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

MOTIVATING BUSINESS MAJOR STUDENTS TO LEARN COMPUTER PROGRAMMING – A CASE STUDY

Learning to program is viewed as difficult by many students. How to motivate and engage business major students to learn programming is challenging. In this paper, we report our attempt to teach business students programming. In our teaching method, first we orient students with the business and managerial aspect of programming by posing six original questions. By discussing these six questions, students gain appreciation of the high level responsibility of participating, contributing, assessing, and managing Information Systems (IS) as business professionals. Second, we adopt a Let Us Do It Together approach to deliver hands on labs to teach the technical aspect of programming. Inspired by the constructivism learning theory and the learning by doing and experimentation idea, our Let Us Do It Together approach mitigates students’ anxiety and fear of programming. Overall, our teaching approach seems to enhance students’ interest in programming

Biomarkers Predicting Isocyanate-Induced Asthma

Three diisocyanates can cause occupational asthma (OA): toluene diisocyanate (TDI), 4,4 diphenylmethane diisocyanate (MDI), and 1,6-hexamethylene diisocyanate (HDI). We analyzed potential biomarkers of isocyanate-induced OA, based on investigated immunologic, genetic, neurogenic, and protein markers, because there is no serological testing method. The prevalence of serum IgG to cytokeratin (CK)18 and CK19 in TDI-OA was significantly higher than in controls, although the prevalence of these antibodies was too low for them to be used as biomarkers. Another candidate biomarker was serum IgG to tissue transglutaminase (tTG), because the prevalence of serum specific IgG to tTG was significantly higher in patients with TDI-OA than in controls. The human leukocyte antigen (HLA) DRB1*1501-DQB1*0602-DPB1*0501 haplotype may be used as a genetic marker for TDI-OA in Koreans via enhanced specific IgE sensitization in exposed subjects. The genetic polymorphisms of catenin alpha 3, alpha-T catenin (CTNNA3) were significantly associated with TDI-OA. Additionally, examining the neurokinin 2 receptor (NK2R) 7853G>A and 11424 G>A polymorphisms, the NK2R 7853GG genotype had higher serum vascular endothelial growth factor (VEGF) levels than the GA or AA genotypes among Korean workers exposed to TDI. To identify new serologic markers using a proteomic approach, differentially expressed proteins between subjects with MDI-OA and asymptomatic exposed controls in a Korean population showed that the optimal serum cutoff levels were 69.8 ng/mL for ferritin and 2.5 µg/mL for transferrin. When these two parameters were combined, the sensitivity was 71.4% and the specificity was 85.7%. The serum cytokine matrix metalloproteinase-9 (MMP-9) level is a useful biomarker for identifying cases of TDI-OA among exposed workers. Despite these possible biomarkers, more effort should be focused on developing early diagnostic biomarkers using a comprehensive approach based on the pathogenic mechanisms of isocyanate-induced OA

Improved Noisy Student Training for Automatic Speech Recognition

Recently, a semi-supervised learning method known as "noisy student training" has been shown to improve image classification performance of deep networks significantly. Noisy student training is an iterative self-training method that leverages augmentation to improve network performance. In this work, we adapt and improve noisy student training for automatic speech recognition, employing (adaptive) SpecAugment as the augmentation method. We find effective methods to filter, balance and augment the data generated in between self-training iterations. By doing so, we are able to obtain word error rates (WERs) 4.2%/8.6% on the clean/noisy LibriSpeech test sets by only using the clean 100h subset of LibriSpeech as the supervised set and the rest (860h) as the unlabeled set. Furthermore, we are able to achieve WERs 1.7%/3.4% on the clean/noisy LibriSpeech test sets by using the unlab-60k subset of LibriLight as the unlabeled set for LibriSpeech 960h. We are thus able to improve upon the previous state-of-the-art clean/noisy test WERs achieved on LibriSpeech 100h (4.74%/12.20%) and LibriSpeech (1.9%/4.1%).Comment: 5 pages, 5 figures, 4 tables; v2: minor revisions, reference adde

Monoclinic phase in the relaxor-based piezo-/ ferroelectric Pb(Mg1/3_{1/3}Nb2/3)O3_{2/3})O_3-PbTiO3_3 system

A ferroelectric monoclinic phase of space group $Cm$ ($M_A$ type) has been discovered in 0.65Pb(Mg$_{1/3}$Nb$_{2/3})O_3$-0.35PbTiO$_3$ by means of high resolution synchrotron X-ray diffraction. It appears at room temperature in a single crystal previously poled under an electric field of 43 kV/cm applied along the pseudocubic [001] direction, in the region of the phase diagram around the morphotropic phase boundary between the rhombohedral (R3m) and the tetragonal (P4mm) phases. The monoclinic phase has lattice parameters a = 5.692 A, b = 5.679 A, c = 4.050 A and $\beta$ = $90.15^{\circ}$, with the b$_m$-axis oriented along the pseudo-cubic [110] direction . It is similar to the monoclinic phase observed in PbZr$_{1-x}$Ti$_x$O$_3$, but different from that recently found in Pb(Zn$_{1/3}$Nb$_{2/3})O_3$-PbTiO$_3$, which is of space group $Pm$ ($M_C$ type).Comment: Revised version after referees' comments. PDF file. 6 pages, 4 figures embedde