The clinical impact of phase offset errors and different correction methods in cardiovascular magnetic resonance phase contrast imaging: a multi-scanner study

4D flow MRI velocity uncertainty quantification

PURPOSE

An automatic method is presented for estimating 4D flow MRI velocity measurement uncertainty in each voxel. The velocity distance (VD) metric, a statistical distance between the measured velocity and local error distribution, is introduced as a novel measure of 4D flow MRI velocity measurement quality.

METHODS

The method uses mass conservation to assess the local velocity error variance and the standardized difference of means (SDM) velocity to estimate the velocity error correlations. VD is evaluated as the Mahalanobis distance between the local velocity measurement and the local error distribution. The uncertainty model is validated synthetically and tested in vitro under different flow resolutions and noise levels. The VD's application is demonstrated on two in vivo thoracic vasculature 4D flow datasets.

RESULTS

Synthetic results show the proposed uncertainty quantification method is sensitive to aliased regions across various velocity-to-noise ratios and assesses velocity error correlations in four- and six-point acquisitions with correlation errors at or under 3.2%. In vitro results demonstrate the method's sensitivity to spatial resolution, venc settings, partial volume effects, and phase wrapping error sources. Applying VD to assess in vivo 4D flow MRI in the aorta demonstrates the expected increase in measured velocity quality with contrast administration and systolic flow.

CONCLUSION

The proposed 4D flow MRI uncertainty quantification method assesses velocity measurement error owing to sources including noise, intravoxel phase dispersion, and velocity aliasing. This method enables rigorous comparison of 4D flow MRI datasets obtained in longitudinal studies, across patient populations, and with different MRI systems.

Optimizing encoding strategies for 4D Flow MRI of mean and turbulent flow

For 4D Flow MRI of mean and turbulent flow a compromise between spatiotemporal undersampling and velocity encodings needs to be found. Assuming a fixed scan time budget, the impact of trading off spatiotemporal undersampling versus velocity encodings on quantification of velocity and turbulence for aortic 4D Flow MRI was investigated. For this purpose, patient-specific mean and turbulent aortic flow data were generated using computational fluid dynamics which were embedded into the patient-specific background image data to generate synthetic MRI data with corresponding ground truth flow. Cardiac and respiratory motion were included. Using the synthetic MRI data as input, 4D Flow MRI was subsequently simulated with undersampling along pseudo-spiral Golden angle Cartesian trajectories for various velocity encoding schemes. Data were reconstructed using a locally low rank approach to obtain mean and turbulent flow fields to be compared to ground truth. Results show that, for a 15-min scan, velocity magnitudes can be reconstructed with good accuracy relatively independent of the velocity encoding scheme ( S S I M U = 0.938 ± 0.003 ) , good accuracy ( S S I M U ≥ 0.933 ) and with peak velocity errors limited to 10%. Turbulence maps on the other hand suffer from both lower reconstruction quality ( S S I M TKE ≥ 0.323 ) and larger sensitivity to undersampling, motion and velocity encoding strengths ( S S I M TKE = 0.570 ± 0.110 ) when compared to velocity maps. The best compromise to measure unwrapped velocity maps and turbulent kinetic energy given a fixed 15-min scan budget was found to be a 7-point multi- V enc acquisition with a low V enc tuned for best sensitivity to the range of expected intra-voxel standard deviations and a high V enc larger than the expected peak velocity.

Regional aortic wall shear stress increases over time in patients with a bicuspid aortic valve

BACKGROUND

Aortic wall shear stress (WSS) is a known predictor of ascending aortic growth in patients with a bicuspid aortic valve (BAV). The aim of this study was to study regional WSS and changes over time in BAV patients.

METHODS

BAV patients and age-matched healthy controls underwent four-dimensional (4D) flow cardiovascular magnetic resonance (CMR). Regional, peak systolic ascending aortic WSS, aortic valve function, aortic stiffness measures, and aortic dimensions were assessed. In BAV patients, 4D flow CMR was repeated after 3 years of follow-up and both at baseline and follow-up computed tomography angiography (CTA) were acquired. Aortic growth (volume increase of ≥5%) was measured on CTA. Regional WSS differences within patients' aorta and WSS changes over time were analyzed using linear mixed-effect models and were associated with clinical parameters.

RESULTS

Thirty BAV patients (aged 34 years [interquartile range (IQR) 25-41]) were included in the follow-up analysis. Additionally, another 16 BAV patients and 32 healthy controls (aged 33 years [IQR 28-48]) were included for other regional analyses. Magnitude, axial, and circumferential WSS increased over time (all p < 0.001) irrespective of aortic growth. The percentage of regions exposed to a magnitude WSS >95th percentile of healthy controls increased from 21% (baseline 506/2400 regions) to 31% (follow-up 734/2400 regions) (p < 0.001). WSS angle, a measure of helicity near the aortic wall, decreased during follow-up. Magnitude WSS changes over time were associated with systolic blood pressure, peak aortic valve velocity, aortic valve regurgitation fraction, aortic stiffness indexes, and normalized flow displacement (all p < 0.05).

CONCLUSION

An increase in regional WSS over time was observed in BAV patients, irrespective of aortic growth. The increasing WSSs, comprising a larger area of the aorta, warrant further research to investigate the possible predictive value for aortic dissection.

Assessment of the Severity of Aortic Regurgitation by Noninvasive Imaging : Non-invasive MMI for AR

PURPOSE OF THE REVIEW

The role of multimodality imaging in the evaluation of patients with aortic regurgitation is summarized in this review.

RECENT FINDINGS

The etiology (mechanism) of the aortic regurgitation and the severity of aortic regurgitation and hemodynamic consequences are key in the decision making of patients with severe aortic regurgitation. While echocardiography remains as the leading technique to assess all these parameters, other imaging techniques have become essential for the accurate assessment of aortic regurgitation severity and the timing of aortic intervention. The anatomic suitability of transcatheter aortic valve implantation in inoperable patients with severe aortic regurgitation is usually assessed with computed tomography. Aortic regurgitation is a prevalent disease with various pathophysiological mechanisms that need a personalized treatment. The evaluation of the mechanism and severity of aortic regurgitation can be initially performed with echocardiography. Three-dimensional techniques, including echocardiography, have become very relevant for accurate assessment of the regurgitation severity and its hemodynamic consequences. Assessment of myocardial tissue characteristics with cardiac magnetic resonance is key in the risk stratification of patients and in the timing of aortic intervention. Computed tomography is important in the assessment of aortic dimensions and selection of patients for transcatheter aortic valve implantation.

Flow evaluation software for four-dimensional flow MRI: a reliability and validation study

PURPOSE

Four-dimensional time-resolved phase-contrast cardiovascular magnetic resonance imaging (4D flow MRI) enables blood flow quantification in multiple vessels, which is crucial for patients with congenital heart disease (CHD). We investigated net flow volumes in the ascending aorta and pulmonary arteries by four different postprocessing software packages for 4D flow MRI in comparison with 2D cine phase-contrast measurements (2D PC).

MATERIAL AND METHODS

4D flow and 2D PC datasets of 47 patients with biventricular CHD (median age 16, range 0.6-52 years) were acquired at 1.5 T. Net flow volumes in the ascending aorta, the main, right, and left pulmonary arteries were measured using four different postprocessing software applications and compared to offset-corrected 2D PC data. Reliability of 4D flow postprocessing software was assessed by Bland-Altman analysis and intraclass correlation coefficient (ICC). Linear regression of internal flow controls was calculated. Interobserver reproducibility was evaluated in 25 patients.

RESULTS

Correlation and agreement of flow volumes were very good for all software compared to 2D PC (ICC ≥ 0.94; bias ≤ 5%). Internal controls were excellent for 2D PC (r ≥ 0.95, p < 0.001) and 4D flow (r ≥ 0.94, p < 0.001) without significant difference of correlation coefficients between methods. Interobserver reliability was good for all vendors (ICC ≥ 0.94, agreement bias < 8%).

CONCLUSION

Haemodynamic information from 4D flow in the large thoracic arteries assessed by four commercially available postprocessing applications matches routinely performed 2D PC values. Therefore, we consider 4D flow MRI-derived data ready for clinical use in patients with CHD.

Four-Dimensional Flow MR Imaging: Technique and Advances

4D Flow MRI is an advanced imaging technique for comprehensive non-invasive assessment of the cardiovascular system. The capture of the blood velocity vector field throughout the cardiac cycle enables measures of flow, pulse wave velocity, kinetic energy, wall shear stress, and more. Advances in hardware, MRI data acquisition and reconstruction methodology allow for clinically feasible scan times. The availability of 4D Flow analysis packages allows for more widespread use in research and the clinic and will facilitate much needed multi-center, multi-vendor studies in order to establish consistency across scanner platforms and to enable larger scale studies to demonstrate clinical value.

4D Flow cardiovascular magnetic resonance consensus statement: 2023 update

Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 '4D Flow CMR Consensus Statement'. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards.

Deep learning phase error correction for cerebrovascular 4D flow MRI

Background phase errors in 4D Flow MRI may negatively impact blood flow quantification. In this study, we assessed their impact on cerebrovascular flow volume measurements, evaluated the benefit of manual image-based correction, and assessed the potential of a convolutional neural network (CNN), a form of deep learning, to directly infer the correction vector field. With IRB waiver of informed consent, we retrospectively identified 96 MRI exams from 48 patients who underwent cerebrovascular 4D Flow MRI from October 2015 to 2020. Flow measurements of the anterior, posterior, and venous circulation were performed to assess inflow-outflow error and the benefit of manual image-based phase error correction. A CNN was then trained to directly infer the phase-error correction field, without segmentation, from 4D Flow volumes to automate correction, reserving from 23 exams for testing. Statistical analyses included Spearman correlation, Bland-Altman, Wilcoxon-signed rank (WSR) and F-tests. Prior to correction, there was strong correlation between inflow and outflow (ρ = 0.833-0.947) measurements with the largest discrepancy in the venous circulation. Manual phase error correction improved inflow-outflow correlation (ρ = 0.945-0.981) and decreased variance (p < 0.001, F-test). Fully automated CNN correction was non-inferior to manual correction with no significant differences in correlation (ρ = 0.971 vs ρ = 0.982) or bias (p = 0.82, Wilcoxon-Signed Rank test) of inflow and outflow measurements. Residual background phase error can impair inflow-outflow consistency of cerebrovascular flow volume measurements. A CNN can be used to directly infer the phase-error vector field to fully automate phase error correction.

Enhanced 4D Flow MRI-Based CFD with Adaptive Mesh Refinement for Flow Dynamics Assessment in Coarctation of the Aorta

4D Flow MRI is a diagnostic tool that can visualize and quantify patient-specific hemodynamics and help interventionalists optimize treatment strategies for repairing coarctation of the aorta (COA). Despite recent developments in 4D Flow MRI, shortcomings include phase-offset errors, limited spatiotemporal resolution, aliasing, inaccuracies due to slow aneurysmal flows, and distortion of images due to metallic artifact from vascular stents. To address these limitations, we developed a framework utilizing Computational Fluid Dynamics (CFD) with Adaptive Mesh Refinement (AMR) that enhances 4D Flow MRI visualization/quantification. We applied this framework to five pediatric patients with COA, providing in-vivo and in-silico datasets, pre- and post-intervention. These two data sets were compared and showed that CFD flow rates were within 9.6% of 4D Flow MRI, which is within a clinically acceptable range. CFD simulated slow aneurysmal flow, which MRI failed to capture due to high relative velocity encoding (Venc). CFD successfully predicted in-stent blood flow, which was not visible in the in-vivo data due to susceptibility artifact. AMR improved spatial resolution by factors of 101 to 103 and temporal resolution four-fold. This computational framework has strong potential to optimize visualization/quantification of aneurysmal and in-stent flows, improve spatiotemporal resolution, and assess hemodynamic efficiency post-COA treatment.

The Role of Cardiac Magnetic Resonance in Aortic Stenosis and Regurgitation

Cardiac magnetic resonance (CMR) imaging is a well-set diagnostic technique for assessment of valvular heart diseases and is gaining ground in current clinical practice. It provides high-quality images without the administration of ionizing radiation and occasionally without the need of contrast agents. It offers the unique possibility of a comprehensive stand-alone assessment of the heart including biventricular function, left ventricle remodeling, myocardial fibrosis, and associated valvulopathies. CMR is the recognized reference for the quantification of ventricular volumes, mass, and function. A particular strength is the ability to quantify flow, especially with new techniques which allow accurate measurement of stenosis and regurgitation. Furthermore, tissue mapping enables the visualization and quantification of structural changes in the myocardium. In this way, CMR has the potential to yield important prognostic information predicting those patients who will progress to surgery and impact outcomes. In this review, the fundamentals of CMR in assessment of aortic valve diseases (AVD) are described, together with its strengths and weaknesses. This state-of-the-art review provides an updated overview of CMR potentials in all AVD issues, including valve anatomy, flow quantification, ventricular volumes and function, and tissue characterization.

Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications

Inverse modeling approaches in cardiovascular medicine are a collection of methodologies that can provide non-invasive patient-specific estimations of tissue properties, mechanical loads, and other mechanics-based risk factors using medical imaging as inputs. Its incorporation into clinical practice has the potential to improve diagnosis and treatment planning with low associated risks and costs. These methods have become available for medical applications mainly due to the continuing development of image-based kinematic techniques, the maturity of the associated theories describing cardiovascular function, and recent progress in computer science, modeling, and simulation engineering. Inverse method applications are multidisciplinary, requiring tailored solutions to the available clinical data, pathology of interest, and available computational resources. Herein, we review biomechanical modeling and simulation principles, methods of solving inverse problems, and techniques for image-based kinematic analysis. In the final section, the major advances in inverse modeling of human cardiovascular mechanics since its early development in the early 2000s are reviewed with emphasis on method-specific descriptions, results, and conclusions. We draw selected studies on healthy and diseased hearts, aortas, and pulmonary arteries achieved through the incorporation of tissue mechanics, hemodynamics, and fluid-structure interaction methods paired with patient-specific data acquired with medical imaging in inverse modeling approaches.

Comparison of axial and sagittal spinal cord motion measurements in degenerative cervical myelopathy

BACKGROUND AND PURPOSE

The timing of decision-making for a surgical intervention in patients with mild degenerative cervical myelopathy (DCM) is challenging. Spinal cord motion phase contrast MRI (PC-MRI) measurements can reveal the extent of dynamic mechanical strain on the spinal cord to potentially identify high-risk patients. This study aims to determine the comparability of axial and sagittal PC-MRI measurements of spinal cord motion with the prospect of improving the clinical workup.

METHODS

Sixty-four DCM patients underwent a PC-MRI scan assessing spinal cord motion. The agreement of axial and sagittal measurements was determined by means of intraclass correlation coefficients (ICCs) and Bland-Altman analyses.

RESULTS

The comparability of axial and sagittal PC-MRI measurements was good to excellent at all cervical levels (ICCs motion amplitude: .810-.940; p &lt; .001). Significant differences between axial and sagittal amplitude values could be found at segments C3 and C4, while its magnitude was low (C3: 0.07 ± 0.19 cm/second; C4: -0.12 ± 0.30 cm/second). Bland-Altman analysis showed a good agreement between axial and sagittal PC-MRI scans (coefficients of repeatability: minimum -0.23 cm/second at C2; maximum -0.58 cm/second at C4). Subgroup analysis regarding anatomic conditions (stenotic vs. nonstenotic segments) and different velocity encoding (2 vs. 3 cm/second) showed comparable results.

CONCLUSIONS

This study demonstrates good comparability between axial and sagittal spinal cord motion measurements in DCM patients. To this end, axial and sagittal PC-MRI are both accurate and sensitive in detecting pathologic cord motion. Therefore, such measures could identify high-risk patients and improve clinical decision-making (ie, timing of decompression).