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Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI

Research output: Contribution to journalArticlepeer-review

David Marlevi, Jonas Schollenberger, Maria Aristova, Edward Ferdian, Yue Ma, Alistair A. Young, Elazer R. Edelman, Susanne Schnell, C. Alberto Figueroa, David A. Nordsletten

Original languageEnglish
Pages (from-to)3096-3110
Number of pages15
JournalMagnetic Resonance in Medicine
Volume86
Issue number6
DOIs
Accepted/In press2021
PublishedDec 2021

Bibliographical note

Funding Information: D.M. holds a Knut and Alice Wallenberg Foundation scholarship for postdoctoral studies at Massachusetts Institute of Technology. J.S. is supported by a University of Michigan Rackham Predoctoral Fellowship. M.A. was supported by a Ruth L. Kirschstein National Research Service Award (NIH F30 HL140910) and the Northwestern - Medical Science Training Program (NIH T32 GM815229). E.R.E. was funded in part by NIH R01 49039. A.A.Y. acknowledges core funding from the Wellcome/EPSRC Centre for Medical Engineering (WT203148/Z/16/Z) and the London Medical Imaging and AI Centre for Value-Based Healthcare. D.N. would like to acknowledge funding from Engineering and Physical Sciences Research Council (EP/N011554/1 and EP/R003866/1). Funding Information: D.M. holds a Knut and Alice Wallenberg Foundation scholarship for postdoctoral studies at Massachusetts Institute of Technology. J.S. is supported by a University of Michigan Rackham Predoctoral Fellowship. M.A. was supported by a Ruth L. Kirschstein National Research Service Award (NIH F30 HL140910) and the Northwestern ‐ Medical Science Training Program (NIH T32 GM815229). E.R.E. was funded in part by NIH R01 49039. A.A.Y. acknowledges core funding from the Wellcome/EPSRC Centre for Medical Engineering (WT203148/Z/16/Z) and the London Medical Imaging and AI Centre for Value‐Based Healthcare. D.N. would like to acknowledge funding from Engineering and Physical Sciences Research Council (EP/N011554/1 and EP/R003866/1). Publisher Copyright: © 2021 International Society for Magnetic Resonance in Medicine.

King's Authors

Abstract

Purpose: Hemodynamic alterations are indicative of cerebrovascular disease. However, the narrow and tortuous cerebrovasculature complicates image-based assessment, especially when quantifying relative pressure. Here, we present a systematic evaluation of image-based cerebrovascular relative pressure mapping, investigating the accuracy of the routinely used reduced Bernoulli (RB), the extended unsteady Bernoulli (UB), and the full-field virtual work-energy relative pressure ((Formula presented.) WERP) method. Methods: Patient-specific in silico models were used to generate synthetic cerebrovascular 4D Flow MRI, with RB, UB, and (Formula presented.) WERP performance quantified as a function of spatiotemporal sampling and image noise. Cerebrovascular relative pressures were also derived in 4D Flow MRI from healthy volunteers ((Formula presented.)), acquired at two spatial resolutions (dx = 1.1 and 0.8 mm). Results: The in silico analysis indicate that accurate relative pressure estimations are inherently coupled to spatial sampling: at dx = 1.0 mm high errors are reported for all methods; at dx = 0.5 mm (Formula presented.) WERP recovers relative pressures at a mean error of 0.02 ± 0.25 mm Hg, while errors remain higher for RB and UB (mean error of −2.18 ± 1.91 and −2.18 ± 1.87 mm Hg, respectively). The dependence on spatial sampling is also indicated in vivo, albeit with higher correlative dependence between resolutions using (Formula presented.) WERP (k = 0.64, R2 = 0.81 for dx = 1.1 vs. 0.8 mm) than with RB or UB (k = 0.04, R2 = 0.03, and k = 0.07, R2 = 0.07, respectively). Conclusion: Image-based full-field methods such as (Formula presented.) WERP enable cerebrovascular relative pressure mapping; however, accuracy is directly dependent on utilized spatial resolution.

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