Traveling Volunteers: A Multi-Vendor, Multi-Center Study on Reproducibility and Comparability of 4D Flow Derived Aortic Hemodynamics in Cardiovascular Magnetic Resonance.

BACKGROUND: Implementation of four-dimensional flow magnetic resonance (4D Flow MR) in clinical routine requires awareness of confounders. PURPOSE: To investigate inter-vendor comparability of 4D Flow MR derived aortic hemodynamic parameters, assess scan-rescan repeatability, and intra- and interobserver reproducibility. STUDY TYPE: Prospective multicenter study. POPULATION: Fifteen healthy volunteers (age 24.5 ± 5.3 years, 8 females). FIELD STRENGTH/SEQUENCE: 3 T, vendor-provided and clinically used 4D Flow MR sequences of each site. ASSESSMENT: Forward flow volume, peak velocity, average, and maximum wall shear stress (WSS) were assessed via nine planes (P1-P9) throughout the thoracic aorta by a single observer (AD, 2 years of experience). Inter-vendor comparability as well as scan-rescan, intra- and interobserver reproducibility were examined. STATISTICAL TESTS: Equivalence was tested setting the 95% confidence interval of intraobserver and scan-rescan difference as the limit of clinical acceptable disagreement. Intraclass correlation coefficient (ICC) and Bland-Altman plots were used for scan-rescan reproducibility and intra- and interobserver agreement. A P-value <0.05 was considered statistically significant. ICCs ≥ 0.75 indicated strong correlation (>0.9: excellent, 0.75-0.9: good). RESULTS: Ten volunteers finished the complete study successfully. 4D flow derived hemodynamic parameters between scanners of three different vendors are not equivalent exceeding the equivalence range. P3-P9 differed significantly between all three scanners for forward flow (59.1 ± 13.1 mL vs. 68.1 ± 12.0 mL vs. 55.4 ± 13.1 mL), maximum WSS (1842.0 ± 190.5 mPa vs. 1969.5 ± 398.7 mPa vs. 1500.6 ± 247.2 mPa), average WSS (1400.0 ± 149.3 mPa vs. 1322.6 ± 211.8 mPa vs. 1142.0 ± 198.5 mPa), and peak velocity between scanners I vs. III (114.7 ± 12.6 cm/s vs. 101.3 ± 15.6 cm/s). Overall, the plane location at the sinotubular junction (P1) presented most inter-vendor stability (forward: 78.5 ± 15.1 mL vs. 80.3 ± 15.4 mL vs. 79.5 ± 19.9 mL [P = 0.368]; peak: 126.4 ± 16.7 cm/s vs. 119.7 ± 13.6 cm/s vs. 111.2 ± 22.6 cm/s [P = 0.097]). Scan-rescan reproducibility and intra- and interobserver variability were good to excellent (ICC ≥ 0.8) with best agreement for forward flow (ICC ≥ 0.98). DATA CONCLUSION: The clinical protocol used at three different sites led to differences in hemodynamic parameters assessed by 4D flow. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Impact of sequence type and field strength (1.5, 3, and 7T) on 4D flow MRI hemodynamic aortic parameters in healthy volunteers.

PURPOSE: 4D flow magnetic resonance imaging (4D-MRI) allows time-resolved visualization of blood flow patterns, quantification of volumes, velocities, and advanced parameters, such as wall shear stress (WSS). As 4D-MRI enters the clinical arena, standardization and awareness of confounders are important. Our aim was to evaluate the equivalence of 4D flow-derived aortic hemodynamics in healthy volunteers using different sequences and field strengths. METHODS: 4D-MRI was acquired in 10 healthy volunteers at 1.5T using three different prototype sequences, at 3T and at 7T (Siemens Healthineers). After evaluation of diagnostic quality in three segments (ascending-, descending aorta, aortic arch), peak velocity, flow volumes, and WSS were investigated. Equivalence limits for comparison of field strengths/sequences were based on the limits of Bland-Altman analyses of the intraobserver variability. RESULTS: Non-diagnostic quality was found in 10/144 segments, 9/10 were obtained at 7T. Apart for the comparison of forward flow between sequence 1 and 3, the differences in measurements between field strengths/sequences exceeded the range of agreement. Significant differences were found between field strengths/sequences for forward flow (1.5T vs. 3T, 3T vs. 7T, sequence 1 vs. 3, 2 vs. 3 [P < .001]), WSS (1.5T vs. 3T [P < .05], sequence 1 vs. 2, 1 vs. 3, 2 vs. 3 [P < .001]), and peak velocity (1.5T vs. 7T, sequence 1 vs. 3 [P > .001]). All parameters at all field strengths/with all sequences correlated moderately to strongly (r ≥ 0.5). CONCLUSION: Data from all sequences could be acquired and resulting images showed sufficient quality for further analysis. However, the variability of the measurements of peak velocity, flow volumes, and WSS was higher when comparing field strengths/sequences as the equivalence limits defined by the intraobserver assessments.

Quantification of myocardial strain assessed by cardiovascular magnetic resonance feature tracking in healthy subjects-influence of segmentation and analysis software.

OBJECTIVES: Quantification of myocardial deformation by feature tracking is of growing interest in cardiovascular magnetic resonance. It allows the assessment of regional myocardial function based on cine images. However, image acquisition, post-processing, and interpretation are not standardized. We aimed to assess the influence of segmentation procedure such as slice selection and different types of analysis software on values and quantification of myocardial strain in healthy adults. METHODS: Healthy volunteers were retrospectively analyzed. Post-processing was performed using CVI42 and TomTec. Longitudinal and radialLong axis (LAX) strain were quantified using 4-chamber-view, 3-chamber-view, and 2-chamber-view. Circumferential and radialShort axis (SAX) strain were assessed in basal, midventricular, and apical short-axis views and using full coverage. Global and segmental strain values were compared to each other regarding their post-processing approach and analysis software package. RESULTS: We screened healthy volunteers studied at 1.5 or 3.0 T and included 67 (age 44.3 ± 16.3 years, 31 females). Circumferential and radialSAX strain values were different between a full coverage approach vs. three short slices (- 17.6 ± 1.8% vs. - 19.2 ± 2.3% and 29.1 ± 4.8% vs. 34.6 ± 7.1%). Different analysis software calculated significantly different strain values. Within the same vendor, different field strengths (- 17.0 ± 2.1% at 1.5 T vs. - 17.0 ± 1.7% at 3 T, p = 0.845) did not influence the calculated global longitudinal strain (GLS), and were similar in gender (- 17.4 ± 2.0% in females vs. - 16.6 ± 1.8% in males, p = 0.098). Circumferential and radial strain were different in females and males (circumferential strain - 18.2 ± 1.7% vs. - 17.1 ± 1.8%, p = 0.029 and radial strain 30.7 ± 4.7% vs. 27.8 ± 4.6%, p = 0.047). CONCLUSIONS: Myocardial deformation assessed by feature tracking depends on segmentation procedure and type of analysis software. CircumferentialSAX and radialSAX depend on the number of slices used for feature tracking analysis. As known from other imaging modalities, GLS seems to be the most stable parameter. During follow-up studies, standardized conditions should be warranted. Trial registration Retrospectively registered KEY POINTS: • Myocardial deformation assessed by feature tracking depends on the segmentation procedure. • Global myocardial strain values differ significantly among vendors. • Standardization in post-processing using CMR feature tracking is essential.

Z-score mapping for standardized analysis and reporting of cardiovascular magnetic resonance modified Look-Locker inversion recovery (MOLLI) T1 data: Normal behavior and validation in patients with amyloidosis.

BACKGROUND: T1 mapping using modified Look-Locker inversion recovery (MOLLI) provides quantitative information on myocardial tissue composition. T1 results differ between sites due to variations in hardware and software equipment, limiting the comparability of results. The aim was to test if Z-scores can be used to compare the results of MOLLI T1 mapping from different cardiovascular magnetic resonance (CMR) platforms. METHODS: First, healthy subjects (n = 15) underwent 11 combinations of native short-axis T1 mapping (four CMR systems from two manufacturers at 1.5 T and 3 T, three MOLLI schemes). Mean and standard deviation (SD) of septal myocardial T1 were derived for each combination. T1 maps were transformed into Z-score maps based on mean and SD values using a prototype post-processing module. Second, Z-score mapping was applied to a validation sample of patients with cardiac amyloidosis at 1.5 T (n = 25) or 3 T (n = 13). RESULTS: In conventional T1 analysis, results were confounded by variations in field strength, MOLLI scheme, and manufacturer-specific system characteristics. Z-score-based analysis yielded consistent results without significant differences between any two of the combinations in part 1 of the study. In the validation sample, Z-score mapping differentiated between patients with cardiac amyloidosis and healthy subjects with the same diagnostic accuracy as standard T1 analysis regardless of field strength. CONCLUSIONS: T1 analysis based on Z-score mapping provides consistent results without significant differences due to field strengths, CMR systems, or MOLLI variants, and detects cardiac amyloidosis with the same diagnostic accuracy as conventional T1 analysis. Z-score mapping provides a means to compare native T1 results acquired with MOLLI across different CMR platforms.

The influence of post-processing software on quantitative results in 4D flow cardiovascular magnetic resonance examinations.

Background: Several commercially available software packages exist for the analysis of three-dimensional cine phase-contrast cardiovascular magnetic resonance (CMR) with three-directional velocity encoding (four-dimensional (4D) flow CMR). Only sparse data are available on the impact of these different software solutions on quantitative results. We compared two different commercially available and widely used software packages and their impact on the forward flow volume (FFV), peak velocity (PV), and maximum wall shear stress (WSS) per plane. Materials and methods: 4D flow CMR datasets acquired by 3 Tesla magnetic resonance imaging of 10 healthy volunteers, 13 aortic stenosis patients, and 7 aortic valve replacement patients were retrospectively analyzed for FFV, PV, and WSS using two software packages in six analysis planes along the thoracic aorta. Absolute (AD) and relative differences (RD), intraclass correlation coefficients (ICC), Bland-Altman analysis, and Spearman's correlation analysis were calculated. Results: For the FFV and PV in healthy volunteers, there was good to excellent agreement between both software packages [FFV: ICC = 0.93-0.97, AD: 0.1 ± 5.4 ml (-2.3 ± 2.4 ml), RD: -0.3 ± 8% (-5.7 ± 6.0%); PV: ICC = 0.81-0.99, AD: -0.02 ± 0.02 ml (-0.1 ± 0.1 ml), RD: -1.6 ± 2.1% (-9.3 ± 6.1%)]. In patients, the FFV showed good to excellent agreement [ICC: 0.75-0.91, AD: -1.8 ± 6.5 ml (-8.3 ± 9.9 ml), RD: -2.2 ± 9.2% (-13.8 ± 17.4%)]. In the ascending aorta, PV showed only poor to moderate agreement in patients (plane 2 ICC: 0.33, plane 3 ICC: 0.72), whereas the rest of the thoracic aorta revealed good to excellent agreement [ICC: 0.95-0.98, AD: -0.03 ± 0.07 (-0.1 ± 0.1 m/s), RD: -3.5 ± 7.9% (-7.8 ± 9.9%)]. WSS analysis showed no to poor agreement between both software packages. Global correlation analyses revealed good to very good correlation between FFV and PV and only poor correlation for WSS. Conclusions: There was good to very good agreement for the FFV and PV except for the ascending aorta in patients when comparing PV and no agreement for WSS. Standardization is therefore necessary.

Translating principles of quality control to cardiovascular magnetic resonance: assessing quantitative parameters of the left ventricle in a large cohort.

Cardiac magnetic resonance (CMR) examinations require standardization to achieve reproducible results. Therefore, quality control as known as in other industries such as in-vitro diagnostics, could be of essential value. One such method is the statistical detection of long-time drifts of clinically relevant measurements. Starting in 2010, reports from all CMR examinations of a high-volume center were stored in a hospital information system. Quantitative parameters of the left ventricle were analyzed over time with moving averages of different window sizes. Influencing factors on the acquisition and on the downstream analysis were captured. 26,902 patient examinations were exported from the clinical information system. The moving median was compared to predefined tolerance ranges, which revealed an overall of 50 potential quality relevant changes ("alerts") in SV, EDV and LVM. Potential causes such as change of staff, scanner relocation and software changes were found not to be causal of the alerts. No other influencing factors were identified retrospectively. Statistical quality assurance systems based on moving average control charts may provide an important step towards reliability of quantitative CMR. A prospective evaluation is needed for the effective root cause analysis of quality relevant alerts.

Changes of aortic hemodynamics after aortic valve replacement-A four dimensional flow cardiovascular magnetic resonance follow up study.

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

Markers of Myocardial Damage Predict Mortality in Patients With Aortic Stenosis.

BACKGROUND: Cardiovascular magnetic resonance (CMR) is increasingly used for risk stratification in aortic stenosis (AS). However, the relative prognostic power of CMR markers and their respective thresholds remains undefined. OBJECTIVES: Using machine learning, the study aimed to identify prognostically important CMR markers in AS and their thresholds of mortality. METHODS: Patients with severe AS undergoing AVR (n = 440, derivation; n = 359, validation cohort) were prospectively enrolled across 13 international sites (median 3.8 years' follow-up). CMR was performed shortly before surgical or transcatheter AVR. A random survival forest model was built using 29 variables (13 CMR) with post-AVR death as the outcome. RESULTS: There were 52 deaths in the derivation cohort and 51 deaths in the validation cohort. The 4 most predictive CMR markers were extracellular volume fraction, late gadolinium enhancement, indexed left ventricular end-diastolic volume (LVEDVi), and right ventricular ejection fraction. Across the whole cohort and in asymptomatic patients, risk-adjusted predicted mortality increased strongly once extracellular volume fraction exceeded 27%, while late gadolinium enhancement >2% showed persistent high risk. Increased mortality was also observed with both large (LVEDVi >80 mL/m2) and small (LVEDVi ≤55 mL/m2) ventricles, and with high (>80%) and low (≤50%) right ventricular ejection fraction. The predictability was improved when these 4 markers were added to clinical factors (3-year C-index: 0.778 vs 0.739). The prognostic thresholds and risk stratification by CMR variables were reproduced in the validation cohort. CONCLUSIONS: Machine learning identified myocardial fibrosis and biventricular remodeling markers as the top predictors of survival in AS and highlighted their nonlinear association with mortality. These markers may have potential in optimizing the decision of AVR.

A multi-vendor, multi-center study on reproducibility and comparability of fast strain-encoded cardiovascular magnetic resonance imaging.

Myocardial strain is a convenient parameter to quantify left ventricular (LV) function. Fast strain-encoding (fSENC) enables the acquisition of cardiovascular magnetic resonance images for strain-measurement within a few heartbeats during free-breathing. It is necessary to analyze inter-vendor agreement of techniques to determine strain, such as fSENC, in order to compare existing studies and plan multi-center studies. Therefore, the aim of this study was to investigate inter-vendor agreement and test-retest reproducibility of fSENC for three major MRI-vendors. fSENC-images were acquired three times in the same group of 15 healthy volunteers using 3 Tesla scanners from three different vendors: at the German Heart Institute Berlin, the Charité University Medicine Berlin-Campus Buch and the Theresien-Hospital Mannheim. Volunteers were scanned using the same imaging protocol composed of two fSENC-acquisitions, a 15-min break and another two fSENC-acquisitions. LV global longitudinal and circumferential strain (GLS, GCS) were analyzed by a trained observer (Myostrain 5.0, Myocardial Solutions) and for nine volunteers repeatedly by another observer. Inter-vendor agreement was determined using Bland-Altman analysis. Test-retest reproducibility and intra- and inter-observer reproducibility were analyzed using intraclass correlation coefficient (ICC) and coefficients of variation (CoV). Inter-vendor agreement between all three sites was good for GLS and GCS, with biases of 0.01-1.88%. Test-retest reproducibility of scans before and after the break was high, shown by ICC- and CoV values of 0.63-0.97 and 3-9% for GLS and 0.69-0.82 and 4-7% for GCS, respectively. Intra- and inter-observer reproducibility were excellent for both parameters (ICC of 0.77-0.99, CoV of 2-5%). This trial demonstrates good inter-vendor agreement and test-retest reproducibility of GLS and GCS measurements, acquired at three different scanners from three different vendors using fSENC. The results indicate that it is necessary to account for a possible bias (< 2%) when comparing strain measurements of different scanners. Technical differences between scanners, which impact inter-vendor agreement, should be further analyzed and minimized.DRKS Registration Number: 00013253.Universal Trial Number (UTN): U1111-1207-5874.

Extracellular Myocardial Volume in Patients With Aortic Stenosis.

BACKGROUND: Myocardial fibrosis is a key mechanism of left ventricular decompensation in aortic stenosis and can be quantified using cardiovascular magnetic resonance (CMR) measures such as extracellular volume fraction (ECV%). Outcomes following aortic valve intervention may be linked to the presence and extent of myocardial fibrosis. OBJECTIVES: This study sought to determine associations between ECV% and markers of left ventricular decompensation and post-intervention clinical outcomes. METHODS: Patients with severe aortic stenosis underwent CMR, including ECV% quantification using modified Look-Locker inversion recovery-based T1 mapping and late gadolinium enhancement before aortic valve intervention. A central core laboratory quantified CMR parameters. RESULTS: Four-hundred forty patients (age 70 ± 10 years, 59% male) from 10 international centers underwent CMR a median of 15 days (IQR: 4 to 58 days) before aortic valve intervention. ECV% did not vary by scanner manufacturer, magnetic field strength, or T1 mapping sequence (all p > 0.20). ECV% correlated with markers of left ventricular decompensation including left ventricular mass, left atrial volume, New York Heart Association functional class III/IV, late gadolinium enhancement, and lower left ventricular ejection fraction (p < 0.05 for all), the latter 2 associations being independent of all other clinical variables (p = 0.035 and p < 0.001). After a median of 3.8 years (IQR: 2.8 to 4.6 years) of follow-up, 52 patients had died, 14 from adjudicated cardiovascular causes. A progressive increase in all-cause mortality was seen across tertiles of ECV% (17.3, 31.6, and 52.7 deaths per 1,000 patient-years; log-rank test; p = 0.009). Not only was ECV% associated with cardiovascular mortality (p = 0.003), but it was also independently associated with all-cause mortality following adjustment for age, sex, ejection fraction, and late gadolinium enhancement (hazard ratio per percent increase in ECV%: 1.10; 95% confidence interval [1.02 to 1.19]; p = 0.013). CONCLUSIONS: In patients with severe aortic stenosis scheduled for aortic valve intervention, an increased ECV% is a measure of left ventricular decompensation and a powerful independent predictor of mortality.