Simulation of harmonic shear waves in the human brain and comparison with measurements from magnetic resonance elastography

Effect of blast orientation, multi-point blasts, and repetitive blasts on brain injury

Explosions in the battlefield can result in brain damage. Research on the effects of shock waves on brain tissue mainly focuses on the effects of single-orientation blast waves, while there have been few studies on the dynamic response of the human brain to directional explosions in different planes, multi-point explosions and repetitive explosions. Therefore, the brain tissue response and the intracranial pressure (ICP) caused by different blast loadings were numerically simulated using the CONWEP method. In the study of the blast in different directions, the lateral explosion blast wave was found to cause greater ICP than did blasts from other directions. When multi-point explosions occurred in the sagittal plane simultaneously, the ICP in the temporal lobe increased by 37.8 % and the ICP in the parietal lobe decreased by 17.6 %. When multi-point explosions occurred in the horizontal plane, the ICP in the frontal lobe increased by 61.8 % and the ICP in the temporal lobe increased by 12.2 %. In a study of repetitive explosions, the maximum ICP of the second blast increased by 40.6 % over that of the first blast, and that of the third blast increased by 61.2 % over that of the second blast. The ICP on the brain tissue from repetitive blasts can exceed 200 % of that of a single explosion blast wave.

Fusion of Magnetic Resonance Elastography Images With Computed Tomography and Magnetic Resonance Imaging Using the Human Visual System

Magnetic resonance elastography (MRE) is used to assess the stiffness of the liver to rule out cirrhosis or fibrosis. The image, nevertheless, is regarded as shear-wave imaging and does not depict any anatomical features. Multimodality medical image fusion (MMIF), such as the fusion of MRE with computed tomography (CT) scan or magnetic resonance imaging (MRI), can help doctors optimize the advantages of each imaging technique. As a result, perceptions serve as valid and valuable assessment criteria. The contrast sensitivity function (CSF), which describes the rates of visual contrast sensitivity through the changing of spatial frequencies, is used mathematically to characterize the human visual system (HVS). As a result, we suggest novel methods for fusing images that use discrete wavelets transform (DWT) based on HVS and CSF models. Images from MRI or CT scan were combined with MRE images, and the outcomes were assessed both subjectively and objectively. Visual inspection of merging images was done throughout the qualitative analysis. The CT-MRE fused images in all four datasets were shown to be superior at maintaining bones and spatial resolution, despite the MRI-MRE being better at exhibiting soft tissues and contrast resolution. It is clear from all four datasets that the liver soft tissue in MRI and CT images mixed successfully with the red-colored stiffness distribution seen in MRE images. The proposed approach outperformed DWT, which produced visual artifacts such as signal loss. Quantitative evaluation using mean, standard deviation, and entropy showed that the generated images from the proposed technique performed better than the source images and DWT. Additionally, peak signal-to-noise ratio, mean square error, correlation coefficient, and structural similarity index measure were employed to compare the two fusion approaches, namely, MRI-MRE and CT-MRE. The comparison did not show the superiority of one approach over the other. In conclusion, both subjective and objective evaluation approaches revealed that the combined images contained more information and characteristics. Hence, the proposed method might be a useful procedure to diagnose and localize the stiffness regions on the liver soft tissue by fusion of MRE with MRI or CT.

Impact of material homogeneity assumption on cortical stiffness estimates by MR elastography

PURPOSE

Inversion algorithms used to convert acquired MR elastography wave data into material property estimates often assume that the underlying materials are locally homogeneous. Here we evaluate the impact of that assumption on stiffness estimates in gray-matter regions of interest in brain MR elastography.

METHODS

We describe an updated neural network inversion framework using finite-difference model-derived data to train convolutional neural network inversion algorithms. Neural network inversions trained on homogeneous simulations (homogeneous learned inversions [HLIs]) or inhomogeneous simulations (inhomogeneous learned inversions [ILIs]) are generated with a variety of kernel sizes. These inversions are evaluated in a brain MR elastography simulation experiment and in vivo in a test-retest repeatability experiment including 10 healthy volunteers.

RESULTS

In simulation and in vivo, HLI and ILI with small kernels produce similar results. As kernel size increases, the assumption of homogeneity has a larger effect, and HLI and ILI stiffness estimates show larger differences. At each inversion's optimal kernel size in simulation (7 × 7 × 7 for HLI, 11 × 11 × 11 for ILI), ILI is more sensitive to true changes in stiffness in gray-matter regions of interest in simulation. In vivo, there is no difference in the region-level repeatability of stiffness estimates between the inversions, although ILI appears to better maintain the stiffness map structure as kernel size increases, while decreasing the spatial variance in stiffness estimates.

CONCLUSIONS

This study suggests that inhomogeneous inversions provide small but significant benefits even when large stiffness gradients are absent.