Verena Knobloch

Bayesian multipoint velocity encoding for concurrent flow and turbulence mapping.

An approach to efficiently measure three‐dimensional velocity vector fields and turbulent kinetic energy of blood flow is presented. Multipoint phase‐contrast imaging is used in combination with Bayesian analysis to map both mean and fluctuating velocities over a large dynamic range and for practically relevant signal‐to‐noise ratios. It is demonstrated that the approach permits significant spatiotemporal undersampling to allow for clinically acceptable scan times. Using numerical simulations and in vitro measurements in aortic valve phantoms, it is shown that for given scan time, Bayesian multipoint velocity encoding provides consistently lower errors of velocity and turbulent kinetic energy over a larger dynamic range relative to previous methods. In vitro, significant differences in both peak velocity and turbulent kinetic energy between the aortic CoreValve (150 cm/s, 293 J/m3) and the St. Jude Medical mechanical valve (120 cm/s, 149 J/m3) were found. Comparison of peak turbulent kinetic energy measured in a patient with aortic stenosis (950 J/m3) and in a patient with an implanted aortic CoreValve (540 J/m3) revealed considerable differences relative to the values detected in healthy subjects (149 ± 12 J/m3) indicating the potential of the method to provide a comprehensive hemodynamic assessment of valve performance in vivo. Magn Reson Med, 2013.

Flow induced by ependymal cilia dominates near-wall cerebrospinal fluid dynamics in the lateral ventricles.

While there is growing experimental evidence that cerebrospinal fluid (CSF) flow induced by the beating of ependymal cilia is an important factor for neuronal guidance, the respective contribution of vascular pulsation-driven macroscale oscillatory CSF flow remains unclear. This work uses computational fluid dynamics to elucidate the interplay between macroscale and cilia-induced CSF flows and their relative impact on near-wall dynamics. Physiological macroscale CSF dynamics are simulated in the ventricular space using subject-specific anatomy, wall motion and choroid plexus pulsations derived from magnetic resonance imaging. Near-wall flow is quantified in two subdomains selected from the right lateral ventricle, for which dynamic boundary conditions are extracted from the macroscale simulations. When cilia are neglected, CSF pulsation leads to periodic flow reversals along the ventricular surface, resulting in close to zero time-averaged force on the ventricle wall. The cilia promote more aligned wall shear stresses that are on average two orders of magnitude larger compared with those produced by macroscopic pulsatile flow. These findings indicate that CSF flow-mediated neuronal guidance is likely to be dominated by the action of the ependymal cilia in the lateral ventricles, whereas CSF dynamics in the centre regions of the ventricles is driven predominantly by wall motion and choroid plexus pulsation.

Sparsity transform k‐t principal component analysis for accelerating cine three‐dimensional flow measurements

Time‐resolved three‐dimensional flow measurements are limited by long acquisition times. Among the various acceleration techniques available, k‐t methods have shown potential as they permit significant scan time reduction even with a single receive coil by exploiting spatiotemporal correlations. In this work, an extension of k‐t principal component analysis is proposed utilizing signal differences between the velocity encodings of three‐directional flow measurements to further compact the signal representation and hence improve reconstruction accuracy. The effect of sparsity transform in k‐t principal component analysis is demonstrated using simulated and measured data of the carotid bifurcation. Deploying sparsity transform for 8‐fold undersampled simulated data, velocity root‐mean‐square errors were found to decrease by 52 ± 14%, 59 ± 11%, and 16 ± 32% in the common, external, and internal carotid artery, respectively. In vivo, errors were reduced by 15 ± 17% in the common carotid artery with sparsity transform. Based on these findings, spatial resolution of three‐dimensional flow measurements was increased to 0.8 mm isotropic resolution with prospective 8‐fold undersampling and sparsity transform k‐t principal component analysis reconstruction. Volumetric data were acquired in 6 min. Pathline visualization revealed details of helical flow patterns partially hidden at lower spatial resolution. Magn Reson Med, 2013.

Mapping mean and fluctuating velocities by Bayesian multipoint MR velocity encoding-validation against 3D particle tracking velocimetry

To validate Bayesian multipoint MR velocity encoding against particle tracking velocimetry for measuring velocity vector fields and fluctuating velocities in a realistic aortic model.

Arterial, Venous, and Cerebrospinal Fluid Flow: Simultaneous Assessment with Bayesian Multipoint Velocity-encoded MR Imaging

PURPOSE To measure arterial, venous, and cerebrospinal fluid (CSF) velocities simultaneously by using Bayesian multipoint velocity-encoded magnetic resonance (MR) imaging and to compare interacquisition reproducibility relative to that of standard phase-contrast MR imaging for sequential measurements of arterial, venous, and CSF velocities. MATERIALS AND METHODS This study was approved by the local ethics committee, and informed consent was obtained from all subjects. Simultaneous measurement of blood and CSF flow was performed at the C1-C2 level in 10 healthy subjects (mean age, 24.4 years ± 2.7; five men, five women) by using accelerated Bayesian multipoint velocity-encoded MR imaging. Data were compared with those obtained from two separate conventional phase-contrast MR imaging acquisitions, one optimized for arterial and venous blood flow (velocity encoding range, ±50 cm/sec) and the other optimized for CSF flow (velocity encoding range, ±10 cm/sec), with an imaging time of approximately 2 minutes each. Data acquisition was repeated six times. Intraclass correlation coefficient (ICC) and linear regression were used to quantify interacquisition reproducibility. RESULTS There was no significant difference in arterial blood flow measured with Bayesian multipoint velocity-encoded MR imaging and that measured with phase-contrast MR imaging (mean ICC, 0.96 ± 0.03 vs 0.97 ± 0.02, respectively). Likewise, there was no significant difference between CSF flow measured with Bayesian multipoint velocity-encoded MR imaging and that measured with phase-contrast MR imaging (mean ICC, 0.97 ± 0.02 vs 0.96 ± 0.05, respectively). For venous blood flow, the ICC with Bayesian multipoint MR imaging was significantly larger than that with conventional phase-contrast MR imaging (mean, 0.75 ± 0.23 vs 0.65 ± 0.26, respectively; P = .016). CONCLUSION Bayesian multipoint velocity-encoded MR imaging allows for simultaneous assessment of fast and slow flows in arterial, venous, and CSF lumina in a single acquisition. It eliminates the need for vessel-dependent adjustment of the velocity-encoding range, as required for conventional sequential phase-contrast MR imaging measurements.

Assessment of energy loss in aortic stenosis using Bayesian multipoint phase-contrast MRI

(TKE) and energy loss indices (ELI) are listed in Table 1. Compared to our previous data obtained in healthy subjects [3], peak TKE in the patients presented here was found to be significantly increased (149±12 J/m 3 vs. 1350 and 1630 J/m 3 , respectively). It is noteworthy that the patient with higher TKE values had a lower energy loss index. Values from Doppler echocardiography are given for comparison. Figure 1 shows maps of TKE in the aortic arch in both patients along with flow patterns derived from the velocity data. Conclusions Bayesian multipoint PC-MRI permits concurrent mapping of both mean kinetic and turbulent kinetic energy in patients and allows the assessment of relative energy loss and pressure gradients associated with aortic valve stenosis. The energy loss index was found to be approximately 8-fold higher as compared to healthy subjects [2] and may hold promise to serve as a novel marker for grading valve disease.

Assessment of 3D velocity vector fields and turbulent kinetic energy in a realistic aortic phantom using multi-point variable-density velocity encoding

A multi-point velocity encoding approach for the assessment of velocity vector fields and TKE is shown in this work. The method is applied in an aortic arch phantom under different flow conditions.

Spatio-temporally constrained reconstruction for highly accelerated flow MRI

Introduction Due to the inherent long scan times, single and multidirectional cine phase-contrast MRI [1] has limited practical value in a clinical setting [2]. Using parallel imaging combined with constrained reconstruction procedures [3], acceleration factors on the order of 4-6 have been shown practicable for flow quantification [4]. Recently, kt PCA(5) was proposed to further constrain reconstruction. By using principle component analysis (PCA) on the training data, the number of unknowns to be solved for can be reduced thereby permitting acceleration factors excelling those of previous methods. In this work, the k-t PCA method is extended by introducing spatial compartment-specific basis sets to improve reconstruction accuracy at very high undersampling factors (method termed k-t PCA+ hereafter).

Assessment of energy loss across aortic valves using accelerated CMR multi-point flow measurements

Summary A novel approach for evaluating the performance of artificial or diseased heart valves is presented and applied on in-vitro as well as in-vivo aortic valve data. The method, which is based on turbulence and flow measurements, provides a measure to assess and compare energy dissipation under varying flow conditions. Background Diseased or artificial heart valves possibly lead to turbulent flow and regurgitation, both increasing the workload of the heart. Current measures for valve assessment, i.e. effective orifice area, only indirectly and partially correlate with the energy loss due to the valve [1]. Phase-Contrast MRI makes it possible to directly quantify these energy losses, and by relating them to kinetic energy of the flow ap arameter describing the hemodynamic performance of the valve can be obtained. Methods