Flow Magnetic Resonance Imaging Data Research Articles

Denoising four-dimensional flow magnetic resonance imaging data using a split-and-overlap approach via singular value decomposition

Four-dimensional flow magnetic resonance imaging (4D flow MRI) offers a powerful tool for visualizing fluid flows, critical for both diagnosing cardiovascular diseases and analyzing engineering fluid dynamics. Despite its potential in medical research, the clinical applicability of 4D flow MRI often faces challenges due to inherent noise. To mitigate this, we introduce the split-and-overlap singular value decomposition (SOSVD) filter, a distinctive noise reduction approach. Unlike traditional singular value decomposition methods, the SOSVD filter partitions the primary data matrix into overlapping subdomains and then applies singular value decomposition to each subdomain, preserving only the dominant mode for noise attenuation. Evaluations on simulated and experimental flow data within a square duct revealed a significant decrease in root mean square noise metrics. Moreover, when applied to in vivo aortic data, the SOSVD filter enhanced various flow determinants, including divergence, velocity gradients, streamlines, and velocity coherence. Thus, the SOSVD method presents a promising avenue for augmenting noise reduction in 4D flow MRI, potentially elevating diagnostic accuracy and enriching cardiovascular disease research.

Interval changes in four-dimensional flow-derived in vivo hemodynamics stratify aortic growth in type B aortic dissection patients

BackgroundAortic diameter growth in type B aortic dissection (TBAD) is associated with progressive aortic dilation, resulting in increased mortality in patients with both de novo TBAD (dnTBAD) and residual dissection after type A dissection repair (rTAAD). Preemptive thoracic endovascular aortic repair may improve mortality in patients with TBAD, although it is unclear which patients may benefit most from early intervention. In vivo hemodynamic assessment using four-dimensional (4D) flow magnetic resonance imaging (MRI) has been used to characterize TBAD patients with growing aortas. In this longitudinal study, we investigated whether changes over time in 4D flow derived true and false lumen (TL, FL) hemodynamic parameters correlate with aortic growth rate which is a marker of increased risk. MethodsWe retrospectively identified TBAD patients with baseline and follow-up 4D flow MRI at least 120 days apart. Patients with TBAD intervention before baseline or between scans were excluded. 4D flow MRI data analysis included segmentation of the TL and FL, followed by voxel-wise calculation of TL and FL total kinetic energy (KE), maximum velocity (MV), mean forward flow (FF), and mean reverse flow (RF). Changes over time (Δ) were calculated for all hemodynamic parameters. Maximal diameter in the descending aorta was measured from MR angiogram images acquired at the time of 4D flow. Aortic growth rate was defined as the change in diameter divided by baseline diameter and standardized to scan interval. Results32 patients met inclusion criteria (age: 56.9±14.1 years, Female: 13, n=19 rTAAD, n=13 dnTBAD). Mean follow up time was 538 days (range: 135-1689). Baseline aortic diameter did not correlate with growth rate. In the entire cohort, Δ FL MV (rho=0.37, p=.04) and Δ FL RF (rho=0.45, p=0.01) correlated with growth rate. In rTAAD only, Δ FL MV (rho=0.48, p=.04) and Δ FL RF (rho=0.51, p=0.03) correlated with growth rate, while in dnTBAD only, Δ TL KE (rho=0.63, p=.02) and Δ TL MV (rho=0.69, p=.01) correlated with growth rate. Conclusions4D flow derived longitudinal hemodynamic changes correlate with aortic growth rate in TBAD and may provide additional prognostic value for risk stratification. 4D flow MRI could be integrated into existing imaging protocols to allow for identification of TBAD patients who would benefit from preemptive surgical or endovascular intervention.

Haemodynamic effects of hypertension and type 2 diabetes: Insights from a 4D flow MRI-based personalized cardiovascular mathematical model.

Type 2 diabetes (T2D) and hypertension increase the risk of cardiovascular diseases mediated by whole-body changes to metabolism, cardiovascular structure and haemodynamics. The haemodynamic changes related to hypertension and T2D are complex and subject-specific, however, and not fully understood. We aimed to investigate the haemodynamic mechanisms in T2D and hypertension by comparing the haemodynamics between healthy controls and subjects with T2D, hypertension, or both. For all subjects, we combined 4D flow magnetic resonance imaging data, brachial blood pressure and a cardiovascular mathematical model to create a comprehensive subject-specific analysis of central haemodynamics. When comparing the subject-specific haemodynamic parameters between the four groups, the predominant haemodynamic difference is impaired left ventricular relaxation in subjects with both T2D and hypertension compared to subjects with only T2D, only hypertension and controls. The impaired relaxation indicates that, in this cohort, the long-term changes in haemodynamic load of co-existing T2D and hypertension cause diastolic dysfunction demonstrable at rest, whereas either disease on its own does not. However, through subject-specific predictions of impaired relaxation, we show that altered relaxation alone is not enough to explain the subject-specific and group-related differences; instead, a combination of parameters is affected in T2D and hypertension. These results confirm previous studies that reported more adverse effects from the combination of T2D and hypertension compared to either disease on its own. Furthermore, this shows the potential of personalized cardiovascular models in providing haemodynamic mechanistic insights and subject-specific predictions that could aid in the understanding and treatment planning of patients with T2D and hypertension. KEY POINTS: The combination of 4D flow magnetic resonance imaging data and a cardiovascular mathematical model allows for a comprehensive analysis of subject-specific haemodynamic parameters that otherwise cannot be derived non-invasively. Using this combination, we show that diastolic dysfunction in subjects with both type 2 diabetes (T2D) and hypertension is the main group-level difference between controls, subjects with T2D, subjects with hypertension, and subjects with both T2D and hypertension. These results suggest that, in this relatively healthy population, the additional load of both hypertension and T2D affects the haemodynamic function of the left ventricle, whereas each disease on its own is not enough to cause significant effects under resting conditions. Finally, using the subject-specific model, we show that the haemodynamic effects of diastolic dysfunction alone are not sufficient to explain all the observed haemodynamic differences. Instead, additional subject-specific variations in cardiac and vascular function combine to explain the complex haemodynamics of subjects affected by hypertension and/or T2D.

The exploration of flow pattern in the superior vena cava of healthy adults: A 4D flow magnetic resonance imaging study

Our study sought to investigate the blood flow pattern in the superior vena cava (SVC) of healthy adults and to describe the development and characteristics of the flow pattern using 4D flow magnetic resonance imaging (MRI). 4D flow MRI data with full coverage of SVC and brachiocephalic veins (BVs) were acquired with a 3-Testa MRI in thirty healthy adults (age: 28.70 ​± ​9.09). Hemodynamic parameters in the SVC, including velocity, velocity vector, flow visualization (pathlines, streamlines), flow waveform and energy loss, were obtained with specialized commercial post-processing software based on 4D flow MRI data. This study found that: (1) The SVC has a pulsating flow waveform with double peaks. (2) Based on BVs flow of the SVC pathline visualization, flow patterns could be divided into three development types: twining (n ​= ​14), untwining (n ​= ​9) and no helical flow (n ​= ​7). (3) With the decreasing blood velocity, helical flow areas tended to gradually extend. (4) There were no significant differences in most hemodynamic parameters among the three types. The exploration of the blood flow characteristics of normal SVC may be the first step for capturing pathogenic features before the patients develop symptoms with a certain positive significance. The potential physiological significance of these phenomena deserves more exploration in future studies.

Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning

Changes in cardiovascular hemodynamics are closely related to the development of aortic regurgitation (AR), a type of valvular heart disease. Metrics derived from blood flows are used to indicate AR onset and evaluate its severity. These metrics can be non-invasively obtained using four-dimensional (4D) flow magnetic resonance imaging (MRI), where accuracy is primarily dependent on spatial resolution. However, insufficient resolution often results from limitations in 4D flow MRI and complex aortic regurgitation hemodynamics. To address this, computational fluid dynamics simulations were transformed into synthetic 4D flow MRI data and used to train a variety of neural networks. These networks generated super-resolution, full-field phase images with an upsample factor of 4. Results showed decreased velocity error, high structural similarity scores, and improved learning capabilities from previous work. Further validation was performed on two sets of in vivo 4D flow MRI data and demonstrated success in de-noising flow images. This approach presents an opportunity to comprehensively analyse AR hemodynamics in a non-invasive manner.

Advances in machine learning applications for cardiovascular 4D flow MRI.

Four-dimensional flow magnetic resonance imaging (MRI) has evolved as a non-invasive imaging technique to visualize and quantify blood flow in the heart and vessels. Hemodynamic parameters derived from 4D flow MRI, such as net flow and peak velocities, but also kinetic energy, turbulent kinetic energy, viscous energy loss, and wall shear stress have shown to be of diagnostic relevance for cardiovascular diseases. 4D flow MRI, however, has several limitations. Its long acquisition times and its limited spatio-temporal resolutions lead to inaccuracies in velocity measurements in small and low-flow vessels and near the vessel wall. Additionally, 4D flow MRI requires long post-processing times, since inaccuracies due to the measurement process need to be corrected for and parameter quantification requires 2D and 3D contour drawing. Several machine learning (ML) techniques have been proposed to overcome these limitations. Existing scan acceleration methods have been extended using ML for image reconstruction and ML based super-resolution methods have been used to assimilate high-resolution computational fluid dynamic simulations and 4D flow MRI, which leads to more realistic velocity results. ML efforts have also focused on the automation of other post-processing steps, by learning phase corrections and anti-aliasing. To automate contour drawing and 3D segmentation, networks such as the U-Net have been widely applied. This review summarizes the latest ML advances in 4D flow MRI with a focus on technical aspects and applications. It is divided into the current status of fast and accurate 4D flow MRI data generation, ML based post-processing tools for phase correction and vessel delineation and the statistical evaluation of blood flow.

Turbulent Intensity of Blood Flow in the Healthy Aorta Increases With Dobutamine Stress and is Related to Cardiac Output.

Introduction: The blood flow in the normal cardiovascular system is predominately laminar but operates close to the threshold to turbulence. Morphological distortions such as vascular and valvular stenosis can cause transition into turbulent blood flow, which in turn may cause damage to tissues in the cardiovascular system. A growing number of studies have used magnetic resonance imaging (MRI) to estimate the extent and degree of turbulent flow in different cardiovascular diseases. However, the way in which heart rate and inotropy affect turbulent flow has not been investigated. In this study we hypothesized that dobutamine stress would result in higher turbulence intensity in the healthy thoracic aorta. Method: 4D flow MRI data were acquired in twelve healthy subjects at rest and with dobutamine, which was infused until the heart rate increased by 60% when compared to rest. A semi-automatic segmentation method was used to segment the thoracic aorta in the 4D flow MR images. Subsequently, flow velocity and several turbulent kinetic energy (TKE) parameters were calculated in the ascending aorta, aortic arch, descending aorta and whole thoracic aorta. Results: With dobutamine infusion there was an increase in heart rate (66 ± 9 vs. 108 ± 13 bpm, p < 0.001) and stroke volume (88 ± 13 vs. 102 ± 25 ml, p < 0.01). Additionally, there was an increase in Peak Average velocity (0.7 ± 0.1 vs. 1.2 ± 0.2 m/s, p < 0.001, Peak Max velocity (1.3 ± 0.1 vs. 2.0 ± 0.2 m/s, p < 0.001), Peak Total TKE (2.9 ± 0.7 vs. 8.0 ± 2.2 mJ, p < 0.001), Peak Median TKE (36 ± 7 vs. 93 ± 24 J/m3, p = 0.002) and Peak Max TKE (176 ± 33 vs. 334 ± 69 J/m3, p < 0.001). The relation between cardiac output and Peak Total TKE in the whole thoracic aorta was very strong (R2 = 0.90, p < 0.001). Conclusion: TKE of blood flow in the healthy thoracic aorta increases with dobutamine stress and is strongly related to cardiac output. Quantification of such turbulence intensity parameters with cardiac stress may serve as a risk assessment of aortic disease development.

Assessment of 4D flow MRI's quality by verifying its Navier-Stokes compatibility.

4D Flow Magnetic Resonance Imaging (MRI) is the state‐of‐the‐art technique to comprehensively measure the complex spatio‐temporal and multidirectional patterns of blood flow. However, it is subject to artifacts such as noise and aliasing, which due to the 3D and dynamic structure is difficult to detect in clinical practice. In this work, a new mathematical and computational model to determine the quality of 4D Flow MRI is presented. The model is derived by assuming the true velocity satisfies the incompressible Navier–Stokes equations and that can be decomposed by the measurements u→meas plus an extra field w→. Therefore, a non‐linear problem with w→ as unknown arises, which serves as a measure of data quality. A stabilized finite element formulation tailored to this problem is proposed and analyzed. Then, extensive numerical examples—using synthetic 4D Flow MRI data as well as real measurements on experimental phantom and subjects—illustrate the ability to use w→ for assessing the quality of 4D Flow MRI measurements over space and time.

Optimization of 4D flow MRI velocity field in the aorta with divergence-free smoothing.

Divergence-free smoothing with wall treatment (DFSwt) method is proposed for processing with four-dimensional (4D) flow magnetic resonance imaging (MRI) data of blood flows to enhance the quality of flow field with physical constraints. The new method satisfies the no-slip wall boundary condition and applies wall function of velocity profile for better estimating the velocity gradient in the near-wall region, and consequently improved wall shear stress (WSS) calculation against the issue of coarse resolution of 4D flow MRI. In the first testing case, blood flow field obtained from 4D flow MRI is well smoothed by DFSwt method. A great consistency is observed between the post-processed 4D flow MRI data and the computational fluid dynamics (CFD) data in the interested velocity field. WSS has an apparent improvement due to the proposed near-wall treatment with special wall function comparing to the result from original 4D flow MRI data or the DFS-processed data with no wall function. The other five cases also show the same performance that smoothed velocity field and improved WSS estimation are achieved on 4D flow MRI data optimized by DFSwt. The improvements will benefit the study of hemodynamics regarding the determination of location or the potential possibility of lesions.

Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data

Blood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to PC MRI. One method that excels in creating low-error, physics-based, velocity fields is computational fluid dynamics (CFD). Augmentation of cerebral 4D flow MRI data with CFD-informed neural networks may provide a method to produce highly accurate physiological flow fields. In this preliminary study, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a convolutional neural network, and then using the trained network to enhance MRI-derived velocity fields in cerebral blood vessel data sets. Through testing on simulated images, phantom data, and cerebrovascular 4D flow data from 20 patients, the trained network successfully de-noised flow images, decreased velocity error, and enhanced near-vessel-wall velocity quantification and visualization. Such image enhancement can improve experimental and clinical qualitative and quantitative cerebrovascular PC MRI analysis.

A test–retest multisite reproducibility study of cardiovascular four-dimensional flow MRI without respiratory gating

To systematically investigate the multisite reproducibility, test-retest reliability, and observer variability of non-respiratory-gated four-dimensional (4D) flow magnetic resonance imaging (MRI) in the thoracic great vessels for the assessment of blood flow and peak velocity. Electrocardiogram (ECG)-gated 4D flow MRI data were acquired without respiratory gating in 10 healthy volunteers. To analyse multisite reproducibility, 4D flow was scanned at three different sites using a 3 T GE MRI machine with identical protocols for the group of participants. In addition, to evaluate test-retest reliability, the same volunteers were scanned in each centre during a second visit. Data analysis included calculation of peak systolic velocity and time-resolved and total flow of both the ascending aorta and pulmonary artery. Two observers conducted the above measurements to assess the interobserver variability. Multisite, test-retest, interobserver agreement were good for the calculation of total flow and peak systolic velocity (mean differences <10% of the average flow parameter). Non-respiratory-gated 4D MRI-based assessment of aortic and pulmonary blood flow can be performed with good reproducibility. It may facilitate the potential clinical application of this technique.

4DFlowNet: Super-Resolution 4D Flow MRI Using Deep Learning and Computational Fluid Dynamics

4D-flow magnetic resonance imaging (MRI) is an emerging imaging technique where spatiotemporal 3D blood velocity can be captured with full volumetric coverage in a single non-invasive examination. This enables qualitative and quantitative analysis of hemodynamic flow parameters of the heart and great vessels. An increase in the image resolution would provide more accuracy and allow better assessment of the blood flow, especially for patients with abnormal flows. However, this must be balanced with increasing imaging time. The recent success of deep learning in generating super resolution images shows promise for implementation in medical images. We utilized computational fluid dynamics simulations to generate fluid flow simulations and represent them as synthetic 4D flow MRI data. We built our training dataset to mimic actual 4D flow MRI data with its corresponding noise distribution. Our novel 4DFlowNet network was trained on this synthetic 4D flow data and was capable in producing noise-free super resolution 4D flow phase images with upsample factor of 2. We also tested the 4DFlowNet in actual 4D flow MR images of a phantom and normal volunteer data, and demonstrated comparable results with the actual flow rate measurements giving an absolute relative error of 0.6 to 5.8% and 1.1 to 3.8% in the phantom data and normal volunteer data, respectively.

Deep variational network for rapid 4D flow MRI reconstruction

Phase-contrast magnetic resonance imaging (MRI) provides time-resolved quantification of blood flow dynamics that can aid clinical diagnosis. Long in vivo scan times due to repeated three-dimensional (3D) volume sampling over cardiac phases and breathing cycles necessitate accelerated imaging techniques that leverage data correlations. Standard compressed sensing reconstruction methods require tuning of hyperparameters and are computationally expensive, which diminishes the potential reduction of examination times. We propose an efficient model-based deep neural reconstruction network and evaluate its performance on clinical aortic flow data. The network is shown to reconstruct undersampled 4D flow MRI data in under a minute on standard consumer hardware. Remarkably, the relatively low amounts of tunable parameters allowed the network to be trained on images from 11 reference scans while generalizing well to retrospective and prospective undersampled data for various acceleration factors and anatomies.

Fluid-structure interaction simulations outperform computational fluid dynamics in the description of thoracic aorta haemodynamics and in the differentiation of progressive dilation in Marfan syndrome patients.

Abnormal fluid dynamics at the ascending aorta may be at the origin of aortic aneurysms. This study was aimed at comparing the performance of computational fluid dynamics (CFD) and fluid–structure interaction (FSI) simulations against four-dimensional (4D) flow magnetic resonance imaging (MRI) data; and to assess the capacity of advanced fluid dynamics markers to stratify aneurysm progression risk. Eight Marfan syndrome (MFS) patients, four with stable and four with dilating aneurysms of the proximal aorta, and four healthy controls were studied. FSI and CFD simulations were performed with MRI-derived geometry, inlet velocity field and Young's modulus. Flow displacement, jet angle and maximum velocity evaluated from FSI and CFD simulations were compared to 4D flow MRI data. A dimensionless parameter, the shear stress ratio (SSR), was evaluated from FSI and CFD simulations and assessed as potential correlate of aneurysm progression. FSI simulations successfully matched MRI data regarding descending to ascending aorta flow rates (R2 = 0.92) and pulse wave velocity (R2 = 0.99). Compared to CFD, FSI simulations showed significantly lower percentage errors in ascending and descending aorta in flow displacement (−46% ascending, −41% descending), jet angle (−28% ascending, −50% descending) and maximum velocity (−37% ascending, −34% descending) with respect to 4D flow MRI. FSI- but not CFD-derived SSR differentiated between stable and dilating MFS patients. Fluid dynamic simulations of the thoracic aorta require fluid–solid interaction to properly reproduce complex haemodynamics. FSI- but not CFD-derived SSR could help stratifying MFS patients.

High Resolution Simulation of Diastolic Left Ventricular Hemodynamics Guided by Four-Dimensional Flow Magnetic Resonance Imaging Data

We investigate the diastolic hemodynamics in a patient-specific left ventricle (LV) of a healthy subject using four dimensional flow magnetic resonance imaging (4D-Flow MRI) measurement and numerical simulation. From four dimensional Cardiac Magnetic Resonance (CMR) Imaging data, the kinematics of the endocardium is reconstructed. The endocardial kinematics and the time varying velocity distribution from 4D-Flow MRI at the mitral orifice are prescribed as boundary conditions for the numerical simulation. Both 4D-Flow MRI data and numerical results show the classical formation of the mitral vortex ring (MVR) during E-wave filling. The in-vivo data reveals that a large three-dimensional vortex structure forms near in the mid-level region of LV during diastasis (mid-level vortex). This mid-level vortex is formed simultaneously with the MVR and has not been reported in the literature. Quantitative comparison shows that the computed kinetic energy (KE) evolves in a similar manner to one derived from 4D-Flow MRI data during early E-wave filling. Both computational and measurement data show that the peak KE at E-wave is approximately 8 mJ. Our results suggest that numerical simulation can be used to provide useful hemodynamic data given the inputs from 4D-Flow MRI, which is now available in clinical practice. However, further investigation is needed to understand the formation mechanism of the mid-level vortex and its implication on the end-diastolic flow pattern.

Image-based background phase error correction in 4D flow MRI revisited.

To correct background phase errors in phase-contrast magnetic resonance imaging (MRI), image-based correction by referencing through stationary tissue is widely used. The aim of the present study was a detailed assessment of background phase errors in 4D Flow MRI and limitations of image-based correction. In a phantom study, 4D Flow MRI data were acquired for typical settings on two clinical 3T MR systems. Background errors were analyzed with respect to their spatial order and minimum requirements regarding the signal-to-noise ratio (SNR) and the amount of stationary tissue for image-based correction were assessed. For in vivo evaluation, data of the aorta were acquired on one 3T MR system in five healthy subjects including subsequent scans on the stationary phantom as reference. Background errors were found to exhibit spatial variation of first- to third-order. For correction, a minimum SNR of 20 was needed to achieve an error of less than 0.4% of the encoding velocity. The minimum amount of stationary tissue was strongly dependent on the spatial order requiring at least 25%, 60%, and 75% of stationary tissue for first-, second-, and third-order correction. In vivo evaluation showed that with 35-41% of stationary tissue available only first-order correction yielded a significant reduction (P < 0.01). Background phase errors can range from first to third spatial order in 4D Flow MRI requiring correction with appropriate polynomials. At the same time, the limited amount of stationary tissue available in vivo limits image-based background phase correction to first spatial order. 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1516-1525.

Acute Cerebral Venous Thrombosis: Three-Dimensional Visualization and Quantification of Hemodynamic Alterations Using 4-Dimensional Flow Magnetic Resonance Imaging.

Cerebral venous thrombosis (CVT) affects venous hemodynamics and can provoke severe stroke and chronic intracranial hypertension. We sought to comprehensively analyze 3-dimensional blood flow and hemodynamic alterations during acute CVT including collateral recruitment and at follow-up. Twenty-two consecutive patients with acute CVT were prospectively included and underwent routine brain magnetic resonance imaging (MRI) and 4-dimensional flow MRI at 3 T for the in vivo assessment of cerebral blood flow. Neurological and MRI follow-up at 6 months was performed in 18 patients. Three-dimensional blood flow visualization and quantification of large dural venous sinuses and deep cerebral veins was successfully performed in all patients. During acute CVT, we observed abnormal flow patterns including stagnant flow, flow acceleration in stenoses, and change of flow directions. In patients with complete recanalization, flow trajectories resembled those known from previously published 4-dimensional flow MRI data in healthy adults. There was a trend toward a relationship between occluded segments and cerebral lesions (not significant). Furthermore, patients with versus without cerebral lesions showed increased mean (0.08±0.09 versus 0.005±0.014 m/s) and peak velocities (0.18±0.21 versus 0.006±0.02 m/s) within partially thrombosed left and right transverse sinuses (P<0.05) at baseline. Four-dimensional flow MRI was successfully applied for the 3-dimensional visualization and quantification of venous hemodynamics in patients with CVT and provided new dynamic information regarding vessel recanalization. This technique seems promising to investigate the contribution of hemodynamic parameters and collaterals in a larger cohort to identify those at risk of stroke.

Altered Diastolic Flow Patterns and Kinetic Energy in Subtle Left Ventricular Remodeling and Dysfunction Detected by 4D Flow MRI.

Aims4D flow magnetic resonance imaging (MRI) allows quantitative assessment of left ventricular (LV) function according to characteristics of the dynamic flow in the chamber. Marked abnormalities in flow components’ volume and kinetic energy (KE) have previously been demonstrated in moderately dilated and depressed LV’s compared to healthy subjects. We hypothesized that these 4D flow-based measures would detect even subtle LV dysfunction and remodeling.Methods and ResultsWe acquired 4D flow and morphological MRI data from 26 patients with chronic ischemic heart disease with New York Heart Association (NYHA) class I and II and with no to mild LV systolic dysfunction and remodeling, and from 10 healthy controls. A previously validated method was used to separate the LV end-diastolic volume (LVEDV) into functional components: direct flow, which passes directly to ejection, and non-ejecting flow, which remains in the LV for at least 1 cycle. The direct flow and non-ejecting flow proportions of end-diastolic volume and KE were assessed. The proportions of direct flow volume and KE fell with increasing LVEDV-index (LVEDVI) and LVESV-index (LVESVI) (direct flow volume r = -0.64 and r = -0.74, both P<0.001; direct flow KE r = -0.48, P = 0.013, and r = -0.56, P = 0.003). The proportions of non-ejecting flow volume and KE rose with increasing LVEDVI and LVESVI (non-ejecting flow volume: r = 0.67 and r = 0.76, both P<0.001; non-ejecting flow KE: r = 0.53, P = 0.005 and r = 0.52, P = 0.006). The proportion of direct flow volume correlated moderately to LVEF (r = 0.68, P < 0.001) and was higher in a sub-group of patients with LVEDVI >74 ml/m2 compared to patients with LVEDVI <74 ml/m2 and controls (both P<0.05).ConclusionDirect flow volume and KE proportions diminish with increased LV volumes, while non-ejecting flow proportions increase. A decrease in direct flow volume and KE at end-diastole proposes that alterations in these novel 4D flow-specific markers may detect LV dysfunction even in subtle or subclinical LV remodeling.

Towards high‐resolution 4D flow MRI in the human aorta using kt‐GRAPPA and B1+ shimming at 7T

To evaluate the feasibility of aortic 4D flow magnetic resonance imaging (MRI) at 7T with improved spatial resolution using kt-GRAPPA acceleration while restricting acquisition time and to address radiofrequency (RF) excitation heterogeneities with B1+ shimming. 4D flow MRI data were obtained in the aorta of eight subjects using a 16-channel transmit/receive coil array at 7T. Flow quantification and acquisition time were compared for a kt-GRAPPA accelerated (R = 5) and a standard GRAPPA (R = 2) accelerated protocol. The impact of different dynamic B1+ shimming strategies on flow quantification was investigated. Two kt-GRAPPA accelerated protocols with 1.2 × 1.2 × 1.2 mm(3) and 1.8 × 1.8 × 2.4 mm(3) spatial resolution were compared. Using kt-GRAPPA, we achieved a 4.3-fold reduction in net acquisition time resulting in scan times of about 10 minutes. No significant effect on flow quantification was observed compared to standard GRAPPA with R = 2. Optimizing the B1+ fields for the aorta impacted significantly (P < 0.05) the flow quantification while specific B1+ settings were required for respiration navigators. The high-resolution protocol yielded similar flow quantification, but allowed the depiction of branching vessels. 7T in combination with B1+ shimming allows for high-resolution 4D flow MRI acquisitions in the human aorta, while kt-GRAPPA limits total scan times without affecting flow quantification. J. Magn. Reson. Imaging 2016;44:486-499.

4D flow MRI can detect subtle right ventricular dysfunction in primary left ventricular disease.

To investigate whether 4D flow magnetic resonance imaging (MRI) can detect subtle right ventricular (RV) dysfunction in primary left ventricular (LV) disease. 4D flow and morphological 3T MRI data were acquired in 22 patients with mild ischemic heart disease who were stratified into two groups based on LV end-diastolic volume index (EDVI): lower-LVEDVI and higher-LVEDVI, as well as in 11 healthy controls. The RV volume was segmented at end-diastole (ED) and end-systole (ES). Pathlines were emitted from the ED volume and traced forwards and backwards in time to ES. The blood volume was separated into flow components. The Direct Flow (DF) component was defined as RV inflow passing directly to outflow. The kinetic energy (KE) of the DF component was calculated. Echocardiographic conventional RV indices were also assessed. The higher-LVEDVI group had larger LVEDVI and lower LV ejection fraction (98 ± 32 ml/m(2) ; 48 ± 13%) compared to the healthy (67 ± 12, P = 0.002; 64 ± 7, P < 0.001) and lower-LVEDI groups (62 ± 10; 68 ± 7, both P < 0.001). The RV 4D flow-specific measures "DF/EDV volume-ratio" and "DF/EDV KE-ratio at ED" were lower in the higher-LVEDVI group (38 ± 5%; 52 ± 6%) compared to the healthy (44 ± 6; 65 ± 7, P = 0.018 and P < 0.001) and lower-LVEDVI groups (44 ± 6; 64 ± 7, P = 0.011 and P < 0.001). There was no difference in any of the conventional MRI and echocardiographic RV indices between the three groups. We found that in primary LV disease mild impairment of RV function can be detected by 4D flow-specific measures, but not by the conventional MRI and echocardiographic indices.