphological parameters, which is helpful for CAD risk assessment.

An automated and time-efficient framework for simulation of coronary blood flow under steady and pulsatile conditions.

BACKGROUND AND OBJECTIVE: Invasive fractional flow reserve (FFR) measurement is the gold standard method for coronary artery disease (CAD) diagnosis. FFR-CT exploits computational fluid dynamics (CFD) for non-invasive evaluation of FFR, simulating coronary flow in virtual geometries reconstructed from computed tomography (CT), but suffers from cost-intensive computing process and uncertainties in the definition of patient specific boundary conditions (BCs). In this work, we investigated the use of time-averaged steady BCs, compared to pulsatile to reduce the computational time and deployed a self-adjusting method for the tuning of BCs to patient-specific clinical data. METHODS: 133 coronary arteries were reconstructed form CT images of patients suffering from CAD. For each vessel, invasive FFR was measured. After segmentation, the geometries were prepared for CFD simulation by clipping the outlets and discretizing into tetrahedral mesh. Steady BCs were defined in two steps: (i) rest BCs were extrapolated from clinical and image-derived data; (ii) hyperemic BCs were computed from resting conditions. Flow rate was iteratively adjusted during the simulation, until patient's aortic pressure was matched. Pulsatile BCs were defined exploiting the convergence values of steady BCs. After CFD simulation, lesion-specific hemodynamic indexes were computed and compared between group of patients for which surgery was indicated and not. The whole pipeline was implemented as a straightforward process, in which each single step is performed automatically. RESULTS: Steady and pulsatile FFR-CT yielded a strong correlation (r = 0.988, p < 0.001) and correlated with invasive FFR (r = 0.797, p < 0.001). The per-point difference between the pressure and FFR-CT field predicted by the two methods was below 1 % and 2 %, respectively. Both approaches exhibited a good diagnostic performance: accuracy was 0.860 and 0.864, the AUC was 0.923 and 0.912, for steady and pulsatile case, respectively. The computational time required by steady BCs CFD was approximatively 30-folds lower than pulsatile case. CONCLUSIONS: This work shows the feasibility of using steady BCs CFD for computing the FFR-CT in coronary arteries, as well as its computational and diagnostic performance within a fully automated pipeline.

Left ventricle diastolic vortex ring characterization in ischemic cardiomyopathy: insight into atrio-ventricular interplay.

Diastolic vortex ring (VR) plays a key role in the blood-pumping function exerted by the left ventricle (LV), with altered VR structures being associated with LV dysfunction. Herein, we sought to characterize the VR diastolic alterations in ischemic cardiomyopathy (ICM) patients with systo-diastolic LV dysfunction, as compared to healthy controls, in order to provide a more comprehensive understanding of LV diastolic function. 4D Flow MRI data were acquired in ICM patients (n = 15) and healthy controls (n = 15). The λ2 method was used to extract VRs during early and late diastolic filling. Geometrical VR features, e.g., circularity index (CI), orientation (α), and inclination with respect to the LV outflow tract (ß), were extracted. Kinetic energy (KE), rate of viscous energy loss ( EL ˙ ), vorticity (W), and volume (V) were computed for each VR; the ratios with the respective quantities computed for the entire LV were derived. At peak E-wave, the VR was less circular (p = 0.032), formed a smaller α with the LV long-axis (p = 0.003) and a greater ß (p = 0.002) in ICM patients as compared to controls. At peak A-wave, CI was significantly increased (p = 0.034), while α was significantly smaller (p = 0.016) and ß was significantly increased (p = 0.036) in ICM as compared to controls. At both peak E-wave and peak A-wave, EL ˙ VR / EL ˙ LV , WVR/WLV, and VVR/VLV significantly decreased in ICM patients vs. healthy controls. KEVR/VVR showed a significant decrease in ICM patients with respect to controls at peak E-wave, while VVR remained comparable between normal and pathologic conditions. In the analyzed ICM patients, the diastolic VRs showed alterations in terms of geometry and energetics. These derangements might be attributed to both structural and functional alterations affecting the infarcted wall region and the remote myocardium.

Angulation and curvature of aortic landing zone affect implantation depth in transcatheter aortic valve implantation.

In transcatheter aortic valve implantation (TAVI), final device position may be affected by device interaction with the whole aortic landing zone (LZ) extending to ascending aorta. We investigated the impact of aortic LZ curvature and angulation on TAVI implantation depth, comparing short-frame balloon-expanding (BE) and long-frame self-expanding (SE) devices. Patients (n = 202) treated with BE or SE devices were matched based on one-to-one propensity score. Primary endpoint was the mismatch between the intended (HPre) and the final (HPost) implantation depth. LZ curvature and angulation were calculated based on the aortic centerline trajectory available from pre-TAVI computed tomography. Total LZ curvature ( k L Z , t o t ) and LZ angulation distal to aortic annulus ( α L Z , D i s t a l ) were greater in the SE compared to the BE group (P < 0.001 for both). In the BE group, HPost was significantly higher than HPre at both cusps (P < 0.001). In the SE group, HPost was significantly deeper than HPre only at the left coronary cusp (P = 0.013). At multivariate analysis, α L Z , D i s t a l was the only independent predictor (OR = 1.11, P = 0.002) of deeper final implantation depth with a cut-off value of 17.8°. Aortic LZ curvature and angulation significantly affected final TAVI implantation depth, especially in high stent-frame SE devices reporting, upon complete release, deeper implantation depth with respect to the intended one.

A CT-based deep learning system for automatic assessment of aortic root morphology for TAVI planning.

Accurate planning of transcatheter aortic valve implantation (TAVI) is important to minimize complications, and it requires anatomic evaluation of the aortic root (AR), commonly performed through 3D computed tomography (CT) image analysis. Currently, there is no standard automated solution for this process. Two convolutional neural networks with 3D U-Net architectures (model 1 and model 2) were trained on 310 CT scans for AR analysis. Model 1 performs AR segmentation and model 2 identifies the aortic annulus and sinotubular junction (STJ) contours. After training, the two models were integrated into a fully automated pipeline for geometric analysis of the AR. Results were validated against manual measurements of 178 TAVI candidates. The trained CNNs segmented the AR, annulus, and STJ effectively, resulting in mean Dice scores of 0.93 for the AR, and mean surface distances of 0.73 mm and 0.99 mm for the annulus and STJ, respectively. Automatic measurements were in good agreement with manual annotations, yielding annulus diameters that differed by 0.52 [-2.96, 4.00] mm (bias and 95% limits of agreement for manual minus algorithm). Evaluating the area-derived diameter, bias, and limits of agreement were 0.07 [-0.25, 0.39] mm. STJ and sinuses diameters computed by the automatic method yielded differences of 0.16 [-2.03, 2.34] and 0.1 [-2.93, 3.13] mm, respectively. The proposed tool is a fully automatic solution to quantify morphological biomarkers for pre-TAVI planning. The method was validated against manual annotation from clinical experts and showed to be quick and effective in assessing AR anatomy, with potential for time and cost savings.

Quantification of paravalvular leaks associated with TAVI implants using 4D MRI in an aortic root phantom made possible by the use of 3D printing.

Introduction: Transcatheter aortic valve implantation (TAVI) has become an alternative to surgical replacement of the aortic valve elderly patients. However, TAVI patients may suffer from paravalvular leaks (PVL). Detecting and grading is usually done by echocardiography, but is limited by resolution, 2D visualization and operator dependency. 4D flow magnetic resonance imaging (MRI) is a promising alternative, which did not reach clinical application in TAVI patients. The aim of this study was applying 3D printing technologies in order to evaluate flow patterns and hemodynamics of PVLs following TAVI, exploiting 4D flow MRI and standard ultrasound. Materials and methods: An MR-compatible, anatomically left ventricle, aortic root, and ascending aorta model was fabricated by combining 3D-printed parts and various soft silicone materials to match physiological characteristics. An Abbott Portico™ valve was used in continuous antegrade flow (12-22 l/min), retrograde flow with varying transvalvular pressures (60-110 mmHg), and physiological pulsatile hemodynamics (aortic pressure: 120/80 mmHg, cardiac output: 5 l/min) Time-resolved MR measurements were performed above and below the TAVI stent and compared with color Doppler ultrasound measurements in exactly the same setup. Results: The continuous antegrade flow measurements from MRI largely agreed with the flowmeter measurements, and a maximum error of only 7% was observed. In the retrograde configuration, visualization of the paravalvular leaks was possible from the MR measurements, but flow was overestimated by up to 33%. The 4D MRI measurement in the pulsatile setup revealed a single main PVL, which was also confirmed by the color Doppler measurements, and velocities were similar (2.0 m/s vs. 1.7 m/s). Discussion: 4D MRI techniques were used to qualitatively assess flow in a patient-specific, MR-compatible and flexible model, which only became possible through the use of 3D printing techniques. Flow patterns in the ascending aorta, identification and quantification of PVLs was possible and the location and extent of PVLs were confirmed by ultrasound measurements. The 4D MRI flow technique allowed evaluation of flow patterns in the ascending aorta and the left ventricle below the TAVI stent with good results in identifying PVLs, demonstrating its capabilities over ultrasound by providing the ability to visualize the paravalvular jets in three dimensions at however, additional expenditure of time and money.

Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta.

BACKGROUND AND OBJECTIVE: Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA. METHODS: Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. RESULTS: Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. CONCLUSIONS: We built a data-driven generative model of 4D aortic inlet velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.

Aortic hemodynamics assessment prior and after valve sparing reconstruction: A patient-specific 4D flow-based FSI model.

INTRODUCTION: Valve-sparing root replacement (VSRR) of the ascending aorta is a life-saving procedure for the treatment of aortic aneurysms, but patients remain at risk for post-operative events involving the downstream native aorta, the mechanism for which is uncertain. It is possible that proximal graft replacement of the ascending aorta induces hemodynamics alterations in the descending aorta, which could trigger adverse events. Herein, we present a fluid-structure interaction (FSI) protocol, based on patient-specific geometry and boundary conditions, to assess impact of proximal aortic grafts on downstream aortic hemodynamics and distensibility. METHODS: Cardiac magnetic resonance (CMR), including MRA, cine-CMR and 4D flow sequences, was performed prior and after VSRR on one subject. Central blood pressure was non-invasively acquired at the time of the CMR: data were used to reconstruct the pre- and post-VSRR model and derive patient-specific boundary conditions for the FSI and a computational fluid dynamic (CFD) analysis with the same settings. Results were validated comparing the predicted velocity field against 4D flow dataset, over four landmarks along the aorta, and the predicted distensibility against the cine-CMR derived value. RESULTS: Instantaneous velocity magnitudes extracted from 4D flow and FSI were similar (p > 0.05), while CFD-predicted velocity was significantly higher (p < 0.001), especially in the descending aorta of the pre-VSRR model (vmax was 73 cm/s, 76 cm/s and 99 cm/s, respectively). As measured in cine-CMR, FSI predicted an increase in descending aorta distensibility after grafting (i.e., 4.02 to 5.79 10-3 mmHg-1). In the descending aorta, the post-VSRR model showed increased velocity, aortic distensibility, stress and strain and wall shear stress. CONCLUSIONS: Our Results indicate that i) the distensibility of the wall cannot be neglected, and hence the FSI method is necessary to obtain reliable results; ii) graft implantation induces alterations in the hemodynamics and biomechanics along the thoracic aorta, that may trigger adverse vessel remodeling.

Evaluation and verification of patient-specific modelling of type B aortic dissection.

Quantitative assessment of the complex hemodynamic environment in type B aortic dissection (TBAD) through computational fluid dynamics (CFD) simulations can provide detailed insights into the disease and its progression. As imaging and computational technologies have advanced, methodologies have been developed to increase the accuracy and physiological relevance of CFD simulations. This study presents a patient-specific workflow to simulate blood flow in TBAD, utilising the maximum amount of in vivo data available in the form of CT images, 4D-flow MRI and invasive Doppler-wire pressure measurements, to implement the recommended current best practice methodologies in terms of patient-specific geometry and boundary conditions. The study aimed to evaluate and verify this workflow through detailed qualitative and quantitative comparisons of the CFD and in vivo data. Based on data acquired from five TBAD patients, a range of essential model inputs was obtained, including inlet flow waveforms and 3-element Windkessel model parameters, which can be utilised in further studies where in vivo flow data is not available. Local and global analysis showed good consistency between CFD results and 4D-MRI data, with the maximum velocity in the primary entry tear differing by up to 0.3 m/s, and 80% of the analysed regions achieving moderate or strong correlations between the predicted and in vivo velocities. CFD predicted pressures were generally well matched to the Doppler-wire measurements, with some deviation in peak systolic values. Overall, this study presents a validated comprehensive workflow with extensive data for CFD simulation of TBAD.

Qualitative and Quantitative Assessments of Blood Flow on Tears in Type B Aortic Dissection With Different Morphologies.

Objective: The interactions between aortic morphology and hemodynamics play a key role in determining type B aortic dissection (TBAD) progression and remodeling. The study aimed to provide qualitative and quantitative hemodynamic assessment in four different TBAD morphologies based on 4D flow MRI analysis. Materials and Methods: Four patients with different TBAD morphologies underwent CT and 4D flow MRI scans. Qualitative blood flow evaluation was performed by visualizing velocity streamlines and flow directionality near the tears. Quantitative analysis included flow rate, velocity and reverse flow index (RFI) measurements. Statistical analysis was performed to evaluate hemodynamic differences between the true lumen (TL) and false lumen (FL) of patients. Results: Qualitative analysis revealed blood flow splitting near the primary entry tears (PETs), often causing the formation of vortices in the FL. All patients exhibited clear hemodynamic differences between TL and FL, with the TL generally showing higher velocities and flow rates, and lower RFIs. Average velocity magnitude measurements were significantly different for Patient 1 (t = 5.61, p = 0.001), Patient 2 (t = 3.09, p = 0.02) and Patient 4 (t = 2.81, p = 0.03). At follow-up, Patient three suffered from left renal ischemia because of FL collapse. This patient presented a complex morphology with two FLs and marked flow differences between TL and FLs. In Patient 4, left renal artery malperfusion was observed at the 32-months follow-up, due to FL thrombosis growing after PET repair. Conclusion: The study demonstrates the clinical feasibility of using 4D flow MRI in the context of TBAD. Detailed patient-specific hemodynamics assessment before treatment may provide useful insights to better understand this pathology in the future.

The influence of inlet velocity profile on predicted flow in type B aortic dissection.

In order for computational fluid dynamics to provide quantitative parameters to aid in the clinical assessment of type B aortic dissection, the results must accurately mimic the hemodynamic environment within the aorta. The choice of inlet velocity profile (IVP) therefore is crucial; however, idealised profiles are often adopted, and the effect of IVP on hemodynamics in a dissected aorta is unclear. This study examined two scenarios with respect to the influence of IVP-using (a) patient-specific data in the form of a three-directional (3D), through-plane (TP) or flat IVP; and (b) non-patient-specific flow waveform. The results obtained from nine simulations using patient-specific data showed that all forms of IVP were able to reproduce global flow patterns as observed with 4D flow magnetic resonance imaging. Differences in maximum velocity and time-averaged wall shear stress near the primary entry tear were up to 3% and 6%, respectively, while pressure differences across the true and false lumen differed by up to 6%. More notable variations were found in regions of low wall shear stress when the primary entry tear was close to the left subclavian artery. The results obtained with non-patient-specific waveforms were markedly different. Throughout the aorta, a 25% reduction in stroke volume resulted in up to 28% and 35% reduction in velocity and wall shear stress, respectively, while the shape of flow waveform had a profound influence on the predicted pressure. The results of this study suggest that 3D, TP and flat IVPs all yield reasonably similar velocity and time-averaged wall shear stress results, but TP IVPs should be used where possible for better prediction of pressure. In the absence of patient-specific velocity data, effort should be made to acquire patient's stroke volume and adjust the applied IVP accordingly.

4-D Flow MRI-Based Computational Analysis of Blood Flow in Patient-Specific Aortic Dissection.

OBJECTIVE: Computational hemodynamic studies of aortic dissections usually combine patient-specific geometries with idealized or generic boundary conditions. In this study, we present a comprehensive methodology for the simulation of hemodynamics in type B aortic dissection (TBAD), based on fully patient-specific boundary conditions. METHODS: Pre-operative four-dimensional (4-D) flow magnetic resonance imaging (MRI) and Doppler-wire pressure measurements (pre- and post-operative) were acquired from a TBAD patient. These data were used to derive boundary conditions for computational modeling of flow before and after thoracic endovascular repair (TEVAR). Validations of the computational results were performed by comparing predicted flow patterns with pre-TEVAR 4-D flow MRI, as well as pressures with in vivo measurements. RESULTS AND CONCLUSION: Comparison of instantaneous velocity streamlines showed a good qualitative agreement with 4-D flow MRI. Quantitative comparison of predicted pressures with pressure measurements revealed a maximum difference of 11 mmHg (-9.7%). Furthermore, our model correctly predicted the reduction of true lumen pressure from 74/115 mmHg pre-TEVAR to 64/107 mmHg post-TEVAR (diastolic/systolic pressures at entry tear level), compared to the corresponding measurements of 72/118 mmHg and 64/114 mmHg. This demonstrates that pre-TEVAR 4D flow MRI can be used to tune boundary conditions for post-TEVAR hemodynamic analyses.

Evaluation of 4D flow MRI-based non-invasive pressure assessment in aortic coarctations.

Severity of aortic coarctation (CoA) is currently assessed by estimating trans-coarctation pressure drops through cardiac catheterization or echocardiography. In principle, more detailed information could be obtained non-invasively based on space- and time-resolved magnetic resonance imaging (4D flow) data. Yet the limitations of this imaging technique require testing the accuracy of 4D flow-derived hemodynamic quantities against other methodologies. With the objective of assessing the feasibility and accuracy of this non-invasive method to support the clinical diagnosis of CoA, we developed an algorithm (4DF-FEPPE) to obtain relative pressure distributions from 4D flow data by solving the Poisson pressure equation. 4DF-FEPPE was tested against results from a patient-specific fluid-structure interaction (FSI) simulation, whose patient-specific boundary conditions were prescribed based on 4D flow data. Since numerical simulations provide noise-free pressure fields on fine spatial and temporal scales, our analysis allowed to assess the uncertainties related to 4D flow noise and limited resolution. 4DF-FEPPE and FSI results were compared on a series of cross-sections along the aorta. Bland-Altman analysis revealed very good agreement between the two methodologies in terms of instantaneous data at peak systole, end-diastole and time-averaged values: biases (means of differences) were +0.4 mmHg, -1.1 mmHg and +0.6 mmHg, respectively. Limits of agreement (2 SD) were ±0.978 mmHg, ±1.06 mmHg and ±1.97 mmHg, respectively. Peak-to-peak and maximum trans-coarctation pressure drops obtained with 4DF-FEPPE differed from FSI results by 0.75 mmHg and -1.34 mmHg respectively. The present study considers important validation aspects of non-invasive pressure difference estimation based on 4D flow MRI, showing the potential of this technology to be more broadly applied to the clinical practice.