Deep Learning for Accelerated Ultrasound Imaging

In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an increasing demand to reconstruct high quality images from limited number of data. However, the existing solutions require either hardware changes or computationally expansive algorithms. To overcome these limitations, here we propose a novel deep learning approach that interpolates the missing RF data by utilizing the sparsity of the RF data in the Fourier domain. Extensive experimental results from sub-sampled RF data from a real US system confirmed that the proposed method can effectively reduce the data rate without sacrificing the image quality.Comment: Invited paper for ICASSP 2018 Special Session for "Machine Learning in Medical Imaging: from Measurement to Diagnosis

Integration of waveguide-type wavelength demultiplexingphotodetectors by the selective intermixing of an InGaAs-InGaAsPquantum-well structure

Abstract—Using the selective intermixing of an InGaAs–In- GaAsP multiquantum-well (MQW) structure, a wavelength demultiplexing photodetector which can demultiplex two widely separated wavelengths was fabricated. An InGaAs–InGaAsP MQW with a u-InP cladding layer and a u-InGaAs cap layer, grown by metal organic chemical vapor deposition was used. Selective area intermixing of the InGaAs–InGaAsP MQW structure was done by a rapid thermal annealing after the deposition and patterning of the SiO2 dielectric layer on the InGaAs cap layer. The integrated structure consists of shorter and longer wavelength sections, separated by an absorber section. Shorter wavelength and absorber sections were intermixed with the SiO2 dielectric layer. At a wavelength of 1477 nm, the output photocurrent ratio was enhanced as the length of the absorber region increased and a ratio of over 30 dB was observed, while at a wavelength of 1561 nm, an output photocurrent ratio of 18.9 dB was observed

A Large Bandgap Shift in InGaAs(P)/InP Multi-Quantum Well Structure Obtained by Impurity-Free Vacancy Diffusion Using SiO2 Capping and its Application to Photodetectors

In this paper, we have investigated the bandgap tuning in the InGaAs (P)/ InP multiquantum well (MQW) structure obtained by impurity-free vacancy diffusion (IFVD) using low temperature photoluminescence (PL). The MQW intermixing was performed in a rapid thermal annealer (RTA) using the dielectric capping materials, Si02 and SiNX. The Si02 capping was successfully used with InGaAs cap layer to cause a large bandgap tuning effect in the InGaAs/InP MQW material. The blue shift of bandgap energy after RTA treatment was as much as 185 and 230 meV at 750 t and 850 t, respectively, with its value controllable using annealing time and temperature. Samples with Si02-InP or SiN-InGaAs cap layer combinations, on the other hand, did not show any significant energy shifts. The absorption spectra taken from the same samples confimed the energy shifts obtained using PL. The process developed can be readily applied to fabrication of photodetectors that are sensitive to wavelength and/or polarization.This work was fmancially supported in part by OERC(Opto-Electronic Research Center) through the grant # 97K3-0809- 02-06-1 and by the SPRC (Semiconductor Physics Research Center) of Korea. The authors thank U. H. Lee and Prof. D. Lee of Chung Nam National Univ. for their help with the absorption measurement

Characteristics of Intermixed InGaAs/InGaAsP Multi-Quantum-Well Structure

The intermixing of a InGaAs/InGaAsP multi-quantum-well (MQW) structure induced by SiO2 dielectric cap layer deposition and heat treatment was investigated. Photoluminescence experiments reveal a large blue shift of the effective bandgap for the intermixed quantum well. By secondary ion mass spectroscopy, the group III and V elements of a MQW are found to interdiffuse at a similar rate after the intermixing process. An optical waveguide was fabricated using intermixed material where a propagation loss reduction of 450 dB was recorded at a wavelength close to the original bandgap wavelength

TUDCA-Treated Mesenchymal Stem Cells Protect against ER Stress in the Hippocampus of a Murine Chronic Kidney Disease Model

Chronic kidney disease (CKD) leads to the loss of kidney function, as well as the dysfunction of several other organs due to the release of uremic toxins into the system. In a murine CKD model, reactive oxygen species (ROS) generation and endoplasmic reticulum (ER) stress are increased in the hippocampus. Mesenchymal stem cells (MSCs) are one of the candidates for cell-based therapy for CKD; however severe pathophysiological conditions can decrease their therapeutic potential. To address these issues, we established tauroursodeoxycholic acid (TUDCA)-treated MSCs using MSCs isolated from patients with CKD (CKD-hMSCs) and assessed the survival and ROS generation of neural cell line SH-SY5Y cells by co-culturing with TUDCA-treated CKD-hMSCs. In the presence of the uremic toxin P-cresol, the death of SH-SY5Y cells was induced by ROS-mediated ER stress. Co-culture with TUDCA-treated CKD-hMSCs increased anti-oxidant enzyme activities in SH-SY5Y cells through the upregulation of the cellular prion protein (PrPC) expression. Upregulated PrPC expression in SH-SY5Y cells protected against CKD-mediated ER stress and apoptosis. In an adenine-induced murine CKD model, injection with TUDCA-treated CKD-hMSCs suppressed ROS generation and ER stress in the hippocampus. These results indicate that TUDCA-treated CKD-hMSCs prevent the CKD-mediated cell death of SH-SY5Y cells by inhibiting ER stress. Our study suggests that treatment with TUDCA could be a powerful strategy for developing autologous MSC-based therapeutics for patients with CKD, and that PrPC might be a pivotal target for protecting neural cells from CKD-mediated ER stress

Enhanced Convolutional Neural Network for In Situ AUV Thruster Health Monitoring Using Acoustic Signals

As the demand for ocean exploration increases, studies are being actively conducted on autonomous underwater vehicles (AUVs) that can efficiently perform various missions. To successfully perform long-term, wide-ranging missions, it is necessary to apply fault diagnosis technology to AUVs. In this study, a system that can monitor the health of in situ AUV thrusters using a convolutional neural network (CNN) was developed. As input data, an acoustic signal that comprehensively contains the mechanical and hydrodynamic information of the AUV thruster was adopted. The acoustic signal was pre-processed into two-dimensional data through continuous wavelet transform. The neural network was trained with three different pre-processing methods and the accuracy was compared. The decibel scale was more effective than the linear scale, and the normalized decibel scale was more effective than the decibel scale. Through tests on off-training conditions that deviate from the neural network learning condition, the developed system properly recognized the distribution characteristics of noise sources even when the operating speed and the thruster rotation speed changed, and correctly diagnosed the state of the thruster. These results showed that the acoustic signal-based CNN can be effectively used for monitoring the health of the AUV's thrusters.N

The characteristics of Zn-doped InP using spin-on dopant as a diffusion source

Zn diffusion into InP was carried out ex-situ using a spin-on dopant as a diffusion source. The characteristics of Zn-doped InP are analyzed using low-temperature photoluminescence (PL), differential Hall measurement, and secondary ion mass spectrometry (SIMS). Dopant activation of Zn is close to 100% using this method. Band-to-acceptor (B-A) transition peak is dominant in PL, which is a characteristic usually found in in-situ doping. This evidence along with an activation energy of 0.5 eV show that the diffusion is substitutional rather than interstitial.This work was supported by the Brain Korea 21 Project in 2000

Area selectivity of InGaAsP-InP multiquantum-well intermixing by impurity-free vacancy diffusion

Area selectivity of bandgap tuning in the InGaAsPInP multiquantum-well structure has been investigated using low temperature photoluminescence (PL). The bandgap blue-shift in the intermixed region was as much as 170 meV for a rapid thermal annealer anneal of 30 s at 850 C, and was controllable using annealing temperature and time. From samples with SiO2 stripe patterns, clearly separated PL peaks were observed centered at 0.95 and 1.08 eV, each representing signals originating from the dielectric capped and exposed areas, respectively. In samples with stripes intervals less than 6 m, PL signals did not separate, but formed one broad spectrum due to lateral diffusion. The lateral diffusion was found less than 3.0 m.This work was supported in part by Korea Telecom Switching Technology Research under Grant 970124 and in part by the Institute of Information Technology Assessment under Grant 97-NF-02-14-a-01