SNR Weighting for Shear Wave Speed Reconstruction in Tomoelastography

MR Elastography With Optimization-Based Phase Unwrapping and Traveling Wave Expansion-Based Neural Network (TWENN)

Magnetic Resonance Elastography (MRE) can characterize biomechanical properties of soft tissue for disease diagnosis and treatment planning. However, complicated wavefields acquired from MRE coupled with noise pose challenges for accurate displacement extraction and modulus estimation. Using optimization-based displacement extraction and Traveling Wave Expansion-based Neural Network (TWENN) modulus estimation, we propose a new pipeline for processing MRE images. An objective function with Dual Data Consistency (Dual-DC) has been used to ensure accurate phase unwrapping and displacement extraction. For the estimation of complex wavenumbers, a complex-valued neural network with displacement covariance as an input has been developed. A model of traveling wave expansion is used to generate training datasets for the network with varying levels of noise. The complex shear modulus map is obtained through fusion of multifrequency and multidirectional data. Validation using brain and liver simulation images demonstrates the practical value of the proposed pipeline, which can estimate the biomechanical properties with minimal root-mean-square errors when compared to state-of-the-art methods. Applications of the proposed method for processing MRE images of phantom, brain, and liver reveal clear anatomical features, robustness to noise, and good generalizability of the pipeline.

Phase-regularized and displacement-regularized compressed sensing for fast magnetic resonance elastography

Liver magnetic resonance elastography (MRE) is a noninvasive stiffness measurement technique that captures the tissue displacement in the phase of the signal. To limit the scanning time to a single breath-hold, liver MRE usually involves advanced readout techniques such as simultaneous multislice (SMS) or multishot methods. Furthermore, all these readout techniques require additional in-plane acceleration using either parallel imaging capabilities, such as sensitivity encoding (SENSE), or k -space undersampling, such as compressed sensing (CS). However, these methods apply a single regularization function on the complex image. This study aims to design and evaluate methods that use separate regularization on the magnitude and phase of MRE to exploit their distinct spatiotemporal characteristics. Specifically, we introduce two compressed sensing methods. The first method, termed phase-regularized compressed sensing (PRCS), applies a two-dimensional total variation (TV) prior to the magnitude and two-dimensional wavelet regularization to the phase. The second method, termed displacement-regularized compressed sensing (DRCS), exploits the spatiotemporal redundancy using 3D total variation on the magnitude. Additionally, DRCS includes a displacement fitting function to apply wavelet regularization to the displacement phasor. Both DRCS and PRCS were evaluated with different levels of compression factors in three datasets: an in silico abdomen dataset, an in vitro tissue-mimicking phantom, and an in vivo liver dataset. The reconstructed images were compared with the full sampled reconstruction, zero-filling reconstruction, wavelet-regularized compressed sensing, and a low rank plus sparse reconstruction. The metrics used for quantitative evaluation were the structural similarity index (SSIM) of magnitude (M-SSIM), displacement (D-SSIM), and shear modulus (S-SSIM), and mean shear modulus. Results from highly undersampled in silico and in vitro datasets demonstrate that the DRCS method provides higher reconstruction quality than the conventional compressed sensing method for a wide range of stiffness values. Notably, DRCS provides 24% and 22% increase in D-SSIM compared with CS for the in silico and in vitro datasets, respectively. Comparison with liver stiffness measured from full sampled data and highly undersampled data (CR=4) demonstrates that the DRCS method provided the strongest correlation ( R 2 =0.95), second-lowest mean bias (-0.18 kPa, lowest for CS with -0.16 kPa), and lowest coefficient of variation (CV=3.6%). Our results demonstrate the potential of using DRCS to improve the reconstruction quality of accelerated MRE.

Inversion-recovery MR elastography of the human brain for improved stiffness quantification near fluid-solid boundaries

PURPOSE

In vivo MR elastography (MRE) holds promise as a neuroimaging marker. In cerebral MRE, shear waves are introduced into the brain, which also stimulate vibrations in adjacent CSF, resulting in blurring and biased stiffness values near brain surfaces. We here propose inversion-recovery MRE (IR-MRE) to suppress CSF signal and improve stiffness quantification in brain surface areas.

METHODS

Inversion-recovery MRE was demonstrated in agar-based phantoms with solid-fluid interfaces and 11 healthy volunteers using 31.25-Hz harmonic vibrations. It was performed by standard single-shot, spin-echo EPI MRE following 2800-ms IR preparation. Wave fields were acquired in 10 axial slices and analyzed for shear wave speed (SWS) as a surrogate marker of tissue stiffness by wavenumber-based multicomponent inversion.

RESULTS

Phantom SWS values near fluid interfaces were 7.5 ± 3.0% higher in IR-MRE than MRE (P = .01). In the brain, IR-MRE SNR was 17% lower than in MRE, without influencing parenchymal SWS (MRE: 1.38 ± 0.02 m/s; IR-MRE: 1.39 ± 0.03 m/s; P = .18). The IR-MRE tissue-CSF interfaces appeared sharper, showing 10% higher SWS near brain surfaces (MRE: 1.01 ± 0.03 m/s; IR-MRE: 1.11 ± 0.01 m/s; P < .001) and 39% smaller ventricle sizes than MRE (P < .001).

CONCLUSIONS

Our results show that brain MRE is affected by fluid oscillations that can be suppressed by IR-MRE, which improves the depiction of anatomy in stiffness maps and the quantification of stiffness values in brain surface areas. Moreover, we measured similar stiffness values in brain parenchyma with and without fluid suppression, which indicates that shear wavelengths in solid and fluid compartments are identical, consistent with the theory of biphasic poroelastic media.