4,441 research outputs found
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 . 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 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
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