Virtual injections using 4D flow MRI with displacement corrections and constrained probabilistic streamlines

4D flow MRI velocity uncertainty quantification

PURPOSE

An automatic method is presented for estimating 4D flow MRI velocity measurement uncertainty in each voxel. The velocity distance (VD) metric, a statistical distance between the measured velocity and local error distribution, is introduced as a novel measure of 4D flow MRI velocity measurement quality.

METHODS

The method uses mass conservation to assess the local velocity error variance and the standardized difference of means (SDM) velocity to estimate the velocity error correlations. VD is evaluated as the Mahalanobis distance between the local velocity measurement and the local error distribution. The uncertainty model is validated synthetically and tested in vitro under different flow resolutions and noise levels. The VD's application is demonstrated on two in vivo thoracic vasculature 4D flow datasets.

RESULTS

Synthetic results show the proposed uncertainty quantification method is sensitive to aliased regions across various velocity-to-noise ratios and assesses velocity error correlations in four- and six-point acquisitions with correlation errors at or under 3.2%. In vitro results demonstrate the method's sensitivity to spatial resolution, venc settings, partial volume effects, and phase wrapping error sources. Applying VD to assess in vivo 4D flow MRI in the aorta demonstrates the expected increase in measured velocity quality with contrast administration and systolic flow.

CONCLUSION

The proposed 4D flow MRI uncertainty quantification method assesses velocity measurement error owing to sources including noise, intravoxel phase dispersion, and velocity aliasing. This method enables rigorous comparison of 4D flow MRI datasets obtained in longitudinal studies, across patient populations, and with different MRI systems.

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

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Automated hemodynamic assessment for cranial 4D flow MRI

Cranial 4D flow MRI post-processing typically involves manual user interaction which is time-consuming and associated with poor repeatability. The primary goal of this study is to develop a robust quantitative velocity tool (QVT) that utilizes threshold-based segmentation techniques to improve segmentation quality over prior approaches based on centerline processing schemes (CPS) that utilize k-means clustering segmentation. This tool also includes an interactive 3D display designed for simplified vessel selection and automated hemodynamic visualization and quantification. The performances of QVT and CPS were compared in vitro in a flow phantom and in vivo in 10 healthy participants. Vessel segmentations were compared with ground-truth computed tomography in vitro (29 locations) and manual segmentation in vivo (13 locations) using linear regression. Additionally, QVT and CPS MRI flow rates were compared to perivascular ultrasound flow in vitro using linear regression. To assess internal consistency of flow measures in vivo, conservation of flow was assessed at vessel junctions using linear regression and consistency of flow along vessel segments was analyzed by fitting a Gaussian distribution to a histogram of normalized flow values. Post-processing times were compared between the QVT and CPS using paired t-tests. Vessel areas segmented in vitro (CPS: slope = 0.71, r = 0.95 and QVT: slope = 1.03, r = 0.95) and in vivo (CPS: slope = 0.61, r = 0.96 and QVT: slope = 0.93, r = 0.96) were strongly correlated with ground-truth area measurements. However, CPS (using k-means segmentation) consistently underestimated vessel areas. Strong correlations were observed between QVT and ultrasound flow (slope = 0.98, r = 0.96) as well as flow at junctions (slope = 1.05, r = 0.98). Mean and standard deviation of flow along vessel segments was 9.33e-16 ± 3.05%. Additionally, the QVT demonstrated excellent interobserver agreement and significantly reduced post-processing by nearly 10 min (p < 0.001). By completely automating post-processing and providing an easy-to-use 3D visualization interface for interactive vessel selection and hemodynamic quantification, the QVT offers an efficient, robust, and repeatable means to analyze cranial 4D flow MRI. This software is freely available at: https://github.com/uwmri/QVT.