Fluctuation Relation for Heat

We present a fluctuation relation for heat dissipation in a nonequilibrium system. A nonequilibrium work is known to obey the fluctuation theorem in any time interval $t$. A heat, which differs from a work by an energy change, is shown to satisfy a modified fluctuation relation. Modification is brought by correlation between a heat and an energy change during nonequilibrium processes whose effect may not be negligible even in the $t\to\infty$ limit. The fluctuation relation is derived for overdamped Langevin equation systems, and tested in a linear diffusion system.Comment: 5 pages, 3 figur

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

Development of pulse diagnostic devices in Korea

AbstractIn Korean medicine, pulse diagnosis is one of the important methods for determining the health status of a patient. For over 40 years, electromechanical pulse diagnostic devices have been developed to objectify and quantify pulse diagnoses. In this paper, we review previous research and development for pulse diagnostic devices according to various fields of study: demand analysis and current phase, literature studies, sensors, actuators, systems, physical quantity studies, clinical studies, and the U-health system. We point out some confusing issues that have been naively accepted without strict verification: original pressure pulse waveform and derivative pressure pulse waveform, pressure signals and other signal types, and minutely controlled pressure exertion issues. We then consider some technical and clinical issues to achieve the development of a pulse diagnostic device that is appropriate both technically and in terms of Korean medicine. We hope to show the history of pulse diagnostic device research in Korea and propose a proper method to research and develop these devices