4D Flow Magnetic Resonance Imaging Data Research Articles

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.

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.

The impact of 4D-Flow MRI spatial resolution on patient-specific CFD simulations of the thoracic aorta

Magnetic Resonance Imaging (MRI) is considered the gold standard of medical imaging technologies as it allows for accurate imaging of blood vessels. 4-Dimensional Flow Magnetic Resonance Imaging (4D-Flow MRI) is built on conventional MRI, and provides flow data in the three vector directions and a time resolved magnitude data set. As such it can be used to retrospectively calculate haemodynamic parameters of interest, such as Wall Shear Stress (WSS). However, multiple studies have indicated that a significant limitation of the imaging technique is the spatiotemporal resolution that is currently available. Recent advances have proposed and successfully integrated 4D-Flow MRI imaging techniques with Computational Fluid Dynamics (CFD) to produce patient-specific simulations that have the potential to aid in treatments,surgical decision making, and risk stratification. However, the consequences of using insufficient 4D-Flow MRI spatial resolutions on any patient-specific CFD simulations is currently unclear, despite being a recognised limitation. The research presented in this study aims to quantify the inaccuracies in patient-specific 4D-Flow MRI based CFD simulations that can be attributed to insufficient spatial resolutions when acquiring 4D-Flow MRI data. For this research, a patient has undergone four 4D-Flow MRI scans acquired at various isotropic spatial resolutions and patient-specific CFD simulations have subsequently been run using geometry and velocity data produced from each scan. It was found that compared to CFD simulations based on a 1.5,{text {mm}} times 1.5,{text {mm}} times 1.5,{text {mm}}, using a spatial resolution of 4,{text {mm}} times 4,{text {mm}} times 4,{text {mm}} substantially underestimated the maximum velocity magnitude at peak systole by 110.55%. The impacts of 4D-Flow MRI spatial resolution on WSS calculated from CFD simulations have been investigated and it has been shown that WSS is underestimated in CFD simulations that are based on a coarse 4D-Flow MRI spatial resolution. The authors have concluded that a minimum 4D-Flow MRI spatial resolution of 1.5,{text {mm}} times 1.5,{text {mm}} times 1.5,{text {mm}} must be used when acquiring 4D-Flow MRI data to perform patient-specific CFD simulations. A coarser spatial resolution will produce substantial differences within the flow field and geometry.

SRflow: Deep learning based super-resolution of 4D-flow MRI data.

Exploiting 4D-flow magnetic resonance imaging (MRI) data to quantify hemodynamics requires an adequate spatio-temporal vector field resolution at a low noise level. To address this challenge, we provide a learned solution to super-resolve in vivo 4D-flow MRI data at a post-processing level. We propose a deep convolutional neural network (CNN) that learns the inter-scale relationship of the velocity vector map and leverages an efficient residual learning scheme to make it computationally feasible. A novel, direction-sensitive, and robust loss function is crucial to learning vector-field data. We present a detailed comparative study between the proposed super-resolution and the conventional cubic B-spline based vector-field super-resolution. Our method improves the peak-velocity to noise ratio of the flow field by 10 and 30% for in vivo cardiovascular and cerebrovascular data, respectively, for 4 × super-resolution over the state-of-the-art cubic B-spline. Significantly, our method offers 10x faster inference over the cubic B-spline. The proposed approach for super-resolution of 4D-flow data would potentially improve the subsequent calculation of hemodynamic quantities.

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.

On the impact of vessel wall stiffness on quantitative flow dynamics in a synthetic model of the thoracic aorta

Aortic wall stiffening is a predictive marker for morbidity in hypertensive patients. Arterial pulse wave velocity (PWV) correlates with the level of stiffness and can be derived using non-invasive 4D-flow magnetic resonance imaging (MRI). The objectives of this study were twofold: to develop subject-specific thoracic aorta models embedded into an MRI-compatible flow circuit operating under controlled physiological conditions; and to evaluate how a range of aortic wall stiffness impacts 4D-flow-based quantification of hemodynamics, particularly PWV. Three aorta models were 3D-printed using a novel photopolymer material at two compliant and one nearly rigid stiffnesses and characterized via tensile testing. Luminal pressure and 4D-flow MRI data were acquired for each model and cross-sectional net flow, peak velocities, and PWV were measured. In addition, the confounding effect of temporal resolution on all metrics was evaluated. Stiffer models resulted in increased systolic pressures (112, 116, and 133 mmHg), variations in velocity patterns, and increased peak velocities, peak flow rate, and PWV (5.8–7.3 m/s). Lower temporal resolution (20 ms down to 62.5 ms per image frame) impacted estimates of peak velocity and PWV (7.31 down to 4.77 m/s). Using compliant aorta models is essential to produce realistic flow dynamics and conditions that recapitulated in vivo hemodynamics.

Impact of aortic arch curvature in flow haemodynamics in patients with transposition of the great arteries after arterial switch operation.

In this study, we will describe a comprehensive haemodynamic analysis and its relationship to the dilation of the aorta in transposition of the great artery (TGA) patients post-arterial switch operation (ASO) and controls using 4D-flow magnetic resonance imaging (MRI) data. Using 4D-flow MRI data of 14 TGA young patients and 8 age-matched normal controls obtained with 1.5 T GE-MR scanner, we evaluate 3D maps of 15 different haemodynamics parameters in six regions; three of them in the aortic root and three of them in the ascending aorta (anterior-left, -right, and posterior for both cases) to find its relationship with the aortic arch curvature and root dilation. Differences between controls and patients were evaluated using Mann-Whitney U test, and the relationship with the curvature was accessed by unpaired t-test. For statistical significance, we consider a P-value of 0.05. The aortic arch curvature was significantly different between patients 46.238 ± 5.581m-1 and controls 41.066 ± 5.323m-1. Haemodynamic parameters as wall shear stress circumferential (WSS-C), and eccentricity (ECC), were significantly different between TGA patients and controls in both the root and ascending aorta regions. The distribution of forces along the ascending aorta is highly inhomogeneous in TGA patients. We found that the backward velocity (B-VEL), WSS-C, velocity angle (VEL-A), regurgitation fraction (RF), and ECC are highly correlated with the aortic arch curvature and root dilatation. We have identified six potential biomarkers (B-VEL, WSS-C, VEL-A, RF, and ECC), which may be helpful for follow-up evaluation and early prediction of aortic root dilatation in this patient population.

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.

Towards multi-modal data fusion for super-resolution and denoising of 4D-Flow MRI.

4D-Flow magnetic resonance imaging (MRI) has enabled in vivo time-resolved measurement of three-dimensional blood flow velocities in the human vascular system. However, its clinical use has been hampered by two main issues, namely, low spatio-temporal resolution and acquisition noise. While patient-specific computational fluid dynamics (CFD) simulations can address the resolution and noise issues, its fidelity is impacted by accuracy of estimation of boundary conditions, model parameters, vascular geometry, and flow model assumptions. In this paper a scheme to address limitations of both modalities through data-fusion is presented. The solutions of the patient-specific CFD simulation are characterized using proper orthogonal decomposition (POD). Next, a process of projecting the 4D-Flow MRI data onto the POD basis and projection coefficient mapping using generalized dynamic mode decomposition (DMD) enables simultaneous super-resolution and denoising of 4D-Flow MRI. The method has been tested using numerical phantoms derived from patient-specific aneurysmal geometries and applied to in vivo 4D-Flow MRI data.

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.

A Valveless Pulsatile Pump for Heart Failure with Preserved Ejection Fraction: Hemo- and Fluid Dynamic Feasibility

Treatment of heart failure with preserved ejection fraction (HFpEF) remains a major unmet medical need. An implantable valveless pulsatile pump with a single cannula—the CoPulse pump—may provide beneficial hemodynamic support for select HFpEF patients when connected to the failing ventricle. We aimed to demonstrate hemodynamic efficacy and hemocompatible design feasibility for this novel assist device. The hemodynamic effect of the pump was investigated with an in vitro circulatory mock loop and an ex vivo isolated porcine heart model. The hydraulic design was optimized using computational fluid dynamics (CFD), and validated by 4D-flow magnetic resonance imaging (MRI). The pump reduced left atrial pressure (> 27%) and increased cardiac output (> 14%) in vitro. Ex vivo experiments revealed elevated total stroke volume at increased end-systolic volume during pump support. Asymmetric cannula positioning indicated superior washout, decreased stagnation (8.06 mm2 vs. 31.42 mm2), and marginal blood trauma potential with moderate shear stresses (< 24 Pa) in silico. Good agreement in flow velocities was evident among CFD and 4D-flow MRI data (r > 0.76). The CoPulse pump proved hemodynamically effective. Hemocompatibility metrics were comparable to those of a previously reported, typical pulsatile pump with two cannulae. The encouraging in vitro, ex vivo, and hemocompatibility results substantiate further development of the CoPulse pump.

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.

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.

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.