Improving in situ acoustic intensity estimates using MR acoustic radiation force imaging in combination with multifrequency MR elastography

Autor: Ningrui Li, Pooja Gaur, Kristin Quah, Kim Butts Pauly
Rok vydání: 2022
Předmět:
Zdroj: Magnetic Resonance in Medicine. 88:1673-1689
ISSN: 1522-2594
0740-3194
Popis: Magnetic resonance acoustic radiation force imaging (MR-ARFI) enables focal spot localization during nonablative transcranial ultrasound therapies. As the acoustic radiation force is proportional to the applied acoustic intensity, measured MR-ARFI displacements could potentially be used to estimate the acoustic intensity at the target. However, variable brain stiffness is an obstacle. The goal of this study was to develop and assess a method to accurately estimate the acoustic intensity at the focus using MR-ARFI displacements in combination with viscoelastic properties obtained with multifrequency MR elastography (MRE).Phantoms with a range of viscoelastic properties were fabricated, and MR-ARFI displacements were acquired within each phantom using multiple acoustic intensities. Voigt model parameters were estimated for each phantom based on storage and loss moduli measured using multifrequency MRE, and these were used to predict the relationship between acoustic intensity and measured displacement.Using assumed viscoelastic properties, MR-ARFI displacements alone could not accurately estimate acoustic intensity across phantoms. For example, acoustic intensities were underestimated in phantoms stiffer than the assumed stiffness and overestimated in phantoms softer than the assumed stiffness. This error was greatly reduced using individualized viscoelasticity measurements obtained from MRE.We demonstrated that viscoelasticity information from MRE could be used in combination with MR-ARFI displacements to obtain more accurate estimates of acoustic intensity. Additionally, Voigt model viscosity parameters were found to be predictive of the relaxation rate of each phantom's time-varying displacement response, which could be used to optimize patient-specific MR-ARFI pulse sequences.
Databáze: OpenAIRE
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