MR elastography of the brain and its application in neurological diseases

Abstract

Magnetic resonance elastography (MRE) is an imaging technique for noninvasively and quantitatively assessing tissue stiffness, akin to palpation. MRE is further able assess the mechanical properties of tissues that cannot be reached by hand including the brain. The technique is a three-step process beginning with the introduction of shear waves into the tissue of interest by applying an external vibration. Next, the resulting motion is imaged using a phase-contrast MR pulse sequence with motion encoding gradients that are synchronized to the vibration. Finally, the measured displacement images are mathematically inverted to compute a map of the estimated stiffness. In the brain, the technique has demonstrated strong test-retest repeatability with typical errors of 1% for measuring global stiffness, 2% for measuring stiffness in the lobes of the brain, and 3-7% for measuring stiffness in subcortical gray matter. In healthy volunteers, multiple studies have confirmed that stiffness decreases with age, while more recent studies have demonstrated a strong relationship between viscoelasticity and behavioral performance. Furthermore, several studies have demonstrated the sensitivity of brain stiffness to neurodegeneration, as stiffness has been shown to decrease in multiple sclerosis and in several forms of dementia. Moreover, the spatial pattern of stiffness changes varies among these different classes of dementia. Finally, MRE is a promising tool for the preoperative assessment of intracranial tumors, as it can measure both tumor consistency and adherence to surrounding tissues. These factors are important predictors of surgical difficulty. In brief, MRE demonstrates potential value in a number of neurological diseases. However, significant opportunity remains to further refine the technique and better understand the underlying physiology.

Figure 1. (Johnson et al., 2016)

Summary of shear stiffness in subcortical gray matter structures including amygdala (Am), hippocampus (Hc), caudate (Ca), putamen (Pu), palldium (pa), and thalamus (Th). Surrounding cerebrum (CB) is also included for reference. A. Plot presents the mean stiffness for each participant by region with the line indicating the group mean. B. Image shows the mean shear stiffness of each structure after normalization to a common space. C. Table summarizes pair-wise comparisons of stiffness between regions as assessed by paired t-tests (*p<0.05 with Bonferroni correction).

Figure 2. (Sack et al., 2009)

Summary of the relationship between global brain viscoelasticity and age. Stiffness estimates over multiple frequencies were fit to a two-parameter springpot model. a. The viscoelastic modulus from this model (µ) decreases significantly with age and is larger in women compared to men. b. The structural parameter (α) displays no significant effects due to age or sex.

Figure 3. (Schwarb et al., 2016)

Summary of relationship between hippocampal damping ratio and performance on a spatial reconstruction (SR) task. A. Scatter plots of adjusted damping ratio versus performance as measured by either the total distance of object misplacement (SR Distance) or by the number of arrangement errors (SR Arrangement). Performance metrics were normalized and adjusted so that larger scores reflect better performance. B. This image displays the average damping ratio across all subjects in a common space.

Figure 4. (Streitberger et al., 2012)

Summary of viscoelasticity in subjects with multiple sclerosis (MS) compared to age-matched controls. Stiffness estimates over multiple frequencies were fit to a two-parameter springpot model. a. Shear elasticity (µ) was reduced in MS patients with a secondary (sp) or primary (pp) chronic progressive course, as well as in patients with a relapsing-remitting (rr) disease course. b. The powerlaw exponent (or structural parameter, α) was only reduced in the chronic progressive disease course.

Figure 5. (Schregel et al., 2012)

Example images from a control mouse (left) and a mouse undergoing 12 weeks of cuprizone diet (right). In the control mouse, the corpus callosum is easily identified in the T2-weighted anatomical image (top, white arrows) and observed to be relatively firm in the corresponding stiffness map (bottom). In the experimental animal, the corpus callosum is not apparent on T2-weighted imaging and the stiffness is markedly reduced.

Figure 6. (data from ElSheikh et al., 2017)

Summary of stiffness changes due to 4 forms of dementia. In the first panel is a sagittal view of a lobar brain atlas with each region showing the mean stiffness in a group of cognitively normal (CN) control subjects. Regions include frontal lobe (F), parietal lobe (P), temporal lobe (T), occipital lobe (O), deep gray and white matter (D), and cerebellum (C). The remaining panels display the difference between mean stiffness in the dementia group and the CN group. These 4 groups include Alzheimer’s disease (AD), frontotemporal dementia (FTD), normal pressure hydrocephalus (NPH), and dementia with Lewy bodies (DLB).

Figure 7. (Murphy et al., 2013a)

Example MRE images from both a firm (top) and soft (bottom) meningioma case. On the left is a T1-weighted anatomical image. Example wave images are shown in the middle column, showing an elongated wavelength in the firm tumor and shortened wavelength in the soft tumor relative to surrounding brain parenchyma. Finally, the resulting stiffness maps are shown in the right column.

Figure 8. (data from Yin et al., 2017)

Example slip interface images from both an adherent (top) and non-adherent (bottom) meningioma case. The tumor location can be seen in the T2-weighted FLAIR images in the left column. The shear strain maps are shown on the right. Note the bright ring around the tumor in the non-adherent case, which is absent in the adherent case.