@article{dce93ae93b8042259b8fe156fd56f5d9,
title = "Optimization of a Multifrequency Magnetic Resonance Elastography Protocol for the Human Brain",
abstract = "BACKGROUND AND PURPOSE: The brain's stiffness measurements from magnetic resonance elastography (MRE) strongly depend on actuation frequencies, which makes cross-study comparisons challenging. We performed a preliminary study to acquire optimal sets of actuation frequencies to accurately obtain rheological parameters for the whole brain (WB), white matter (WM), and gray matter (GM). METHODS: Six healthy volunteers aged between 26 and 72 years old went through MRE with a modified single-shot spin-echo echo planar imaging pulse sequence embedded with motion encoding gradients on a 3T scanner. Frequency-independent brain material properties and best-fit material model were determined from the frequency-dependent brain tissue response data (20 -80 Hz), by comparing four different linear viscoelastic material models (Maxwell, Kelvin-Voigt, Springpot, and Zener). During the material fitting, spatial averaging of complex shear moduli (G*) obtained under single actuation frequency was performed, and then rheological parameters were acquired. Since clinical scan time is limited, a combination of three actuation frequencies that would provide the most accurate approximation and lowest fitting error was determined for WB, WM, and GM by optimizing for the lowest Bayesian information criterion (BIC). RESULTS: BIC scores for the Zener and Springpot models showed these models approximate the multifrequency response of the tissue best. The best-fit frequency combinations for the reference Zener and Springpot models were identified to be 30-60-70 and 30-40-80 Hz, respectively, for the WB. CONCLUSIONS: Optimal sets of actuation frequencies to accurately obtain rheological parameters for WB, WM, and GM were determined from shear moduli measurements obtained via 3-dimensional direct inversion. We believe that our study is a first-step in developing a region-specific multifrequency MRE protocol for the human brain.",
keywords = "Brain, magnetic resonance elastography, multifrequency, viscoelasticity",
author = "Mehmet Kurt and Lyndia Wu and Kaveh Laksari and Efe Ozkaya and Suar, {Zeynep M.} and Han Lv and Karla Epperson and Kevin Epperson and Sawyer, {Anne M.} and David Camarillo and Pauly, {Kim Butts} and Max Wintermark",
note = "Funding Information: Correspondence: Address correspondence to Mehmet Kurt, PhD, Department of Mechanical Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, McLean Hall, Room 525, Hoboken, NJ 07030. E-mail: mkurt@stevens.edu Acknowledgments and disclosure: Mehmet Kurt was supported by the Stanford Child Health Research Institute and the Thrasher Research Foundation Early Career Award. We thank Richard L. Ehman from the Mayo Clinic Rochester for providing the activation device and acknowledge his support through the NIH grant EB001981. We acknowledge support from Stanford Child Health Research Institute (David Camarillo), NIH 1R21NS111415-01 and NSF DCSD grant #1826270 (Mehmet Kurt). We acknowledge Karla Epperson, Kevin Epperson, and Anne M. Sawyer from the Richard M. Lucas Center for Imaging at Stanford University for their support during the experiments. Scanning of human volunteers was supported by General Electric Healthcare Tiger Team funding. The authors have no competing interest related to the study. Funding Information: and disclosure: Mehmet Kurt was supported by the Stanford Child Health Research Institute and the Thrasher Research Foundation Early Career Award. We thank Richard L. Ehman from the Mayo Clinic Rochester for providing the activation device and acknowledge his support through the NIH grant EB001981. We acknowledge support from Stanford Child Health Research Institute (David Camarillo), NIH 1R21NS111415-01 and NSF DCSD grant #1826270 (Mehmet Kurt). We acknowledge Karla Epperson, Kevin Epperson, and Anne M. Sawyer from the Richard M. Lucas Center for Imaging at Stanford University for their support during the experiments. Scanning of human volunteers was supported by General Electric Healthcare Tiger Team funding. The authors have no competing interest related to the study. Publisher Copyright: {\textcopyright} 2019 by the American Society of Neuroimaging",
year = "2019",
month = jul,
day = "1",
doi = "10.1111/jon.12619",
language = "English (US)",
volume = "29",
pages = "440--446",
journal = "Journal of Neuroimaging",
issn = "1051-2284",
publisher = "Wiley-Blackwell",
number = "4",
}