Reverberant magnetic resonance elastographic imaging using a single mechanical driver

Abstract

Reverberant elastography provides fast and robust estimates of shear modulus; however, its reliance on multiple mechanical drivers hampers clinical utility. In this work, we hypothesize that for constrained organs such as the brain, reverberant elastography can produce accurate magnetic resonance elastograms with a single mechanical driver. To corroborate this hypothesis, we performed studies on healthy volunteers (n=3); and a constrained calibrated brain phantom containing spherical inclusions with diameters ranging from 4-18 mm. In both studies (i.e., phantom and clinical), imaging was performed at frequencies of 50 Hz and 70 Hz. We used the accuracy and contrast-to-noise ratio performance metrics to evaluate reverberant elastograms relative to those computed using the established subzone inversion method. Errors incurred in reverberant elastograms varied from 1.3% to 16.6% when imaging at 50 Hz and 3.1% and 16.8 % when imaging at 70 Hz. In contrast, errors incurred in subzone elastograms ranged from 1.9% to 13% at 50 Hz and 3.6% to 14.9% at 70 Hz. The contrast-to-noise ratio of reverberant elastograms ranged from 63.1 dB to 73 dB compared to 65 dB to 66.2 dB for subzone elastograms. The average global brain stiffness estimated from reverberant and subzone elastograms was 2.36 ± 0.07 kPa and 2.38 ± 0.11 kPa, respectively, when imaging at 50 Hz and 2.70 ± 0.20 kPa and 2.89 ± 0.60 kPa respectively, when imaging at 70 Hz. The results of this investigation demonstrate that reverberant elastography can produce accurate, high-quality elastograms of the brain with a single mechanical driver.

Limitations of Curl and Directional Filters in Elastography

“…Another means of mitigating the difficulties is to establish a 3D set of shear waves across multiple directions, producing the reverberant shear wave field. In that case, the imaging system ROI can have an arbitrary configuration with respect to the organ of interest, and the estimators take advantage of the simple limiting mathematics of the expected ensemble of waves in 3D [46][47][48]. This enables the freehand orientation of the imaging plane, as is common with hand-held imaging probes, in addition to preset 3D scan geometries.…”

Angular Integral Autocorrelation for Speed Estimation in Shear-Wave Elastography

“…The single-shot echo-planar imaging (EPI) sequence [46] was utilized to measure the resulting time-varying harmonic tissue displacements. More details about the phantom properties, MRE experiment parameters, and data postprocessing to determine displacement field are described in Kabir et al [47].…”

“…All MRE measurements of SWS and SNR were conducted considering the almost-full 2D background, with some distance to the lesions' boundaries. According to Kabir et al [47], the background shear modulus for the brain phantom is 3.34 ± 0.04 kPa, equivalent to SWSs of 1.83 m/s. It is evident that the estimated SWS using kx is an outlier with an average error of 157% for the X-motion and Z-motion displacement fields in the background.…”

“…Table 7. SWS in the background material for the brain phantom MRE estimated using simple autocorrelation approaches and the AIA approach in X-motion and Z-motion displacement fields, errors were calculated using the ground truth SWS value of 1.83 (m/s) for the brain phantom as reported in Kabir et al [47]. Table 8 illustrates the SNR of estimated SWS within the background material for the brain phantom MRE using simple autocorrelation approaches in the x and y directions, as well as the AIA approach for both X-motion and Z-motion displacement fields.…”

Brain elastography in aging relates to fluid/solid trendlines

“…In order to winnow down the large possible set of contributors to the decreasing brain stiffness versus age, and to identify a more focused set of dominant mechanisms which plausibly set the trendline, we utilize a multiscale biphasic model of fluid channels in an otherwise elastic parenchymal matrix, generically called the 'microchannel flow model' (MFM). Within the framework created by the model, we examine and attempt to model key changes in the elastic nature of the brain as a function of age using shear wave elastography techniques presented in Kabir et al (2023), Kabir (2023).…”