4D Flow Magnetic Resonance Imaging (MRI) allows non-invasive assessment of cardiovascular hemodynamics through the acquisition of three-dimensional pulsatile velocities in a single scan. However, this technique is often plagued by issues of noise and low resolution. In this paper, we employed a deep learning-based super-resolution method utilizing an SR residual network (ResNet) to enhance the measurement of hemodynamic indices at a higher resolution. Our approach enables the derivation of hemodynamic parameters dependent on spatiotemporal velocity derivatives such as vorticity, circulation, and turbulent kinetic energy, which were validated using a phantom model of arterial stenosis. We also compared the deep learning approach with linear, nearest neighbor, and natural interpolation methods with a 2x upsampling factor. The results were evaluated against Computational Fluid Dynamics simulations as a reference and showed that the deep learning approach improved the accuracy of turbulent kinetic energy (TKE) and viscous energy loss at peak systole by 7% and 9%, respectively, indicating a significant enhancement over traditional interpolation methods. Additionally, herein we introduce a novel hemodynamic parameter, enstrophy, as a potential diagnostic biomarker for assessing stenosis severity. Overall, our findings suggest that deep learning is a reliable and efficient approach for predicting hemodynamic parameters from 4Dflow MRI.
In this paper we propose a deep learning framework to estimate pressure from 4D flow MRI. Pressure drop is an important parameter to detect and diagnose different cardiovascular diseases. Accurate estimation of pressure from 4D flow MRI is hampered however due to noise and low resolution of 4D flow data. In the proposed method we consider the pressure estimation as a mapping function between velocity to pressure and employ an encoder-decoder based deep network for the mapping. A computational fluid dynamic model was designed which identically matched the geometry of a stenotic flow phantom used in 4D Flow MRI experiments and velocity and pressure data was simulated for 1000 different flow conditions to train the network. In addition, the proposed network was tested on real in -vitro 4D flow MRI in the same stenotic model for 3 different flow rates. Estimated pressures from the network showed excellent agreement with the reference CFD simulated pressures. As measure of fidelity, relative pressure drop across the stenosis was computed between the reference pressure and estimated pressure and were compared with the simplified Bernouli method. It was determined that the pressure drop estimation by the proposed method is more accurate than competing method.
To permit optimization of 4D flow protocols in imaging of thoracic aorta, a flow phantom was designed and constructed from clear acrylic plastic. The phantom was precision machined out of clear acrylic plastic for continuous flow, ability to see unwanted air bubbles, and MR compatibility. The solid model of the phantom was designed in SolidWorks and fed to a computer numeric control (CNC) machine for precision machining. The design permits the operator to switch aortic valves constructed from a silicone mold with various degrees of calcifications (different percentage openings), modeling an aortic valve at various stages of disease. The valve opens and closes during the cardiac cycle as in the in-vivo case. The inner diameter of the tube throughout the phantom was 1”, which corresponds to human anatomical measurements in the average person. The phantom was placed in an MR compatible flow circuit, with a 60:40 distilled water/glycerol fluid mixture resulting in a viscosity of 0043 Pa*s and density of 1,060 kg/m3 similar to those of blood. The pump driving the working fluid in the phantom is programmable, capable of delivering physiologic flow rates up to peak flow of 400 ml/s The phantom was placed inside a Philips Achieva 1.5 T scanner and imaged with a 16 element XL Torso Coil. 4D flow imaging was performed at a Venc of 250 cm/s. The field of view was 120 mm x 120 mm x 150 mm, with a voxel size of 1.5 mm x 1.5 mm x 5 mm, and 14 phases. Other scan parameters were as follows: TR=11 ms, TE=4 ms and TFE factor=2.
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