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.

Ferumoxytol dynamic contrast enhanced magnetic resonance imaging identifies altered placental cotyledon perfusion in rhesus macaques†.

Identification of placental dysfunction in early pregnancy with noninvasive imaging could be a valuable tool for assessing maternal and fetal risk. Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) can be a powerful tool for interrogating placenta health. After inoculation with Zika virus or sham inoculation at gestation age (GA) 45 or 55 days, animals were imaged up to three times at GA65, GA100, and GA145. DCE MRI images were acquired at all imaging sessions using ferumoxytol, an iron nanoparticle-based contrast agent, and analyzed for placental intervillous blood flow, number of perfusion domains, and perfusion domain volume. Cesarean section was performed at GA155, and the placenta was photographed and dissected for histopathology. Photographs were used to align cotyledons with estimated perfusion domains from MRI, allowing comparison of estimated cotyledon volume to pathology. Monkeys were separated into high and low pathology groups based on the average number of pathologies present in the placenta. Perfusion domain flow, volume, and number increased through gestation, and total blood flow increased with gestation for both low pathology and high pathology groups. A statistically significant decrease in perfusion domain volume associated with pathology was detected at all gestational ages. Individual perfusion domain flow comparisons demonstrated a statistically significant decrease with pathology at GA100 and GA145, but not GA65. Since ferumoxytol is currently used to treat anemia during human pregnancy and as an off-label MRI contrast agent, future transition of this work to human pregnancy may be possible.

Automatic measurement plane placement for 4D Flow MRI of the great vessels using deep learning.

PURPOSE: Despite the great potential and flexibility of 4D flow MRI for hemodynamic analysis, a major limitation is the need for time-consuming and user-dependent post-processing. We propose a fast four-step algorithm for rapid, robust, and repeatable flow measurements in the great vessels based on automatic placement of measurement planes and vessel segmentation. METHODS: Our algorithm works by (1) subsampling the 3D image into 3D patches, (2) predicting the probability of each patch containing individual vessels and location/orientation of the vessel within the patch via a convolutional neural network, (3) selecting the predicted planes with highest probabilities for each vessel, and (4) shifting the plane centers to the maximum velocity within each plane. The method was trained on 283 scans and evaluated on 40 unseen scans by comparing algorithm-derived processing times, plane locations, and flow measurements to those of two manual observers (graduate students) using t-tests, Pearson correlation, and Bland-Altman analysis. RESULTS: The average processing time for the algorithm (18 s) was shorter than observer 1 (362 s; P < 0.001) and observer 2 (317 s; P < 0.001). The distance between planes placed by the algorithm and those placed by manual observers was slightly greater (O1 vs. algorithm: 9.0 mm, O2 vs. algorithm: 10.3 mm) than the distance between planes placed by the two manual observers (8.3 mm). The correlation between flow values for planes placed by the algorithm and those placed by manual observers was slightly lower (O1 vs. algorithm: R = 0.68, O2 vs. algorithm: R = 0.72) than the flow correlation between the two manual observers (R = 0.81). CONCLUSION: Our method is a feasible and accurate approach for fast, reproducible, and automated flow measurement and visualization in 4D flow MRI of the great vessels, with similar variability compared to a manual annotator as the variability between two manual observers. This approach could be applied in other anatomical regions.