4D Flow Data Research Articles

The value of blood flow velocity and pressure gradient in differentiating patients with different types of heart failure.

Patients with different types of heart failure (HF) exhibit varying rates of blood flow through cardiac chambers and pressure gradients across the aortic valve, attributed to differing degrees of myocardial contractility. Assessment of these dynamics offers insights into early HF diagnosis. This study aimed to analyze left ventricular outflow tract (LVOT) blood flow parameters, specifically peak blood flow velocity and pressure gradient derived from four-dimensional flow cardiovascular magnetic resonance (4D flow CMR), and to evaluate 4D flow CMR's utility in distinguishing HF types. This prospective cross-sectional study recruited 115 HF patients from January 2019 to May 2022 at the General Hospital of Ningxia Medical University, classified by the New York Heart Association Cardiac Function Classification of Heart Failure as class II-IV, alongside a control group (n=30). Participants underwent cardiovascular magnetic resonance (CMR), including 4D flow. HF patients were categorized into heart failure with reduced ejection fraction (HFrEF, n=55), heart failure with mildly reduced ejection fraction (HFmrEF, n=30), and heart failure with preserved ejection fraction (HFpEF, n=30), based on ejection fraction. The cardiac functional parameters and aortic valve flow indices were measured using Circle Cardiovascular Imaging. LVOT 4D flow data were obtained 3 mm below the junction of the aortic valve leaflets, assessing peak velocities above and below the valve. Differences in cardiac function and blood flow parameters between groups were analyzed using one-way analysis of variance (ANOVA). The accuracy of these parameters in identifying subgroups was assessed using the receiver operating characteristic (ROC) curve. Analysis of conventional cardiac function parameters revealed that left ventricular ejection fraction (LVEF) was significantly lower in the HFrEF and HFmrEF groups compared to the HFpEF and control groups (P<0.01). Additionally, end-diastolic volume and end-systolic volume were significantly higher in the HFrEF and HFmrEF groups than in the HFpEF and control groups (P<0.01). However, there were no significant differences in cardiac function parameters between the HFpEF and control groups (P>0.05). Significant differences were observed in aortic valve peak pressure gradients (Supra-APGmax) among the four study groups (5.01±1.09 vs. 6.23±2.94 vs. 7.63±1.81 vs. 8.89±2.97 mmHg, P<0.05). Aortic valve peak velocities in the HFrEF group differed significantly from the HFpEF and control groups (111.31±12.05 cm/s vs. 137.2±16 vs. 147.15±24.55 cm/s, P<0.001). The ROC curve for the pressure gradient below the aortic valve had an area under the curve (AUC) of 0.728 [95% confidence interval (CI): 0.591-0.864, P=0.002], with an optimal threshold of 4.72 mmHg (sensitivity: 0.8, specificity: 0.7, Youden index: 0.5). HF patients exhibit reduced pressure gradients across the aortic valve during systole, indicative of altered intracardiac blood flow dynamics. Combining aortic valve velocities and pressure gradients can aid in distinguishing different types of HF, including HFpEF patients.

Validation of 2D flow MRI for helical and vortical flows

PurposeThe main objective of this study was to develop two-dimensional (2D) phase contrast (PC) methods to quantify the helicity and vorticity of blood flow in the aortic root.MethodsThis proof-of-concept study...

Abstract WP217: 4D Flow MRI Monitoring of Pediatric Brain Arteriovenous Malformations: A Feasibility Study

Introduction: While some brain arteriovenous malformations (bAVM) demonstrate long-term stability, others demonstrate dynamic changes (new aneurysms or venous outflow stenosis) that confer higher rupture risk even after apparent cure. The objective of this study is to evaluate the feasibility and efficacy of 4D Flow MRI to quantitatively assess the hemodynamic changes in pediatric bAVMs following treatment and its use for early detection of high-risk changes. Methods: We have prospectively enrolled 20 children (mean age 11.8 ± 3.8 years) with bAVM into a single center cohort study. 4D Flow MRI was acquired in a transverse slab covering the circle of Willis using prospective gating, compressed sensing, and a resolution of 0.7 x 0.7 x 1.0 mm 3 on a 3T GE Discovery MR750 scanner. MRI at baseline and post-intervention (6-month and 1-year follow up) has been acquired for 5 patients thus far. 4D Flow data was post-processed by segmenting the main cerebral arteries and calculating cardiac-averaged flow rates in cross-sections orthogonal to the centerline of each vessel segment. Results: The flow rate accuracy was evaluated by calculating the flow conservation at the right/left internal carotid and basilar junctions, yielding average errors of 10.7% ± 12.7, 14.0% ± 9.5, and 19.3% ± 13.8 respectively. The hemisphere containing the bAVM received an average of 57.2% ± 2.8 of total brain flow at baseline, which decreased to an average of 51.2% ± 2.9 1-year follow-up. In 2 of 5 patients, the flow laterality between hemispheres flipped post-intervention with increased flow to the contralateral hemisphere. In all the post-intervention studies, we observed a decrease in flow and maximum velocities, as well as an increase in pulsatility index, in the primary feeding arteries to the bAVM (Figure). Conclusion: These preliminary results indicate that 4D Flow MRI may be a robust and useful tool for monitoring changes in intracranial flow patterns after bAVM treatment in children.

Toward accurate and fast velocity quantification with 3D ultrashort TE phase-contrast imaging.

Traditional phase-contrast MRI is affected by displacement artifacts caused by non-synchronized spatial- and velocity-encoding time points. The resulting inaccurate velocity maps can affect the accuracy of derived hemodynamic parameters. This study proposes and characterizes a 3D radial phase-contrast UTE(PC-UTE) sequence to reduce displacement artifacts. Furthermore, it investigates the displacement of a standard Cartesian flow sequence by utilizing a displacement-free synchronized-single-point-imaging MR sequence (SYNC-SPI) that requires clinically prohibitively long acquisition times. 3D flow data was acquired at 3T at three different constant flow rates and varying spatial resolutions in a stenotic aorta phantom using the proposed PC-UTE, a Cartesian flow sequence, and a SYNC-SPI sequence as reference. Expected displacement artifacts were calculated from gradient timing waveforms and compared to displacement values measured in the in vitro flow experiments. The PC-UTE sequence reduces displacement and intravoxel dephasing, leading to decreased geometric distortions and signal cancellations in magnitude images, and more spatially accurate velocity quantification compared to the Cartesian flow acquisitions; errors increase with velocity and higher spatial resolution. PC-UTE MRI can measure velocity vector fields with greater accuracy than Cartesian acquisitions (although pulsatile fields were not studied) and shorter scan times than SYNC-SPI. As such, this approach is superior to traditional Cartesian 3D and 4D flow MRI when spatial misrepresentations cannot be tolerated, for example, when computational fluid dynamics simulations are compared to or combined with in vitro or in vivo measurements, or regional parameters such as wall shear stress are of interest.

Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging

BackgroundTime-resolved, three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI) plays an important role in assessing cardiovascular diseases. However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data introduces variability and limits reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. This paper explores the potential of deep learning to improve 4D flow MRI segmentation by developing models for automatic segmentation and analyzes the impact of the training data on the generalization of the model across different sites, scanner vendors, sequences, and pathologies. MethodsThe study population consists of 260 4D flow MRI datasets, including subjects without known aortic pathology, healthy volunteers, and patients with bicuspid aortic valve (BAV) examined at different hospitals. The dataset was split to train segmentation models on subsets with different representations of characteristics such as pathology, gender, age, scanner model, vendor, and field strength. An enhanced 3D U-net convolutional neural network (CNN) architecture with residual units was trained for 2D+t aortic cross-sectional segmentation. The model performance was evaluated using Dice score, Hausdorff distance, and average symmetric surface distance on test data, datasets with characteristics not represented in the training set (model-specific), and an overall evaluation set. Standard diagnostic flow parameters were computed and compared with manual segmentation results using Bland-Altman analysis and interclass correlation. ResultsThe representation of technical factors such as scanner vendor and field strength in the training dataset had the strongest influence on the overall segmentation performance. Age had a greater impact than gender. Models solely trained on BAV patients’ datasets performed well on datasets of healthy subjects but not vice versa. ConclusionThis study highlights the importance of considering a heterogeneous dataset for the training of widely applicable automatic CNN segmentations in 4D flow MRI, with a particular focus on the inclusion of different pathologies and technical aspects of data acquisition.

Combined free-running four-dimensional anatomical and flow magnetic resonance imaging with native contrast using Synchronization of Neighboring Acquisitions by Physiological Signals

Four-dimensional (4D) flow magnetic resonance imaging (MRI) often relies on the injection of gadolinium- or iron-oxide-based contrast agents to improve vessel delineation. In this work, a novel technique is developed to acquire and reconstruct 4D flow data with excellent dynamic visualization of blood vessels but without the need for contrast injection. Synchronization of Neighboring Acquisitions by Physiological Signals (SyNAPS) uses pilot tone (PT) navigation to retrospectively synchronize the reconstruction of two free-running three-dimensional radial acquisitions, to create co-registered anatomy and flow images. Thirteen volunteers and two Marfan syndrome patients were scanned without contrast agent using one free-running fast interrupted steady-state (FISS) sequence and one free-running phase-contrast MRI (PC-MRI) sequence. PT signals spanning the two sequences were recorded for retrospective respiratory motion correction and cardiac binning. The magnitude and phase images reconstructed, respectively, from FISS and PC-MRI, were synchronized to create SyNAPS 4D flow datasets. Conventional two-dimensional (2D) flow data were acquired for reference in ascending (AAo) and descending aorta (DAo). The blood-to-myocardium contrast ratio, dynamic vessel area, net volume, and peak flow were used to compare SyNAPS 4D flow with Native 4D flow (without FISS information) and 2D flow. A score of 0-4 was given to each dataset by two blinded experts regarding the feasibility of performing vessel delineation. Blood-to-myocardium contrast ratio for SyNAPS 4D flow magnitude images (1.5±0.3) was significantly higher than for Native 4D flow (0.7±0.1, p<0.01) and was comparable to 2D flow (2.3±0.9, p=0.02). Image quality scores of SyNAPS 4D flow from the experts (M.P.: 1.9±0.3, E.T.: 2.5±0.5) were overall significantly higher than the scores from Native 4D flow (M.P.: 1.6±0.6, p=0.03, E.T.: 0.8±0.4, p<0.01) but still significantly lower than the scores from the reference 2D flow datasets (M.P.: 2.8±0.4, p<0.01, E.T.: 3.5±0.7, p<0.01). The Pearson correlation coefficient between the dynamic vessel area measured on SyNAPS 4D flow and that from 2D flow was 0.69±0.24 for the AAo and 0.83±0.10 for the DAo, whereas the Pearson correlation between Native 4D flow and 2D flow measurements was 0.12±0.48 for the AAo and 0.08±0.39 for the DAo. Linear correlations between SyNAPS 4D flow and 2D flow measurements of net volume (r2 =0.83) and peak flow (r2 =0.87) were larger than the correlations between Native 4D flow and 2D flow measurements of net volume (r2 =0.79) and peak flow (r2 =0.76). The feasibility and utility of SyNAPS were demonstrated for joint whole-heart anatomical and flow MRI without requiring electrocardiography gating, respiratory navigators, or contrast agents. Using SyNAPS, a high-contrast anatomical imaging sequence can be used to improve 4D flow measurements that often suffer from poor delineation of vessel boundaries in the absence of contrast agents.

Impact of the evaluation method on 4D flow-derived diastolic transmitral and myocardial peak velocities: Comparison with echocardiography

PurposeTo compare agreement of different evaluation methods of magnetic resonance (MR) 4D flow-derived diastolic transmitral and myocardial peak velocities as well as their ratios, using echocardiography as reference. MethodsIn this prospective study, 60 subjects without symptoms of cardiovascular disease underwent echocardiography and non-contrast 3 T MR 4D flow imaging of the heart. Early- (E) and late-diastolic (A) transmitral peak filling velocities were evaluated from 4D flow data using three different strategies: 1) at the mitral valve tips in short-axis orientation (SA-method), 2) between the mitral valve tips in 4-chamber orientation (4-chamber-method), and 3) as maximal velocities in the transmitral inflow volume (max-velocity-method). Septal, lateral and average early-diastolic myocardial peak velocities (e’) were derived from the myocardial tissue in the vicinity of the mitral valve. 4D flow parameters were compared with echocardiography by correlation and Bland-Altman analysis. ResultsAll 4D flow-derived E, A and E/A values correlated with echocardiography (r = 0.65–0.73, 0.75–0.83 and 0.74–0.86, respectively). While the SA- and 4-chamber-methods substantially underestimated E and A compared to echocardiography (p < 0.001), the max-velocity-method provided E (p = 0.13) and E/A (p = 0.07) without significant bias. Septal, lateral and average e’ from 4D flow as well as the max-velocity-method-derived E/e’ correlated with echocardiographic measurements (r = 0.64–0.81) and showed no significant bias (p = 0.26–0.54). ConclusionMR 4D flow imaging allows precise and accurate evaluation of transmitral and myocardial peak velocities for characterization of LV diastolic function without significant bias to echocardiography, when transmitral velocities are assessed from the transmitral inflow volume. This enables the use of validated echocardiography threshold values.

MR 4D flow–derived left atrial acceleration factor for differentiating advanced left ventricular diastolic dysfunction

ObjectivesThe magnetic resonance (MR) 4D flow imaging–derived left atrial (LA) acceleration factor α was recently introduced as a means to non-invasively estimate LA pressure. We aimed to investigate the association of α with the severity of left ventricular (LV) diastolic dysfunction using echocardiography as the reference method.MethodsEchocardiographic assessment of LV diastolic function and 3-T cardiac MR 4D flow imaging were prospectively performed in 94 subjects (44 male/50 female; mean age, 62 ± 12 years). LA early diastolic peak outflow velocity (vE), systolic peak inflow velocity (vS), and early diastolic peak inflow velocity (vD) were evaluated from 4D flow data. α was calculated from α = vE / [(vS + vD) / 2]. Mean parameter values were compared by t-test; diagnostic performance of α in predicting diastolic (dys)function was investigated by receiver operating characteristic curve analysis.ResultsMean α values were 1.17 ± 0.14, 1.20 ± 0.08, 1.33 ± 0.15, 1.77 ± 0.18, and 2.79 ± 0.69 for grade 0 (n = 51), indeterminate (n = 9), grade I (n = 13), grade II (n = 13), and grade III (n = 8) LV diastolic (dys)function, respectively. α differed between subjects with non-advanced (grade < II) and advanced (grade ≥ II) diastolic dysfunction (1.20 ± 0.15 vs. 2.16 ± 0.66, p < 0.001). The area under the curve (AUC) for detection of advanced diastolic dysfunction was 0.998 (95% CI: 0.958–1.000), yielding sensitivity of 100% (95% CI: 84–100%) and specificity of 99% (95% CI: 93–100%) at cut-off α ≥ 1.58. The AUC for differentiating grade III diastolic dysfunction was also 0.998 (95% CI: 0.976–1.000) at cut-off α ≥ 2.14.ConclusionThe 4D flow–derived LA acceleration factor α allows grade II and grade III diastolic dysfunction to be distinguished from non-advanced grades as well as from each other.Clinical relevance statementAs a single continuous parameter, the 4D flow–derived LA acceleration factor α shows potential to simplify the multi-parametric imaging algorithm for diagnosis of advanced LV diastolic dysfunction, thereby identifying patients at increased risk for cardiovascular events.Key Points• Detection of advanced diastolic dysfunction is typically performed using a complex, multi-parametric approach.• The 4D flow–derived left atrial acceleration factor α alone allows accurate detection of advanced left ventricular diastolic dysfunction.• As a single continuous parameter, the left atrial acceleration factor α could simplify the diagnosis of advanced diastolic dysfunction.

Resolution-based comparative analysis of 4D-phase-contrast magnetic resonance images and hemodynamic simulations of the aortic arch

Abstract Introduction: In this study, phase-contrast magnetic resonance imaging (PC-MRI) 4D flow data for patients with bicuspid aortic valve (BAV) were analyzed based on the spatial resolution of the images. BAV is a congenital heart defect characterized by the presence of two cusps in the aortic valve, leading to various complications. PC-MRI is a noninvasive imaging technique used to assess the hemodynamics in patients. However, interpretation of PC-MRI data can be challenging due to complex hemodynamics, which makes numerical simulations necessary to complement the results. Methods:Within this study PC-MRI 4D flow data in the aortic arch (AA) were compared with hemodynamic numerical simulations based on the MRI resolution for six patient-specific AAs with BAV. First, the segmentations were reconstructed and divided into three groups based on the resolution. Second, the numerical simulations were performed and resulting hemodynamics compared to PC-MRI results at three time points in the cardiac cycle using velocity and wall shear stress. Results: Results from group 1 (highest resolution) show an overestimation of wall shear stress (WSS) values during peaksystole in the three arteries and along the AA and descending aorta (DAo). Nevertheless, velocity mean values show an overall good agreement. Group 2 shows higher velocity magnitudes in the simulation than in PC-MRI. Within group 3, velocity values differ from the measurements. Conclusion: The study shows that with a low resolution, flow patterns can already be evaluated based on PC-MRI, but to get an insight into the flow quantitatively a higher resolution is necessary.

Evaluation of left ventricular and left atrial volumetric function from native MR multislice 4D flow magnitude data

ObjectivesTo assess the feasibility, precision, and accuracy of left ventricular (LV) and left atrial (LA) volumetric function evaluation from native magnetic resonance (MR) multislice 4D flow magnitude images.Materials & MethodsIn this prospective study, 60 subjects without signs or symptoms of heart failure underwent 3T native cardiac MR multislice 4D flow and bSSFP-cine realtime imaging. LV and LA volumetric function parameters were evaluated from 4D flow magnitude (4D flow-cine) and bSSFP-cine data using standard software to obtain end-diastolic volume (EDV), end-systolic volume (ESV), ejection-fraction (EF), stroke-volume (SV), LV muscle mass (LVM), LA maximum volume, LA minimum volume, and LA total ejection fraction (LATEF). Stroke volumes derived from both imaging methods were further compared to 4D pulmonary artery flow-derived net forward volumes (NFV). Methods were compared by correlation and Bland-Altman analysis.ResultsVolumetric function parameters from 4D flow-cine and bSSFP-cine showed high to very high correlations (r = 0.83-0.98). SV, LA volumes and LATEF did not differ between methods. LV end-diastolic and end-systolic volumes were slightly underestimated (EDV: –2.9 ± 5.8 mL; ESV: -2.3 ± 3.8 mL), EF was slightly overestimated (EF: 0.9 ± 2.6%), and LV mass was considerably overestimated (LVM: 39.0 ± 11.4 g) by 4D flow-cine imaging. SVs from both methods correlated very highly with NFV (r = 0.91 in both cases) and did not differ from NFV.ConclusionNative multislice 4D flow magnitude data allows precise evaluation of LV and LA volumetric parameters; however, apart from SV, LV volumetric parameters demonstrate bias and need to be referred to their respective normal values.Clinical relevance statementVolumetric function assessment from native multislice 4D flow magnitude images can be performed with routinely used clinical software, facilitating the application of 4D flow as a one-stop-shop functional cardiac MR exam, providing consistent, simultaneously acquired, volume and flow data.Key points• Native multislice 4D flow imaging allows evaluation of volumetric left ventricular and atrial function parameters.• Left ventricular and left atrial function parameters derived from native multislice 4D flow data correlate highly with corresponding standard cine-derived parameters.• Multislice 4D flow-derived volumetric stroke volume and net forward volume do not differ.

Automatic 4D Flow MRI Segmentation Using the Standardized Difference of Means Velocity.

We present a method to automatically segment 4D flow magnetic resonance imaging (MRI) by identifying net flow effects using the standardized difference of means (SDM) velocity. The SDM velocity quantifies the ratio between the net flow and observed flow pulsatility in each voxel. Vessel segmentation is performed using an F-test, identifying voxels with significantly higher SDM velocity values than background voxels. We compare the SDM segmentation algorithm against pseudo-complex difference (PCD) intensity segmentation of 4D flow measurements in in vitro cerebral aneurysm models and 10 in vitro Circle of Willis (CoW) datasets. We also compared the SDM algorithm to convolutional neural network (CNN) segmentation in 5 thoracic vasculature datasets. The in vitro flow phantom geometry is known, while the ground truth geometries for the CoW and thoracic aortas are derived from high-resolution time-of-flight (TOF) magnetic resonance angiography and manual segmentation, respectively. The SDM algorithm demonstrates greater robustness than PCD and CNN approaches and can be applied to 4D flow data from other vascular territories. The SDM to PCD comparison demonstrated an approximate 48% increase in sensitivity in vitro and 70% increase in the CoW, respectively; the SDM and CNN sensitivities were similar. The vessel surface derived from the SDM method was 46% closer to the in vitro surfaces and 72% closer to the in vitro TOF surfaces than the PCD approach. The SDM and CNN approaches both accurately identify vessel surfaces. The SDM algorithm is a repeatable segmentation method, enabling reliable computation of hemodynamic metrics associated with cardiovascular disease.

Comparison of free-running whole-heart 5D and 4D flow imaging to standard 2D phase contrast in patients with right-sided congenital heart disease

Abstract Funding Acknowledgements Type of funding sources: None. Background Flow measurements by cardiac magnetic resonance (CMR) are part of a routine assessment in patients with congenital heart disease (CHD). Standard 2D phase contrast (2DPC) sequences, however, require precise prescription of image planes leading to long scan times which could be overcome by 3D-acquisitons covering the entire cardiac anatomy in a single acquisition. Thus, recently introduced accelerated free-breathing 3D whole heart flow sequences promise to simplify CMR exams allowing an easier acquisition and retrospective flow measurements in virtually all vessels. Purpose Comparison of standard 2DPC to an accelerated 4D flow sequence with prospective respiratory navigation (1), and a free-running radial fully self-gated respiratory and cardiac motion-resolved 5D flow sequence (5D flow) (2,3). Methods Patients with right-sided CHD and no intra- or extracardiac shunt underwent a CMR on a 1.5T scanner. 2DPC flow sequences and a 4D flow data set were obtained (1). In addition, a whole-heart free-running 3D radial PC CMR sequence was acquired (2). Using a compressed-sensing-based image reconstruction framework (2,3), 5D flow images were obtained, and the end-expiratory phase was used for analysis. 3D vessel segmentation was based on retrospectively computed phase-contrast angiography images for each 4D and 5D flow datasets. Flow measured in the ascending, descending aorta (AA, DA), main, right and left pulmonary artery (MPA, RPA, LPA) and superior vena cava (SVC) as well as the internal consistency for determination of systemic and pulmonary output were compared between all three sequences by Bland-Altman analyses. Results Twenty CHD patients (age 31±11y, 7 women, tetralogy of Fallot N=7, Ross operation N=10; pulmonary regurgitation, atrial switch and Ebstein’s disease each N=1) were included. Mean peak velocity of MPA/ right ventricle to pulmonary artery conduit was 1.9 (range 0.9; 3.5) m/s. Compared to 2DPC, both 4D and 5D flow moderately underestimated absolute flow with significant lower values for the RPA for 4D flow (Table 1). The bias (i.e. the mean absolute difference) of MPA flow between 2DPC and 4D flow was related to the MPA peak velocity (r2=0.3, p = 0.03) with a similar trend for the comparison of 2DPC vs. 5D flow (r2=0.142, p = 0.141). The comparison of the internal consistency for determination of systemic and pulmonary output showed similar results for 2DPC and 5D flow and a slightly lower agreement for the 4D flow sequence (Table 2). Conclusions In this preliminary study, 4D and 5D flow provide results comparable to 2DPC, however, both sequences showed a trend towards lower absolute flow values. Complex flow patterns due to the right-sided pathologies appear to have an impact on the difference observed between sequences as shown for the MPA. The internal consistency of flow quantification was similar for the 2DPC and 5D flow sequences. The presented observations are currently being evaluated in a larger study.

Feasibility of Wave Intensity Analysis from 4D Cardiovascular Magnetic Resonance Imaging Data

Congenital heart defects (CHD) introduce haemodynamic changes; e.g., bicuspid aortic valve (BAV) presents a turbulent helical flow, which activates aortic pathological processes. Flow quantification is crucial for diagnostics and to plan corrective strategies. Multiple imaging modalities exist, with phase contrast magnetic resonance imaging (PC-MRI) being the current gold standard; however, multiple predetermined site measurements may be required, while 4D MRI allows for measurements of area (A) and velocity (U) in all spatial dimensions, acquiring a single volume and enabling a retrospective analysis at multiple locations. We assessed the feasibility of gathering hemodynamic insight into aortic hemodynamics by means of wave intensity analysis (WIA) derived from 4D MRI. Data were collected in n = 12 BAV patients and n = 7 healthy controls. Following data acquisition, WIA was successfully derived at three planes (ascending, thoracic and descending aorta) in all cases. The values of wave speed were physiological and, while the small sample limited any clinical interpretation of the results, the study shows the possibility of studying wave travel and wave reflection based on 4D MRI. Below, we demonstrate for the first time the feasibility of deriving wave intensity analysis from 4D flow data and open the door to research applications in different cardiovascular scenarios.

Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging

Purpose: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI, validated against 4D flow. Methods: A convolutional neural network (CNN) was implemented, taking cine MRI as the input and the in-plane velocity derived from the 4D flow acquisition as the ground truth. The method was evaluated using velocity vector end-point error (EPE) and angle error. Additionally, the E/A ratio and diastolic function classification derived from the predicted velocities were compared to those derived from 4D flow. Results: For intra-cardiac pixels with a velocity > 5 cm/s, our method achieved an EPE of 8.65 cm/s and angle error of 41.27°. For pixels with a velocity > 25 cm/s, the angle error significantly degraded to 19.26°. Although the averaged blood flow velocity prediction was under-estimated by 26.69%, the high correlation (PCC = 0.95) of global time-varying velocity and the visual evaluation demonstrate a good agreement between our prediction and 4D flow data. The E/A ratio was derived with minimal bias, but with considerable mean absolute error of 0.39 and wide limits of agreement. The diastolic function classification showed a high accuracy of 86.9%. Conclusion: Using a deep learning-based algorithm, intra-cardiac blood flow velocities can be predicted from long-axis cine MRI with high correlation with 4D flow derived velocities. Visualization of the derived velocities provides adjunct functional information and may potentially be used to derive the E/A ratio from conventional CMR exams.

Motion-resolved real-time 4D flow MRI withlow-rank and subspace modeling.

To develop a new motion-resolved real-time four-dimensional (4D) flow MRI method, which enables the quantification and visualization of blood flow velocities with three-directional flow encodings and volumetric coverage without electrocardiogram (ECG) synchronization and respiration control. An integrated imaging method is presented for real-time 4D flow MRI, which encompasses data acquisition, image reconstruction, and postprocessing. The proposed method features a specialized continuous -space acquisition scheme, which collects two sets of data (i.e., training data and imaging data) in an interleaved manner. By exploiting strong spatiotemporal correlation of 4D flow data, it reconstructs time-series images from highly-undersampled -space measurements with a low-rank and subspace model. Through data-binning-based postprocessing, it constructs a five-dimensional dataset (i.e., x-y-z-cardiac-respiratory), from which respiration-dependent flow information is further analyzed. The proposed method was evaluated in aortic flow imaging experiments with ten healthy subjects and two patients with atrial fibrillation. The proposed method achieves 2.4 mm isotropic spatial resolution and 34.4 ms temporal resolution for measuring the blood flow of the aorta. For the healthy subjects, it provides flow measurements in good agreement with those from the conventional 4D flow MRI technique. For the patients with atrial fibrillation, it is able to resolve beat-by-beat pathological flow variations, which cannot be obtained from the conventional technique. The postprocessing further provides respiration-dependent flow information. The proposed method enables high-resolution motion-resolved real-time 4D flow imaging without ECG gating and respiration control. It is able to resolve beat-by-beat blood flow variations as well as respiration-dependent flow information.

Assessment of Beat-To-Beat Variability in Left Atrial Hemodynamics Using Real Time Phase Contrast MRI in Patients With Atrial Fibrillation.

Hemodynamic assessment of left atrial (LA) flow using phase contrast MRI provides insight into thromboembolic risk in atrial fibrillation (AF). However, conventional flow imaging techniques are averaged over many heartbeats. To evaluate beat-to-beat variability and LA hemodynamics in patients with AF using real time phase contrast (RTPC) MRI. Prospective. Thirty-five patients with history of AF (68 ± 10 years, nine female), 10 healthy controls (57 ± 19 years, four female). 5T, 2D RTPC with through-plane velocity-encoded gradient echo sequence and 4D flow MRI with three-directional velocity-encoded gradient echo sequence. RTPC was continuously acquired for a mid-LA slice in all subjects. 4D flow data were interpolated at the RTPC location and normally projected for comparison with RTPC. RR intervals extracted from RTPC were used to calculate heart rate variability (HRV=interquartile range over median × 100%). Patients were classified into low (<9.7%) and high (>9.7%) HRV groups. LA peak/mean velocity and stasis (%velocities < 5.8cm/sec) were calculated from segmented 2D images. Variability in RTPC flow metrics was quantified by coefficient of variation (CV) over all cycles. Pearson's correlation coefficient (r), Bland-Altman analysis, Kruskal-Wallis test. A P value < 0.05 was considered statistically significant. RTPC and 4D flow measurements were strongly/significantly correlated for all hemodynamic parameters (R2 =0.75-0.83) in controls. Twenty-four patients had low HRV (mean=4 ± 2%) and 11 patients had high HRV (27 ± 9%). In patients, increased HRV was significantly correlated with CV of peak velocity (r=0.67), mean velocity (r=0.51), and stasis (r=0.41). A stepwise decrease in peak/mean velocity and increase in stasis was observed when comparing controls vs. low HRV vs. high HRV groups. Mean velocity and stasis differences were significant for control vs. high HRV groups. RTPC may be suitable for assessing the impact of HRV on hemodynamics and provide insight for AF management in highly arrhythmic patients. 1 TECHNICAL EFFICACY: Stage 2.

FlowRAU-Net: Accelerated 4D Flow MRI of Aortic Valvular Flows With a Deep 2D Residual Attention Network.

In this work, we propose a novel deep learning reconstruction framework for rapid and accurate reconstruction of 4D flow MRI data. Reconstruction is performed on a slice-by-slice basis by reducing artifacts in zero-filled reconstructed complex images obtained from undersampled k-space. A deep residual attention network FlowRAU-Net is proposed, trained separately for each encoding direction with 2D complex image slices extracted from complex 4D images at each temporal frame and slice position. The network was trained and tested on 4D flow MRI data of aortic valvular flow in 18 human subjects. Performance of the reconstructions was measured in terms of image quality, 3-D velocity vector accuracy, and accuracy in hemodynamic parameters. Reconstruction performance was measured for three different k-space undersamplings and compared with one state of the art compressed sensing reconstruction method and three deep learning-based reconstruction methods. The proposed method outperforms state of the art methods in all performance measures for all three different k-space undersamplings. Hemodynamic parameters such as blood flow rate and peak velocity from the proposed technique show good agreement with reference flow parameters. Visualization of the reconstructed image and velocity magnitude also shows excellent agreement with the fully sampled reference dataset. Moreover, the proposed method is computationally fast. Total 4D flow data (including all slices in space and time) for a subject can be reconstructed in 69 seconds on a single GPU. Although the proposed method has been applied to 4D flow MRI of aortic valvular flows, given a sufficient number of training samples, it should be applicable to other arterial flows.

Risk Factors for Cerebrovascular Events in Moyamoya Angiopathy Using 4D Flow MRI: A Pilot Study.

Little is known about internal carotid artery (ICA) hemodynamics in patients with moyamoya angiopathy (MMA) and its role in cerebrovascular events. To characterize ICA hemodynamics in MMA patients by 4D flow MRI and investigate its relationship with cerebrovascular events. Prospective. Seventy MMA patients (50 years old ± 9, 30 males). Time-resolved three-directional velocity encoded fast field echo sequence (4D flow) MRI, T1-weighted fast field echo sequence, T2 weighted turbo spin echo sequence, diffusion weighted echo planar imaging; T2-weighted fluid-attenuated inversion recovery turbo spin echo sequence, susceptibility weighted fast field echo sequence, and time-of-flight MR angiography fast field echo sequence at 3.0T. ICA hemodynamics (maximum and average velocity [Vmax , Vavg ], average blood flow [Flowavg ], and wall shear stress) were analyzed based on 4D flow data. Cerebral infarction, defined as the occurrence of events, in 124 brain hemispheres was determined according to clinical symptoms and conventional brain MR imaging. The independent-samples T test was used to evaluate differences in ICA hemodynamics between infarcted and non-infarcted hemispheres. Binary logistic regression was performed to investigate the relationship between ICA hemodynamics and events. A P value < 0.05 was considered statistically significant. Sixty-one infarcted hemispheres (eight hemispheres with acute ischemic damage, 30 with chronic ischemic damage, and 23 with chronic hemorrhagic damage) had cerebrovascular events and 63 non-infarcted hemispheres did not. The hemodynamic parameters in the infarcted hemispheres (Vmax : P < 0.001; Vavg : P=0.003; and Flowavg : P=0.004) were significantly lower than those in the non-infarcted hemispheres. However, Vmax (P=0.052), Vavg (P=0.107), and Flowavg (P=0.074) were not significantly different among hemispheres with acute ischemic lesions, chronic ischemic lesions and chronic hemorrhagic lesions. Vmax (odds ratio 3.033, 95% CI: 1.075-8.562) was independently associated with cerebrovascular events. Vmax maybe a higher risk factor of cerebrovascular events in MMA patients. 2 TECHNICAL EFFICACY STAGE: 3.

Motion-corrected 4D-Flow MRI for neurovascular applications

Neurovascular 4D-Flow MRI has emerged as a powerful tool for comprehensive cerebrovascular hemodynamic characterization. Clinical studies in at risk populations such as aging adults indicate hemodynamic markers can be confounded by motion-induced bias. This study develops and characterizes a high fidelity 3D self-navigation approach for retrospective rigid motion correction of neurovascular 4D-Flow data. A 3D radial trajectory with pseudorandom ordering was combined with a multi-resolution low rank regularization approach to enable high spatiotemporal resolution self-navigators from extremely undersampled data. Phantom and volunteer experiments were performed at 3.0T to evaluate the ability to correct for different amounts of induced motions. In addition, the approach was applied to clinical-research exams from ongoing aging studies to characterize performance in the clinical setting. Simulations, phantom and volunteer experiments with motion correction produced images with increased vessel conspicuity, reduced image blurring, and decreased variability in quantitative measures. Clinical exams revealed significant changes in hemodynamic parameters including blood flow rates, flow pulsatility index, and lumen areas after motion correction in probed cerebral arteries (Flow: P<0.001 Lt ICA, P=0.002 Rt ICA, P=0.004 Lt MCA, P=0.004 Rt MCA; Area: P<0.001 Lt ICA, P<0.001 Rt ICA, P=0.004 Lt MCA, P=0.004 Rt MCA; flow pulsatility index: P=0.042 Rt ICA, P=0.002 Lt MCA). Motion induced bias can lead to significant overestimation of hemodynamic markers in cerebral arteries. The proposed method reduces measurement bias from rigid motion in neurovascular 4D-Flow MRI in challenging populations such as aging adults.

Location matters: Offset in tissue-engineered vascular graft implantation location affects wall shear stress in porcine models

ObjectiveAlthough surgical simulation using computational fluid dynamics has advanced, little is known about the accuracy of cardiac surgical procedures after patient-specific design. We evaluated the effects of discrepancies in location for patient-specific simulation and actual implantation on hemodynamic performance of patient-specific tissue-engineered vascular grafts (TEVGs) in porcine models. MethodsMagnetic resonance angiography and 4-dimensional (4D) flow data were acquired in porcine models (n = 11) to create individualized TEVGs. Graft shapes were optimized and manufactured by electrospinning bioresorbable material onto a metal mandrel. TEVGs were implanted 1 or 3 months postimaging, and postoperative magnetic resonance angiography and 4D flow data were obtained and segmented. Displacement between intended and observed TEVG position was determined through center of mass analysis. Hemodynamic data were obtained from 4D flow analysis. Displacement and hemodynamic data were compared using linear regression. ResultsPatient-specific TEVGs were displaced between 1 and 8 mm during implantation compared with their surgically simulated, intended locations. Greater offset between intended and observed position correlated with greater wall shear stress (WSS) in postoperative vasculature (P < .01). Grafts that were implanted closer to their intended locations showed decreased WSS. ConclusionsPatient-specific TEVGs are designed for precise locations to help optimize hemodynamic performance. However, if TEVGs were implanted far from their intended location, worse WSS was observed. This underscores the importance of not only patient-specific design but also precision-guided implantation to optimize hemodynamics in cardiac surgery and increase reproducibility of surgical simulation.