Figure 1.
Experimental setup of MRE of the brain: a) Nonmagnetic driver placed at the end of the patient table.
b) Passive actuator integrated in the head coil and connected to a) by a carbon fiber piston. The main vibration direction is indicated by black arrows.
Figure 2.
Sequence timing diagram for acquisition of wave fields (three Cartesian motion components) in 30 slices at eight time steps () during a vibration period 1/f.
Figure 3.
Preprocessed wave fields used for MDEV inversion in a single slice.
Shown are the curl components (real parts of
after Fourier transformation).
Figure 4.
Changes in MDEV maps resulting from increasing noise suppression in a single transverse slice.
and φ are reconstructed from: (a) unsmoothed and unfiltered wave data; (b) smoothed wave data prior to curl calculations; (c) smoothed wave data prior to curl calculations and subsequent noise filter (wave number limit of 100 m−1. The processing steps used for (c) were applied to the rest of the paper.
Figure 5.
The influence of noise on MDEV inversion given by eq.(8).
The right-hand side and the left-hand side of eq.(7) are calculated from experimental data at individual drive frequencies from 30 Hz to 60 Hz (note, for demonstration purposes, experiments were performed in a volunteer with 7 excitation frequencies ranging from 30 to 60 Hz in increments of 5 Hz). The triangles correspond to unfiltered curl components, and the circles are obtained by applying the filter described in the Methods section (see also fig. 4c). Data from different frequencies are color-coded with different filling patterns (open symbols: 45–60 Hz; solid gray: 40 Hz; horizontal line pattern: 35 Hz; vertical line pattern: 30 Hz). The slopes of the fit lines correspond to modulus . According to eq.(8), the fit lines are forced to run through the origin, resulting in severe underestimation of
. A better implementation of least-squares inversion would account for an offset as done by the fit yielding
(here only the consistent high-frequency data [blue symbols] are considered). The noise-related offset is suppressed by an appropriate noise filter as used in figure 4c yielding
.
Figure 6.
Elastographic atlases according to 3DMMRE at 30, 40, 50 and 60 Hz vibration frequencies and MDEV inversion of the data of 23 healthy volunteers.
Figure 7.
For illustration, one central slice of the T1-weighted template data (T1w), maps and φ maps are selectively shown with all four anatomical regions, i.e., the head of caudate nucleus (HCN), thalamus (TH), corpus callosum genu (CCG), and white matter (WM).
Regions of HCN, TH, and CCG were manually selected while the WM region was automatically segmented from the T1w.
Table 1.
Spatially averaged MRE parameters in four regions of the brain.
Figure 8.
Regional differences estimated using non-normalized elastographic maps of subjects obtained in the range of drive frequencies from 30 Hz to 60 Hz.
The boxplot depicts the lower and upper quartiles and the 50th percentile (median) from the 23 subject group. The full data range is presented by whiskers.