Theory of magnetoelastic resonance in a mono-axial chiral helimagnet

We study magnetoelastic resonance phenomena in a mono-axial chiral helimagnet belonging to hexagonal crystal class. By computing the spectrum of coupled elastic wave and spin wave, it is demonstrated how hybridization occurs depending on their chirality. Specific features of the magnetoelastic resonance are discussed for the conical phase and the soliton lattice phase stabilized in the mono-axial chiral helimagnet. The former phase exhibits appreciable non-reciprocity of the spectrum, the latter is characterized by a multi-resonance behavior. We propose that the non-reciprocal spin wave around the forced-ferromagnetic state has potential capability to convert the linearly polarized elastic wave to circularly polarized one with the chirality opposite to the spin wave chirality.Comment: 12 pages, 5 figures, Accepted in Phys. Rev.

Transient Anomaly Imaging in Visco-Elastic Media Obeying a Frequency Power-Law

In this work, we consider the problem of reconstructing a small anomaly in a viscoelastic medium from wave-field measurements. We choose Szabo's model to describe the viscoelastic properties of the medium. Expressing the ideal elastic field without any viscous effect in terms of the measured field in a viscous medium, we generalize the imaging procedures, such as time reversal, Kirchhoff Imaging and Back propagation, for an ideal medium to detect an anomaly in a visco-elastic medium from wave-field measurements

Deep Learning for High Speed Optical Coherence Elastography

Mechanical properties of tissue provide valuable information for identifying lesions. One approach to obtain quantitative estimates of elastic properties is shear wave elastography with optical coherence elastography (OCE). However, given the shear wave velocity, it is still difficult to estimate elastic properties. Hence, we propose deep learning to directly predict elastic tissue properties from OCE data. We acquire 2D images with a frame rate of 30 kHz and use convolutional neural networks to predict gelatin concentration, which we use as a surrogate for tissue elasticity. We compare our deep learning approach to predictions from conventional regression models, using the shear wave velocity as a feature. Mean absolut prediction errors for the conventional approaches range from 1.32$\pm$0.98 p.p. to 1.57$\pm$1.30 p.p. whereas we report an error of 0.90$\pm$0.84 p.p for the convolutional neural network with 3D spatio-temporal input. Our results indicate that deep learning on spatio-temporal data outperforms elastography based on explicit shear wave velocity estimation.Comment: Accepted at IEEE International Symposium on Biomedical Imaging 202