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Results: 5

Figure 5

Figure 5. From: Magnetic Resonance Elastography of the Brain.

Shear stiffness measurements for the age study volunteers obtained at 100 Hz.

Scott A. Kruse, et al. Neuroimage. ;39(1):231-237.
Figure 3

Figure 3. From: Magnetic Resonance Elastography of the Brain.

(a) Image depicting shear waves propagating in the tissue-simulating gel phantom. Shear waves at 100 Hz were applied to the gel cylinder using the actuator in Figure 2a. This image is one of eight that were acquired, each providing a quantitative depiction of the deformation of the medium caused by the propagating waves at a different time point in the wave cycle. The circular cross section of the stiff inclusion can be identified in the image because the wavelength of the propagating shear waves is longer in the stiffer material. (b) A GRE image indicates the location of the stiff inclusion. (c) The elastogram obtained from the wave data in (a) using the spatio-temporal directional filters and the LFE algorithm.

Scott A. Kruse, et al. Neuroimage. ;39(1):231-237.
Figure 2

Figure 2. From: Magnetic Resonance Elastography of the Brain.

Schematic diagram of the magnetic resonance elastography system. Conventional MR imaging gradients and RF pulses that encode spatial positions are shown at the bottom left. The electromechanical actuator (a) applies vertical displacement to the object to be imaged via a cradle or (b) horizontal displacement via a bite block (right). The cyclic motion-sensitizing gradients and the actuator are synchronized using trigger pulses provided by the imager. The phase offset (θ) between the two can be varied to image the waves at various stages of propagation. As shown by the shaded regions, the motion-sensitizing gradients can be superimposed along any desired axis to detect that component of the cyclic motion vector. All data was collected and analyzed using 100 Hz motion.

Scott A. Kruse, et al. Neuroimage. ;39(1):231-237.
Figure 4

Figure 4. From: Magnetic Resonance Elastography of the Brain.

Results from the MRE experiment performed on 2 different volunteers aged 25 and 23 using 100-Hz mechanical excitations. (a) T2-weighted FSE images for anatomical reference. The ROIs for gray matter and white matter are indicated in the top and bottom rows respectively. (b) Images indicating the shear waves propagating in the brain. The shear waves propagate from the perimeter of the brain inward. (c) The shear stiffness maps computed from the Local Frequency Estimate (LFE) algorithm. A threshold, based on a phase difference SNR of 5:1, was applied to the shear stiffness estimates to mask regions with low displacement amplitude. (d) The shear stiffness maps overlaid on the anatomical reference illustrate the correlation of stiffness changes to anatomy.

Scott A. Kruse, et al. Neuroimage. ;39(1):231-237.
Figure 1

Figure 1. From: Magnetic Resonance Elastography of the Brain.

Shear stiffness measurements in the literature of mammalian brain tissue (Arbogast et al. 1998; Bilston et al. 1997; Donnelly et al. 1997; Estes et al. 1970; Fallenstein et al. 1969; Galford et al. 1970; Green et al. 2006; Hamhaber et al. 2007; Hirakawa et al. 1981; Ljung 1975; Metz et al. 1970; Shuck et al. 1972; Uffmann et al. 2004; Walsh et al. 1976; Wang et al. 1972). The studies were performed in vitro, ex vivo and in vivo (denoted by *) using a variety of experimental techniques (shear/strain, load cell, pressure transducer, vibrating probe and MR elastography). The frequency of dynamic testing is indicated. The shear wave speed was calculated from the real and imaginary parts of the complex modulus (Auld 1990; Oliphant et al. 2001). The result was then entered into Equation [2] to calculate shear stiffness.

Scott A. Kruse, et al. Neuroimage. ;39(1):231-237.

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