Phenomenological Implications of the Topflavor Model

We explore phenomenologies of the topflavour model for the LEP experiment at $m_{_Z}$ scale and the atomic parity violation (APV) experiment in the $C_s$ atoms at low energies. Implications of the model on the $Z$ peak data are studied in terms of the precision variables $\epsilon_i$'s. We find that the LEP data give more stringent constraints on the model parameters than the APV data.Comment: 23 pages (including 5 .eps figs), ReVTeX, the 1st revised version, to appear in Phys. Lett.

Bremsstrahlung Radiation At a Vacuum Bubble Wall

When charged particles collide with a vacuum bubble, they can radiate strong electromagnetic waves due to rapid deceleration. Owing to the energy loss of the particles by this bremsstrahlung radiation, there is a non-negligible damping pressure acting on the bubble wall even when thermal equilibrium is maintained. In the non-relativistic region, this pressure is proportional to the velocity of the wall and could have influenced the bubble dynamics in the early universe.Comment: 6 pages, 2 figures, revtex, to appear in JKP

k-Space Deep Learning for Reference-free EPI Ghost Correction

Nyquist ghost artifacts in EPI are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field MRI due to the non-linear and time-varying local magnetic field changes. Recently, it was shown that the problem of ghost correction can be reformulated as k-space interpolation problem that can be solved using structured low-rank Hankel matrix approaches. Another recent work showed that data driven Hankel matrix decomposition can be reformulated to exhibit similar structures as deep convolutional neural network. By synergistically combining these findings, we propose a k-space deep learning approach that immediately corrects the phase mismatch without a reference scan in both accelerated and non-accelerated EPI acquisitions. To take advantage of the even and odd-phase directional redundancy, the k-space data is divided into two channels configured with even and odd phase encodings. The redundancies between coils are also exploited by stacking the multi-coil k-space data into additional input channels. Then, our k-space ghost correction network is trained to learn the interpolation kernel to estimate the missing virtual k-space data. For the accelerated EPI data, the same neural network is trained to directly estimate the interpolation kernels for missing k-space data from both ghost and subsampling. Reconstruction results using 3T and 7T in-vivo data showed that the proposed method outperformed the image quality compared to the existing methods, and the computing time is much faster.The proposed k-space deep learning for EPI ghost correction is highly robust and fast, and can be combined with acceleration, so that it can be used as a promising correction tool for high-field MRI without changing the current acquisition protocol.Comment: To appear in Magnetic Resonance in Medicin