Quantification of left ventricular myocardial strain: Comparison between MRI tagging, MRI feature tracking, and ultrasound speckle tracking.

Ultrasound speckle tracking is frequently used to quantify myocardial strain, and magnetic resonance imaging (MRI) feature tracking is rapidly gaining interest. Our aim is to validate cardiac MRI feature tracking by comparing it with the gold standard method (i.e., MRI tagging) in healthy subjects and patients. Furthermore, we aim to perform an indirect validation by comparing ultrasound speckle tracking with MRI feature tracking. Forty-two subjects (17 formerly preeclamptic women, three healthy women, and 22 left bundle branch block patients of both sexes) received 3-T cardiac MRI and echocardiography. Cine and tagged MRI, and B-mode ultrasound images, were acquired. Intrapatient global and segmental left ventricular circumferential (MRI tagging vs. MRI feature tracking) and longitudinal (MRI feature tracking vs. ultrasound speckle tracking) peak strain and time to peak strain were compared between the three techniques. Intraclass correlation coefficient (ICC) (< 0.50 = poor, 0.50-0.75 = moderate, > 0.75-0.90 = good, > 0.90 = excellent) and Bland-Altman analysis were used to assess correlation and bias; p less than 0.05 indicates a significant ICC or bias. Global peak strain parameters showed moderate-to-good correlations between methods (ICC = 0.71-0.83, p < 0.01) with no significant biases. Global time to peak strain parameters showed moderate-to-good correlations (ICC = 0.56-0.82, p < 0.01) with no significant biases. Segmental peak strains showed significant biases in all parameters and moderate-to-good correlation (ICC = 0.62-0.77, p < 0.01), except for lateral longitudinal peak strain (ICC = 0.23, p = 0.22). Segmental time to peak strain parameters showed moderate-to-good correlation (ICC = 0.58-0.74, p < 0.01) with no significant biases. MRI feature tracking is a valid method to examine myocardial strain, but there is bias in absolute segmental strain values between imaging techniques. MRI feature tracking shows adequate comparability with ultrasound speckle tracking.

Fully Automated Valve Segmentation for Blood Flow Assessment From 4D Flow MRI Including Automated Cardiac Valve Tracking and Transvalvular Velocity Mapping.

BACKGROUND: Automated 4D flow MRI valvular flow quantification without time-consuming manual segmentation might improve workflow. PURPOSE: Compare automated valve segmentation (AS) to manual (MS), and manually corrected automated segmentation (AMS), in corrected atrioventricular septum defect (c-AVSD) patients and healthy volunteers, for assessing net forward volume (NFV) and regurgitation fraction (RF). STUDY TYPE: Retrospective. POPULATION: 27 c-AVSD patients (median, 23 years; interquartile range, 16-31 years) and 24 healthy volunteers (25 years; 12.5-36.5 years). FIELD STRENGTH/SEQUENCE: Whole-heart 4D flow MRI and cine steady-state free precession at 3T. ASSESSMENT: After automatic valve tracking, valve annuli were segmented on time-resolved reformatted trans-valvular velocity images by AS, MS, and AMS. NFV was calculated for all valves, and RF for right and left atrioventricular valves (RAVV and LAVV). NFV variation (standard deviation divided by mean NFV) and NFV differences (NFV difference of a valve vs. mean NFV of other valves) expressed internal NFV consistency. STATISTICAL TESTS: Comparisons between methods were assessed by Wilcoxon signed-rank tests, and intra/interobserver variability by intraclass correlation coefficients (ICCs). P < 0.05 was considered statistically significant, with multiple testing correction. RESULTS: AMS mean analysis time was significantly shorter compared with MS (5.3 ± 1.6 minutes vs. 9.1 ± 2.5 minutes). MS NFV variation (6.0%) was significantly smaller compared with AMS (6.3%), and AS (8.2%). Median NFV difference of RAVV, LAVV, PV, and AoV between segmentation methods ranged from -0.7-1.0 mL, -0.5-2.8 mL, -1.1-3.6 mL, and - 3.1--2.1 mL, respectively. Median RAVV and LAVV RF, between 7.1%-7.5% and 3.8%-4.3%, respectively, were not significantly different between methods. Intraobserver/interobserver agreement for AMS and MS was strong-to-excellent for NFV and RF (ICC ≥0.88). DATA CONCLUSION: MS demonstrates strongest internal consistency, followed closely by AMS, and AS. Automated segmentation, with or without manual correction, can be considered for 4D flow MRI valvular flow quantification. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Association Between Automated 3D Measurement of Coronary Luminal Narrowing and Risk of Future Myocardial Infarction.

This study focuses on identifying anatomical markers with predictive capacity for long-term myocardial infarction (MI) in focal coronary artery disease (CAD). Eighty future culprit lesions (FCL) and 108 non-culprit lesions (NCL) from 80 patients underwent 3D quantitative coronary angiography. The minimum lumen area (MLA), minimum lumen ratio (MLR), and vessel fractional flow reserve (vFFR) were evaluated. MLR was defined as the ratio between MLA and the cross-sectional area at the proximal lesion edge, with lower values indicating more abrupt luminal narrowing. Significant differences were observed between FCL and NCL in MLR (0.41 vs. 0.53, p < 0.001). MLR correlated inversely with translesional vFFR (r = - 0.26, p = 0.0004) and was the strongest predictor of MI at 5 years (AUC = 0.75). Lesions with MLR < 0.40 had a fourfold increased MI incidence at 5 years. MLR is a robust predictor of future adverse coronary events.

Blood Flow Energy Identifies Coronary Lesions Culprit of Future Myocardial Infarction.

The present study establishes a link between blood flow energy transformations in coronary atherosclerotic lesions and clinical outcomes. The predictive capacity for future myocardial infarction (MI) was compared with that of established quantitative coronary angiography (QCA)-derived predictors. Angiography-based computational fluid dynamics (CFD) simulations were performed on 80 human coronary lesions culprit of MI within 5 years and 108 non-culprit lesions for future MI. Blood flow energy transformations were assessed in the converging flow segment of the lesion as ratios of kinetic and rotational energy values (KER and RER, respectively) at the QCA-identified minimum lumen area and proximal lesion sections. The anatomical and functional lesion severity were evaluated with QCA to derive percentage area stenosis (%AS), vessel fractional flow reserve (vFFR), and translesional vFFR (ΔvFFR). Wall shear stress profiles were investigated in terms of topological shear variation index (TSVI). KER and RER predicted MI at 5 years (AUC = 0.73, 95% CI 0.65-0.80, and AUC = 0.76, 95% CI 0.70-0.83, respectively; p < 0.0001 for both). The predictive capacity for future MI of KER and RER was significantly stronger than vFFR (p = 0.0391 and p = 0.0045, respectively). RER predictive capacity was significantly stronger than %AS and ΔvFFR (p = 0.0041 and p = 0.0059, respectively). The predictive capacity for future MI of KER and RER did not differ significantly from TSVI. Blood flow kinetic and rotational energy transformations were significant predictors for MI at 5 years (p < 0.0001). The findings of this study support the hypothesis of a biomechanical contribution to the process of plaque destabilization/rupture leading to MI.

Influence of intracoronary hemodynamic forces on atherosclerotic plaque phenotypes.

BACKGROUND AND AIMS: Coronary hemodynamics impact coronary plaque progression and destabilization. The aim of the present study was to establish the association between focal vs. diffuse intracoronary pressure gradients and wall shear stress (WSS) patterns with atherosclerotic plaque composition. METHODS: Prospective, international, single-arm study of patients with chronic coronary syndromes and hemodynamic significant lesions (fractional flow reserve [FFR] ≤ 0.80). Motorized FFR pullback pressure gradient (PPG), optical coherence tomography (OCT), and time-average WSS (TAWSS) and topological shear variation index (TSVI) derived from three-dimensional angiography were obtained. RESULTS: One hundred five vessels (median FFR 0.70 [Interquartile range (IQR) 0.56-0.77]) had combined PPG and WSS analyses. TSVI was correlated with PPG (r = 0.47, [95% Confidence Interval (95% CI) 0.30-0.65], p < 0.001). Vessels with a focal CAD (PPG above the median value of 0.67) had significantly higher TAWSS (14.8 [IQR 8.6-24.3] vs. 7.03 [4.8-11.7] Pa, p < 0.001) and TSVI (163.9 [117.6-249.2] vs. 76.8 [23.1-140.9] m-1, p < 0.001). In the 51 vessels with baseline OCT, TSVI was associated with plaque rupture (OR 1.01 [1.00-1.02], p = 0.024), PPG with the extension of lipids (OR 7.78 [6.19-9.77], p = 0.003), with the presence of thin-cap fibroatheroma (OR 2.85 [1.11-7.83], p = 0.024) and plaque rupture (OR 4.94 [1.82 to 13.47], p = 0.002). CONCLUSIONS: Focal and diffuse coronary artery disease, defined using coronary physiology, are associated with differential WSS profiles. Pullback pressure gradients and WSS profiles are associated with atherosclerotic plaque phenotypes. Focal disease (as identified by high PPG) and high TSVI are associated with high-risk plaque features. CLINICAL TRIAL REGISTRATION: https://clinicaltrials,gov/ct2/show/NCT03782688.

Quantitative aortography for assessment of aortic regurgitation in the era of percutaneous aortic valve replacement.

Paravalvular leak (PVL) is a shortcoming that can erode the clinical benefits of transcatheter valve replacement (TAVR) and therefore a readily applicable method (aortography) to quantitate PVL objectively and accurately in the interventional suite is appealing to all operators. The ratio between the areas of the time-density curves in the aorta and left ventricular outflow tract (LVOT-AR) defines the regurgitation fraction (RF). This technique has been validated in a mock circulation; a single injection in diastole was further tested in porcine and ovine models. In the clinical setting, LVOT-AR was compared with trans-thoracic and trans-oesophageal echocardiography and cardiac magnetic resonance imaging. LVOT-AR > 17% discriminates mild from moderate aortic regurgitation on echocardiography and confers a poor prognosis in multiple registries, and justifies balloon post-dilatation. The LVOT-AR differentiates the individual performances of many old and novel devices and is being used in ongoing randomized trials and registries.

Agreement of wall shear stress distribution between two core laboratories using three-dimensional quantitative coronary angiography.

Wall shear stress (WSS) estimated in models reconstructed from intravascular imaging and 3-dimensional-quantitative coronary angiography (3D-QCA) data provides important prognostic information and enables identification of high-risk lesions. However, these analyses are time-consuming and require expertise, limiting WSS adoption in clinical practice. Recently, a novel software has been developed for real-time computation of time-averaged WSS (TAWSS) and multidirectional WSS distribution. This study aims to examine its inter-corelab reproducibility. Sixty lesions (20 coronary bifurcations) with a borderline negative fractional flow reserve were processed using the CAAS Workstation WSS prototype to estimate WSS and multi-directional WSS values. Analysis was performed by two corelabs and their estimations for the WSS in 3 mm segments across each reconstructed vessel was extracted and compared. In total 700 segments (256 located in bifurcated vessels) were included in the analysis. A high intra-class correlation was noted for all the 3D-QCA and TAWSS metrics between the estimations of the two corelabs irrespective of the presence (range: 0.90-0.92) or absence (range: 0.89-0.90) of a coronary bifurcation, while the ICC was good-moderate for the multidirectional WSS (range: 0.72-0.86). Lesion level analysis demonstrated a high agreement of the two corelabls for detecting lesions exposed to an unfavourable haemodynamic environment (WSS > 8.24 Pa, κ = 0.77) that had a high-risk morphology (area stenosis > 61.3%, κ = 0.71) and were prone to progress and cause events. The CAAS Workstation WSS enables reproducible 3D-QCA reconstruction and computation of WSS metrics. Further research is needed to explore its value in detecting high-risk lesions.

Deep learning-based detection of functionally significant stenosis in coronary CT angiography.

Patients with intermediate anatomical degree of coronary artery stenosis require determination of its functional significance. Currently, the reference standard for determining the functional significance of a stenosis is invasive measurement of the fractional flow reserve (FFR), which is associated with high cost and patient burden. To address these drawbacks, FFR can be predicted non-invasively from a coronary CT angiography (CCTA) scan. Hence, we propose a deep learning method for predicting the invasively measured FFR of an artery using a CCTA scan. The study includes CCTA scans of 569 patients from three hospitals. As reference for the functional significance of stenosis, FFR was measured in 514 arteries in 369 patients, and in the remaining 200 patients, obstructive coronary artery disease was ruled out by Coronary Artery Disease-Reporting and Data System (CAD-RADS) category 0 or 1. For prediction, the coronary tree is first extracted and used to reconstruct an MPR for the artery at hand. Thereafter, the coronary artery is characterized by its lumen, its attenuation and the area of the coronary artery calcium in each artery cross-section extracted from the MPR using a CNN. Additionally, characteristics indicating the presence of bifurcations and information indicating whether the artery is a main branch or a side-branch of a main artery are derived from the coronary artery tree. All characteristics are fed to a second network that predicts the FFR value and classifies the presence of functionally significant stenosis. The final result is obtained by merging the two predictions. Performance of our method is evaluated on held out test sets from multiple centers and vendors. The method achieves an area under the receiver operating characteristics curve (AUC) of 0.78, outperforming other works that do not require manual correction of the segmentation of the artery. This demonstrates that our method may reduce the number of patients that unnecessarily undergo invasive measurements.

Kat-ARC accelerated 4D flow CMR: clinical validation for transvalvular flow and peak velocity assessment.

BACKGROUND: To validate the k-adaptive-t autocalibrating reconstruction for Cartesian sampling (kat-ARC), an exclusive sparse reconstruction technique for four-dimensional (4D) flow cardiac magnetic resonance (CMR) using conservation of mass principle applied to transvalvular flow. METHODS: This observational retrospective study (2020/21-075) was approved by the local ethics committee at the University of East Anglia. Consent was waived. Thirty-five patients who had a clinical CMR scan were included. CMR protocol included cine and 4D flow using Kat-ARC acceleration factor 6. No respiratory navigation was applied. For validation, the agreement between mitral net flow (MNF) and the aortic net flow (ANF) was investigated. Additionally, we checked the agreement between peak aortic valve velocity derived by 4D flow and that derived by continuous-wave Doppler echocardiography in 20 patients. RESULTS: The median age of our patient population was 63 years (interquartile range [IQR] 54-73), and 18/35 (51%) were male. Seventeen (49%) patients had mitral regurgitation, and seven (20%) patients had aortic regurgitation. Mean acquisition time was 8 ± 4 min. MNF and ANF were comparable: 60 mL (51-78) versus 63 mL (57-77), p = 0.310). There was an association between MNF and ANF (rho = 0.58, p < 0.001). Peak aortic valve velocity by Doppler and 4D flow were comparable (1.40 m/s, [1.30-1.75] versus 1.46 m/s [1.25-2.11], p = 0.602) and also correlated with each other (rho = 0.77, p < 0.001). CONCLUSIONS: Kat-ARC accelerated 4D flow CMR quantified transvalvular flow in accordance with the conservation of mass principle and is primed for clinical translation.

Validation of time-resolved, automated peak trans-mitral velocity tracking: Two center four-dimensional flow cardiovascular magnetic resonance study.

OBJECTIVE: We aim to validate four-dimensional flow cardiovascular magnetic resonance (4D flow CMR) peak velocity tracking methods for measuring the peak velocity of mitral inflow against Doppler echocardiography. METHOD: Fifty patients were recruited who had 4D flow CMR and Doppler Echocardiography. After transvalvular flow segmentation using established valve tracking methods, peak velocity was automatically derived using three-dimensional streamlines of transvalvular flow. In addition, a static-planar method was used at the tip of mitral valve to mimic Doppler technique. RESULTS: Peak E-wave mitral inflow velocity was comparable between TTE and the novel 4D flow automated dynamic method (0.9 ± 0.5 vs 0.94 ± 0.6 m/s; p = 0.29) however there was a statistically significant difference when compared with the static planar method (0.85 ± 0.5 m/s; p = 0.01). Median A-wave peak velocity was also comparable across TTE and the automated dynamic streamline (0.77 ± 0.4 vs 0.76 ± 0.4 m/s; p = 0.77). A significant difference was seen with the static planar method (0.68 ± 0.5 m/s; p = 0.04). E/A ratio was comparable between TTE and both the automated dynamic and static planar method (1.1 ± 0.7 vs 1.15 ± 0.5 m/s; p = 0.74 and 1.15 ± 0.5 m/s; p = 0.5 respectively). Both novel 4D flow methods showed good correlation with TTE for E-wave (dynamic method; r = 0.70; P < 0.001 and static-planar method; r = 0.67; P < 0.001) and A-wave velocity measurements (dynamic method; r = 0.83; P < 0.001 and static method; r = 0.71; P < 0.001). The automated dynamic method demonstrated excellent intra/inter-observer reproducibility for all parameters. CONCLUSION: Automated dynamic peak velocity tracing method using 4D flow CMR is comparable to Doppler echocardiography for mitral inflow assessment and has excellent reproducibility for clinical use.

Validation of aortic valve pressure gradient quantification using semi-automated 4D flow CMR pipeline.

OBJECTIVE: Doppler echocardiographic aortic valve peak velocity and peak pressure gradient assessment across the aortic valve (AV) is the mainstay for diagnosing aortic stenosis. Four-dimensional flow cardiovascular magnetic resonance (4D flow CMR) is emerging as a valuable diagnostic tool for estimating the peak pressure drop across the aortic valve, but assessment remains cumbersome. We aimed to validate a novel semi-automated pipeline 4D flow CMR method of assessing peak aortic value pressure gradient (AVPG) using the commercially available software solution, CAAS MR Solutions, against invasive angiographic methods. RESULTS: We enrolled 11 patients with severe AS on echocardiography from the EurValve programme. All patients had pre-intervention doppler echocardiography, invasive cardiac catheterisation with peak pressure drop assessment across the AV and 4D flow CMR. The peak AVPG was 51.9 ± 35.2 mmHg using the invasive pressure drop method and 52.2 ± 29.2 mmHg for the 4D flow CMR method (semi-automated pipeline), with good correlation between the two methods (r = 0.70, p = 0.017). Assessment of AVPG by 4D flow CMR using the novel semi-automated pipeline method shows excellent agreement to invasive assessment when compared to doppler-based methods and advocate for its use as complementary to echocardiography.

An automated software for real-time quantification of wall shear stress distribution in quantitative coronary angiography data.

BACKGROUND: Wall shear stress (WSS) estimated in 3D-quantitative coronary angiography (QCA) models appears to provide useful prognostic information and identifies high-risk patients and lesions. However, conventional computational fluid dynamics (CFD) analysis is cumbersome limiting its application in the clinical arena. This report introduces a user-friendly software that allows real-time WSS computation and examines its reproducibility and accuracy in assessing WSS distribution against conventional CFD analysis. METHODS: From a registry of 414 patients with borderline negative fractional flow reserve (0.81-0.85), 100 lesions were randomly selected. 3D-QCA and CFD analysis were performed using the conventional approach and the novel CAAS Workstation WSS software, and QCA as well as WSS estimations of the two approaches were compared. The reproducibility of the two methodologies was evaluated in a subgroup of 50 lesions. RESULTS: A good agreement was noted between the conventional approach and the novel software for 3D-QCA metrics (ICC range: 0.73-0-93) and maximum WSS at the lesion site (ICC: 0.88). Both methodologies had a high reproducibility in assessing lesion severity (ICC range: 0.83-0.97 for the conventional approach; 0.84-0.96 for the CAAS Workstation WSS software) and WSS distribution (ICC: 0.85-0.89 and 0.83-0.87, respectively). Simulation time was significantly shorter using the CAAS Workstation WSS software compared to the conventional approach (4.13 ± 0.59 min vs 23.14 ± 2.56 min, p < 0.001). CONCLUSION: CAAS Workstation WSS software is fast, reproducible, and accurate in assessing WSS distribution. Therefore, this software is expected to enable the broad use of WSS metrics in the clinical arena to identify high-risk lesions and vulnerable patients.

Risk of myocardial infarction based on endothelial shear stress analysis using coronary angiography.

BACKGROUND AND AIMS: Wall shear stress (WSS) has been associated with atherogenesis and plaque progression. The present study assessed the value of WSS analysis derived from conventional coronary angiography to detect lesions culprit for future myocardial infarction (MI). METHODS AND RESULTS: Three-dimensional quantitative coronary angiography (3DQCA), was used to calculate WSS and pressure drop in 80 patients. WSS descriptors were compared between 80 lesions culprit of future MI and 108 non-culprit lesions (controls). Endothelium-blood flow interaction was assessed by computational fluid dynamics (10.8 ± 1.41 min per vessel). Median time between baseline angiography and MI was 25.9 (21.9-29.8) months. Mean patient age was 70.3 ± 12.7. Clinical presentation was STEMI in 35% and NSTEMI in 65%. Culprit lesions showed higher percent area stenosis (%AS), translesional vFFR difference (ΔvFFR), time-averaged WSS (TAWSS) and topological shear variation index (TSVI) compared to non-culprit lesions (p < 0.05 for all). TSVI was superior to TAWSS in predicting MI (AUC-TSVI = 0.77, 95%CI 0.71-0.84 vs. AUC-TAWSS = 0.61, 95%CI 0.53-0.69, p < 0.001). The addition of TSVI increased predictive and reclassification abilities compared to a model based on %AS and ΔvFFR (NRI = 1.04, p < 0.001, IDI = 0.22, p < 0.001). CONCLUSIONS: A 3DQCA-based WSS analysis was feasible and can identify lesions culprit for future MI. The combination of area stenoses, pressure gradients and WSS predicted the occurrence of MI. TSVI, a novel WSS descriptor, showed strong predictive capacity to detect lesions prone to cause MI.

Assessment of turbulent blood flow and wall shear stress in aortic coarctation using image-based simulations.

In this study, we analyzed turbulent flows through a phantom (a 180[Formula: see text] bend with narrowing) at peak systole and a patient-specific coarctation of the aorta (CoA), with a pulsating flow, using magnetic resonance imaging (MRI) and computational fluid dynamics (CFD). For MRI, a 4D-flow MRI is performed using a 3T scanner. For CFD, the standard [Formula: see text], shear stress transport [Formula: see text], and Reynolds stress (RSM) models are applied. A good agreement between measured and simulated velocity is obtained for the phantom, especially for CFD with RSM. The wall shear stress (WSS) shows significant differences between CFD and MRI in absolute values, due to the limited near-wall resolution of MRI. However, normalized WSS shows qualitatively very similar distributions of the local values between MRI and CFD. Finally, a direct comparison between in vivo 4D-flow MRI and CFD with the RSM turbulence model is performed in the CoA. MRI can properly identify regions with locally elevated or suppressed WSS. If the exact values of the WSS are necessary, CFD is the preferred method. For future applications, we recommend the use of the combined MRI/CFD method for analysis and evaluation of the local flow patterns and WSS in the aorta.

Quantitative Angiographic Assessment of Aortic Regurgitation After Transcatheter Implantation of the Venus A-valve: Comparison with Other Self-Expanding Valves and Impact of a Learning Curve in a Single Chinese Center.

Objectives: We aimed to compare the quantitative angiographic aortic regurgitation (AR) into the left ventricular out flow tract (LVOT-AR) of five different types of transcatheter self-expanding valves and to investigate the impact of the learning curve on post-TAVR AR. Background: Quantitative video densitometric aortography is an objective, accurate, and reproducible tool for assessment of AR following TAVR. Methods and results: This retrospective academic core-lab analysis, analyzed 1150 consecutive cine aortograms performed immediately post-TAVR. Quantitative angiographic AR of post-procedural aortography in 181 consecutive patients, who underwent TAVR with the Venus A-valve in a single Chinese center, were compared to the results of Evolut Pro, Evolut R, CoreValve, (Medtronic, Dublin, Ireland) and Acurate Neo (Boston Scientific, Massachusetts, US) transcatheter heart valves (THVs), from a previously published pooled database. Among the 181 aortograms of patients treated with the Venus A-Valve, 113 (62.4%) were analyzable for quantitative assessment of AR. The mean LVOT-AR was 8.9% ± 10.0% with 14.2% of patients having moderate or severe AR in the Venus A-valve group. No significant difference in mean LVOT-AR was observed between Evolut Pro, Evolut R, Acurate Neo, and Venus A-valve. The incidence of LVOT-AR >17%, which correlates with echocardiographic derived ≥ moderate AR, with the Evolut Pro was lower than with the Venus A-valve (5.3% vs. 14.2%, p = 0.034), but was not different from the Evolut R (5.3% vs. 8.8%, p = 0.612), or the Acurate Neo (5.3% vs. 11.3% p = 0.16) systems. A landmark analysis after recruitment of the first half of patients treated with the Venus A valve (N = 56), showed a significantly lower mean LVOT-AR in the second half of the series (11.3% ± 11.9% vs. 6.5% ± 7.1%, p = 0.011). The incidence of LVOT-AR >17% in the latest 57 cases was also numerically lower (7.0% vs. 21.4%, p = 0.857) and compared favorably with the best in class of the self-expanding valves. Conclusion: The Venus A-valve has comparable mean LVOT-AR to other self-expanding valves but has a higher rate of moderate or severe AR than the Evolut Pro THV. However, after completion of a learning phase, results improved and compared favorably with the best in class of the commercially available self-expanding valves. These findings should be confirmed in prospective randomized comparisons of AR between different THVs.

Validation of Prosthetic Mitral Regurgitation Quantification Using Novel Angiographic Platform by Mock Circulation.

OBJECTIVES: This study aimed to validate a dedicated software for quantitative videodensitometric angiographic assessment of mitral regurgitation (QMR). BACKGROUND: Quantitative videodensitometric aortography of aortic regurgitation using the time-density principle is a well-documented technique, but the angiographic assessment of mitral regurgitation (MR) remains at best semi-quantitative and operator dependent. METHODS: Fourteen sheep underwent surgical mitral valve replacement using 2 different prostheses. Pre-sacrifice left ventriculograms were used to assess MR fraction (MRF) using QMR and MR volume (MRV). In an independent core lab, the CAAS QMR 0.1 was used for QMR analysis. In vitro MRF and MRV were assessed in a mock circulation at a comparable cardiac output to the in vivo one by thermodilution. The correlations and agreements of in vitro and in vivo MRF, MRV, and interobserver reproducibility for QMR analysis were assessed using the averaged cardiac cycles (CCs). RESULTS: In vivo derived MRF by QMR strongly correlated with in vitro derived MRF, regardless of the number of the CCs analyzed (best correlation: 3 CCs y = 0.446 + 0.994x; R = 0.784; p =0.002). The mean absolute difference between in vitro derived MRF and in vivo derived MRF from 3 CCs was 0.01 ± 4.2% on Bland-Altman analysis. In vitro MRV and in vivo MRV from 3 CCs were very strongly correlated (y = 0.196 + 1.255x; R = 0.839; p < 0.001). The mean absolute difference between in vitro MRV and in vivo MRV from 3 CCs was -1.4 ± 1.9 ml. There were very strong correlations of in vivo MRF between 2 independent analysts, regardless of the number of the CCs. CONCLUSIONS: In vivo MRF using the novel software is feasible, accurate, and highly reproducible. These promising results have led us to initiate the first human feasibility study comprising patients undergoing percutaneous mitral valve edge-to-edge repair.

Online Quantitative Aortographic Assessment of Aortic Regurgitation After TAVR: Results of the OVAL Study.

OBJECTIVES: The aim of this study was to investigate the online assessment feasibility of aortography using videodensitometry in the catheterization laboratory during transcatheter aortic valve replacement (TAVR). BACKGROUND: Quantitative assessment of regurgitation after TAVR through aortography using videodensitometry is simple, reproducible, and validated in vitro, in vivo, in clinical trials, and in "real-world" patients. However, thus far the assessment has been done offline. METHODS: This was a single center, prospective, proof-of-principle, feasibility study. One hundred consecutive patients with aortic stenosis and indications to undergo TAVR were enrolled. All final aortograms were analyzed immediately after acquisition in the catheterization laboratory and were also sent to an independent core laboratory for blinded offline assessment. The primary endpoint of the study was the feasibility of the online assessment of regurgitation (percentage of analyzable cases). The secondary endpoint was the reproducibility of results between the online assessment and the offline analysis by the core laboratory. RESULTS: Patients' mean age was 81 ± 7 years, and 56% were men. The implanted valves were either SAPIEN 3 (97%) or SAPIEN 3 Ultra (3%). The primary endpoint of online feasibility of analysis was 92% (95% confidence interval [CI]: 86% to 97%) which was the same feasibility encountered by the core laboratory (92%; 95% CI: 86% to 97%). Reproducibility assessment showed a high correlation between online and core laboratory evaluations (R2 = 0.87, p < 0.001), with an intraclass correlation coefficient of 0.962 (95% CI: 0.942 to 0.975; p < 0.001). CONCLUSIONS: This study showed high feasibility of online quantitative assessment of regurgitation and high agreement between the online examiner and core laboratory. These results may pave the way for the application of videodensitometry in the catheterization laboratory after TAVR. (Online Videodensitometric Assessment of Aortic Regurgitation in the Cath-Lab [OVAL]; NCT04047082).

Quantification of Mitral Valve Regurgitation from 4D Flow MRI Using Semiautomated Flow Tracking.

PURPOSE: To compare the accuracy of semiautomated flow tracking with that of semiautomated valve tracking in the quantification of mitral valve (MV) regurgitation from clinical four-dimensional (4D) flow MRI data obtained in patients with mild, moderate, or severe MV regurgitation. MATERIALS AND METHODS: The 4D flow MRI data were retrospectively collected from 30 patients (21 men; mean age, 61 years ± 10 [standard deviation]) who underwent 4D flow MRI from 2006 to 2016. Ten patients had mild MV regurgitation, nine had moderate MV regurgitation, and 11 had severe MV regurgitation, as diagnosed by using semiquantitative echocardiography. The regurgitant volume (Rvol) across the MV was obtained using three methods: indirect quantification of Rvol (RvolINDIRECT), semiautomated quantification of Rvol using valve tracking (RvolVALVE), and semiautomated quantification of Rvol using flow tracking (RvolFLOW). A second observer repeated the measurements. Aortic valve flow was quantified as well to test for intervalve consistency. The Wilcoxon signed rank test, orthogonal regression, Bland-Altman analysis, and coefficients of variation were used to assess agreement among measurements and between observers. RESULTS: RvolFLOW was higher (median, 24.8 mL; interquartile range [IQR], 14.3-45.7 mL) than RvolVALVE (median, 9.9 mL; IQR, 6.0-16.9 mL; P < .001). Both RvolFLOW and RvolVALVE differed significantly from RvolINDIRECT (median, 19.1 mL; IQR, 4.1-47.5 mL; P = .03). RvolFLOW agreed more with RvolINDIRECT (y = 0.78x + 12, r = 0.88) than with RvolVALVE (y = 0.16x + 8.1, r = 0.53). Bland-Altman analysis revealed underestimation of RvolVALVE in severe MV regurgitation. Interobserver agreement was excellent for RvolFLOW (r = 0.95, coefficient of variation = 27%) and moderate for RvolVALVE (r = 0.72, coefficient of variation = 57%). Orthogonal regression demonstrated better intervalve consistency for flow tracking (y = 1.2x - 13.4, r = 0.82) than for valve tracking (y = 2.7x - 92.4, r = 0.67). CONCLUSION: Flow tracking enables more accurate 4D flow MRI-derived MV regurgitation quantification than valve tracking in terms of agreement with indirect quantification and intervalve consistency, particularly in severe MV regurgitation.Supplemental material is available for this article.© RSNA, 2020.

Automated Cardiac Valve Tracking for Flow Quantification with Four-dimensional Flow MRI.

Purpose To compare four-dimensional flow MRI with automated valve tracking to manual valve tracking in patients with acquired or congenital heart disease and healthy volunteers. Materials and Methods In this retrospective study, data were collected from 114 patients and 46 volunteers who underwent four-dimensional flow MRI at 1.5 T or 3.0 T from 2006 through 2017. Among the 114 patients, 33 had acquired and 81 had congenital heart disease (median age, 17 years; interquartile range [IQR], 13-49 years), 51 (45%) were women, and 63 (55%) were men. Among the 46 volunteers (median age, 28 years; IQR, 22-36 years), there were 19 (41%) women and 27 (59%) men. Two orthogonal cine views of each valve were used for valve tracking. Wilcoxon signed-rank test was used to compare analysis times, net forward volumes (NFVs), and regurgitant fractions. Intra- and interobserver variability was tested by using intraclass correlation coefficients (ICCs). Results Analysis time was shorter for automated versus manual tracking (all patients, 14 minutes [IQR, 12-15 minutes] vs 25 minutes [IQR, 20-25 minutes]; P < .001). Although overall differences in NFV and regurgitant fraction were comparable between both methods, NFV variation over four valves was smaller for automated versus manual tracking (all patients, 4.9% [IQR, 3.3%-6.7%] vs 9.8% [IQR, 5.1%-14.7%], respectively; P < .001). Regurgitation severity was discordant for seven pulmonary valves, 22 mitral valves, and 21 tricuspid valves. Intra- and interobserver agreement for automated tracking was excellent for NFV assessment (intra- and interobserver, ICC ≥ 0.99) and strong to excellent for regurgitant fraction assessment (intraobserver, ICC ≥ 0.94; interobserver, ICC ≥ 0.89). Conclusion Automated valve tracking reduces analysis time and improves reliability of valvular flow quantification with four-dimensional flow MRI in patients with acquired or congenital heart disease and in healthy volunteers. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by François in this issue.