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Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI

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

2021Magnetic Resonance in Medicine21 citationsDOIOpen Access PDF

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 ( WERP) method. Methods Patient‐specific in silico models were used to generate synthetic cerebrovascular 4D Flow MRI, with RB, UB, and 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 ( ), 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 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 WERP ( k = 0.64, R 2 = 0.81 for dx = 1.1 vs. 0.8 mm) than with RB or UB ( k = 0.04, R 2 = 0.03, and k = 0.07, R 2 = 0.07, respectively). Conclusion Image‐based full‐field methods such as WERP enable cerebrovascular relative pressure mapping; however, accuracy is directly dependent on utilized spatial resolution.

Topics & Concepts

Bernoulli's principleApproximation errorSampling (signal processing)Relative standard deviationIn silicoBiomedical engineeringNuclear magnetic resonanceChemistryNuclear medicineMathematicsPhysicsStatisticsMedicineDetectorOpticsGeneBiochemistryDetection limitThermodynamicsAdvanced MRI Techniques and ApplicationsCardiac Imaging and DiagnosticsCerebrovascular and Carotid Artery Diseases