In this work we investigate the influence of intravoxel velocity distributions on the velocity noise in phase contrast velocimetry. Intravoxel velocity distributions are directly measured via Fourier velocity encoding and subsequently fitted by Gaussian distributions. Taking intravoxel dephasing due to a finite distribution width into account, a noise-optimized VENC is calculated for phantom flow measurements and in-vivo data at 7 Tesla.
Phase-contrast MRI is a standard velocity imaging technique to noninvasively characterize hemodynamics in-vivo. The velocity is obtained by acquiring at least two images with different first gradient moments m1=∫tendt0G(t)⋅t dt. Here t0 is the isodelay time point (typically RF pulse center) and tend is the echo time. The corresponding velocity noise is commonly [1] described as σv=√2 VENCπ SNR with velocity sensitivity VENC=πγ∣△m1∣, gyromagnetic ratio γ, signal-to-noise-ratio SNR=SσS and standard deviation of the magnitude noise σS. As the velocity noise is linearly depenent on VENC, low VENC values are typically desirable and have led to increased popularity of multi-VENC methods [2-5].
This theory is based on the assumption that the velocity distribution within a voxel is a delta peak. Under real conditions, however, the velocity distribution can cover a broad spectrum. In this work the intravoxel velocity distribution is measured in a flow phantom and in-vivo and then used to model the velocity noise σv as a function of VENC.
Fourier velocity encoding (FVE) acquisitions to measure the intravoxel velocity distributions [6] were performed in a phantom and in-vivo at 7T (Siemens, Germany) using a 28-channel knee coil (Quality Electrodynamics, USA). An agarose-filled cylindrical phantom containing four water-filled pipes with diameters 4.2 / 7.8 / 12.1 / 15.5mm and water flowing through the two largest pipes was used. A programmable physiological flow pump (Shelly Medical Imaging Technologies, Canada) was used to create constant flow of different flow rates as well as a pulsatile flow profile. In-vivo data targeting the femoral artery of a healthy volunteer were acquired after obtaining consent to an IRB approved protocol.
The phantom and in-vivo FVE acquisitions were performed with cardiac triggering to allow the reconstruction of different cardiac phases. Resolution: 1mm⋅1mm⋅1.5cms, FOV: 128mm⋅128mm⋅168cms, TE: 14.2ms. Gaussian distributions were fitted to the data in MATLAB (The Mathworks, USA), and subsequently VENCopt values were calculated. A standard 2D velocity sequence was acquired for both phantom and in-vivo experiments using VENC=100cms.
In this work we modeled the velocity noise as a function of VENC. The results demonstrate that simply lowering VENC, as assumed in Multi-VENC imaging, does not necessarily result in reduced velocity noise: for the in-vivo data we obtained VENCopt=30cms (60cms) in double- (single-) sided encoding close to the vessel wall.
Limitations of the FVE in-vivo study were i) the long echo-time leading to flow related displacement artifacts (slice and femoral artery were not perfectly orthogonal) and ii) the long scan time of 130min leading to degradation due to possible movement.
Our findings have, for example, implications for wall shear stress (WSS) imaging: WSS is derived from velocities close to the vessel wass, i.e. where we measured the highest VENCopt, and WSS is particularly sensitive to noise due to the spatial derivative. Furthermore, we used a spatial resolution of 1mm⋅1mm, but if resolution is lowered to 2mm⋅2mm (typical in aortic 4D flow MRI), VENCopt is assumed to increase to approximately 60cms (120cms) for double- (single-) sided encoding.
Furthermore the obtained results can be used to develop an optimized m1 pattern for flow MR Fingerprinting [7].
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[3] Ha, H., Kim, G. B., Kweon, J., Kim, Y.-H., Kim, N., Yang, D. H. and Lee, S. J. (2016), Multi-VENC acquisition of four-dimensional phase-contrast MRI to improve precision of velocity field measurement. Magn. Reson. Med., 75: 1909–1919.
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[5] Schnell, S., Ansari, S. A., Wu, C., Garcia, J., Murphy, I. G., Rahman, O. A., Rahsepar, A. A., Aristova, M., Collins, J. D., Carr, J. C. and Markl, M. (2017), Accelerated dual-venc 4D flow MRI for neurovascular applications. J. Magn. Reson. Imaging, 46: 102–114.
[6] Moran, P. R. 1982. A flow velocity zeugmatographic interlace for NMR imaging in humans. Magn. Reson. Imaging 1: 197-203.
[7] Flassbeck, S., Schmidt, S., Breithaupt, M., Bachert, P., Ladd, M. E., Schmitter, S.,
Quantification of Flow by Magnetic Resonance Fingerprinting. In
Proceedings of the 25th Annual Meeting of the ISMRM, Honolulu, HI, USA, 2017.
Abstract 0128.