Clinical Applications of Aortic 4D Flow Imaging

Flow Dynamics in the Aortic Arch and Its Effect on the Arterial Input Function in Cardiac Computed Tomography

The arterial input function (AIF)-time-density curve (TDC) of contrast at the coronary ostia-plays a central role in contrast enhanced computed tomography angiography (CTA). This study employs computational modeling in a patient-specific aorta to investigate mixing and dispersion of contrast in the aortic arch (AA) and to compare the TDCs in the coronary ostium and the descending aorta. Here, we examine the validity of the use of TDC in the descending aorta as a surrogate for the AIF. Computational fluid dynamics was used to study hemodynamics and contrast dispersion in a CTA-based patient model of the aorta. Variations in TDC between the aortic root, through the AA and at the descending aorta and the effect of flow patterns on contrast dispersion was studied via post-processing of the results. Simulations showed complex unsteady patterns of contrast mixing and dispersion in the AA that are driven by the pulsatile flow. However, despite the relatively long intra-aortic distance between the coronary ostia and the descending aorta, the TDCs at these two locations were similar in terms of rise-time and up-slope, and the time lag between the two TDCs was 0.19 seconds. TDC in the descending aorta is an accurate analog of the AIF. Methods that use quantitative metrics such as rise-time and slope of the AIF to estimate coronary flowrate and myocardial ischemia can continue with the current practice of using the TDC at the descending aorta as a surrogate for the AIF.

Assessment of turbulent flow effects on the vessel wall using four-dimensional flow MRI

PURPOSE

To explore the use of MR-estimated turbulence quantities for the assessment of turbulent flow effects on the vessel wall.

METHODS

Numerical velocity data for two patient-derived models was obtained using computational fluid dynamics (CFD) for two physiological flow rates. The four-dimensional (4D) Flow MRI measurements were simulated at three different spatial resolutions and used to investigate the estimation of turbulent wall shear stress (tWSS) using the intravoxel standard deviation (IVSD) of velocity and turbulent kinetic energy (TKE) estimated near the vessel wall.

RESULTS

Accurate estimation of tWSS using the IVSD is limited by the spatial resolution achievable with 4D Flow MRI. TKE, estimated near the wall, has a strong linear relationship to the tWSS (mean R²  = 0.84). Near-wall TKE estimates from MR simulations have good agreement to CFD-derived ground truth (mean R²  = 0.90). Maps of near-wall TKE have strong visual correspondence to tWSS.

CONCLUSION

Near-wall estimation of TKE permits assessment of relative maps of tWSS, but direct estimation of tWSS is challenging due to limitations in spatial resolution. Assessment of tWSS and near-wall TKE may open new avenues for analysis of different pathologies. Magn Reson Med 77:2310-2319, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Use of multi-velocity encoding 4D flow MRI to improve quantification of flow patterns in the aorta

PURPOSE

To show that the use of a multi-velocity encoding (VENC) 4D-flow approach offers significant improvements in the characterization of complex flow in the aorta. Four-dimensional flow magnetic resonance imaging (MRI) (4D-flow) can be used to measure complex flow patterns and dynamics in the heart and major vessels. The quality of the information derived from these measures is dependent on the accuracy of the vector field, which is limited by the vector-to-noise ratio.

MATERIALS AND METHODS

A 4D-flow protocol involving three different VENC values of 150, 60, and 20 cm/s was performed on six control subjects and nine patients with type-B chronic aortic dissection at 3T MRI. Data were processed using a single VENC value (150 cm/s) or using a fused dataset that selected the lowest appropriate VENC for each voxel. Performance was analyzed by measuring spatial vector angular correlation, magnitude correlation, temporal vector conservation, and "real-world" streamline tracing performance.

RESULTS

The multi-VENC approach provided a 31% improvement in spatial and 53% improvement in temporal precision of velocity vector measurements during the mid-late diastolic period, where 99% of the flow vectors in the normal aorta are below 20 cm/s. In low-flow conditions this resulted in practical improvements of greater than 50% in pathline tracking and streamline tracing quantified by streamline curvature measurements.

CONCLUSION

A multi-VENC 4D-flow approach provides accurate vector data across normal physiological velocities observed in the aorta, dramatically improving outputs such as pathline tracking, streamline estimation, and further advanced analyses.

Lagrangian postprocessing of computational hemodynamics

Recent advances in imaging, modeling, and computing have rapidly expanded our capabilities to model hemodynamics in the large vessels (heart, arteries, and veins). This data encodes a wealth of information that is often under-utilized. Modeling (and measuring) blood flow in the large vessels typically amounts to solving for the time-varying velocity field in a region of interest. Flow in the heart and larger arteries is often complex, and velocity field data provides a starting point for investigating the hemodynamics. This data can be used to perform Lagrangian particle tracking, and other Lagrangian-based postprocessing. As described herein, Lagrangian methods are necessary to understand inherently transient hemodynamic conditions from the fluid mechanics perspective, and to properly understand the biomechanical factors that lead to acute and gradual changes of vascular function and health. The goal of the present paper is to review Lagrangian methods that have been used in post-processing velocity data of cardiovascular flows.