4Dflow-VP-Net: A deep convolutional neural network for noninvasive estimation of relative pressures in stenotic flows from 4D flow MRI.

PURPOSE: To estimate relative transvalvular pressure gradient (TVPG) noninvasively from 4D flow MRI. METHODS: A novel deep learning-based approach is proposed to estimate pressure gradient across stenosis from four-dimensional flow MRI (4D flow MRI) velocities. A deep neural network 4D flow Velocity-to-Presure Network (4Dflow-VP-Net) was trained to learn the spatiotemporal relationship between velocities and pressure in stenotic vessels. Training data were simulated by computational fluid dynamics (CFD) for different pulsatile flow conditions under an aortic flow waveform. The network was tested to predict pressure from CFD-simulated velocity data, in vitro 4D flow MRI data, and in vivo 4D flow MRI data of patients with both moderate and severe aortic stenosis. TVPG derived from 4Dflow-VP-Net was compared to catheter-based pressure measurements for available flow rates, in vitro and Doppler echocardiography-based pressure measurement, in vivo. RESULTS: Relative pressures calculated by 4Dflow-VP-Net and in vitro pressure catheterization revealed strong correlation (r2 = 0.91). Correlations analysis of TVPG from reference CFD and 4Dflow-VP-Net for 450 simulated flow conditions showed strong correlation (r2 = 0.99). TVPG from in vitro MRI had a correlation coefficient of r2 = 0.98 with reference CFD. 4Dflow-VP-Net, applied to 4D flow MRI in 16 patients, showed comparable TVPG measurement with Doppler echocardiography (r2 = 0.85). Bland-Altman analysis of TVPG measurements showed mean bias and limits of agreement of -0.20 ± 2.07 mmHg and 0.19 ± 0.45 mmHg for CFD-simulated velocities and in vitro 4D flow velocities. In patients, overestimation of Doppler echocardiography relative to TVPG from 4Dflow-VP-Net (10.99 ± 6.77 mmHg) was observed. CONCLUSION: The proposed approach can predict relative pressure in both in vitro and in vivo 4D flow MRI of aortic stenotic patients with high fidelity.

The relationships among spinal CSF flows, spinal cord geometry, and vascular correlations: evidence of intrathecal sources and sinks.

We studied relationships of cerebral spinal fluid (CSF) pulsatile flow at cervical, thoracic, and lumbar levels using phase-contrast cine MRI (PCCMRI) to determine the following: 1) instantaneous and average net flows at cervical, thoracic, and lumbar levels, 2) stochastic correlations of CSF flow with major arterial supplies and major draining veins, and 3) whether adjustments of cord-flow curves-using cord cross-sectional areas, caudal lengths, and caudal volumes-would normalize flow curves from different levels. We scanned 15 healthy volunteers without anesthesia, ages 23-46 yr, using external, retrocardiac-gated, two-dimensional PCCMRI at 3T. Transverse scans of the subarachnoid space, arteries, and veins were acquired and analyzed at cervical, thoracic, and lumbar levels. Instantaneous CSF flow decreased craniocaudally along the full time course of a cardiac cycle. Downward net flow generally increased craniocaudally. During diastole, instantaneous CSF flow decreased proportionally to cross-sectional area, caudal residual length, and caudal residual volume of the cord. The proportionalities were less consistent during systole. CSF, internal carotid artery (ICA), vertebral artery, and lower aorta temporal correlations were highest in systole and decreased craniocaudally. CSF flow temporally correlated better with lower aorta flow than with the ICA at T7 and L2 during systole but not diastole. Inferior vena cava temporal correlation increased craniocaudally. We conclude that whereas instantaneous flow is attenuated cranial caudally, net downward flow, per cardiac cycle, increases caudally, becoming statistically significant at T7 and below the conus medullaris. We can explain the results with the assumption of cord CSF production and peripheral-dominated CSF absorption.

FlowRAU-Net: Accelerated 4D Flow MRI of Aortic Valvular Flows With a Deep 2D Residual Attention Network.

In this work, we propose a novel deep learning reconstruction framework for rapid and accurate reconstruction of 4D flow MRI data. Reconstruction is performed on a slice-by-slice basis by reducing artifacts in zero-filled reconstructed complex images obtained from undersampled k-space. A deep residual attention network FlowRAU-Net is proposed, trained separately for each encoding direction with 2D complex image slices extracted from complex 4D images at each temporal frame and slice position. The network was trained and tested on 4D flow MRI data of aortic valvular flow in 18 human subjects. Performance of the reconstructions was measured in terms of image quality, 3-D velocity vector accuracy, and accuracy in hemodynamic parameters. Reconstruction performance was measured for three different k-space undersamplings and compared with one state of the art compressed sensing reconstruction method and three deep learning-based reconstruction methods. The proposed method outperforms state of the art methods in all performance measures for all three different k-space undersamplings. Hemodynamic parameters such as blood flow rate and peak velocity from the proposed technique show good agreement with reference flow parameters. Visualization of the reconstructed image and velocity magnitude also shows excellent agreement with the fully sampled reference dataset. Moreover, the proposed method is computationally fast. Total 4D flow data (including all slices in space and time) for a subject can be reconstructed in 69 seconds on a single GPU. Although the proposed method has been applied to 4D flow MRI of aortic valvular flows, given a sufficient number of training samples, it should be applicable to other arterial flows.