19 citations · 128 across the 24 of their papers we have counts for
24 papers
FINDER: Zero-Shot Field-Integrated Network for Distortion-free EPI Reconstruction in Diffusion MRI
Namgyu Han, Seong Dae Yun, Chaeeun Lim +7
Echo-planar imaging (EPI) remains the cornerstone of diffusion MRI, but it is prone to severe geometric distortions due to its rapid sampling scheme that renders the sequence highl…
PRIME: Phase Reversed Interleaved Multi-Echo acquisition enables highly accelerated distortion-free diffusion MRI
Yohan Jun, Qiang Liu, Ting Gong +13
Purpose: To develop and evaluate a new pulse sequence for highly accelerated distortion-free diffusion MRI (dMRI) by inserting additional echoes without prolonging TR, when general…
NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI Reconstruction
Xinrui Jiang, Yohan Jun, Jaejin Cho +3
Typical quantitative MRI (qMRI) methods estimate parameter maps in a two-step pipeline that first reconstructs images from undersampled k-space data and then performs model fitting…
Blip-Up Blip-Down Circular EPI (BUDA-cEPI) for Distortion-Free dMRI with Rapid Unrolled Deep Learning Reconstruction
Uten Yarach, Itthi Chatnuntawech, Congyu Liao +9
Purpose: We implemented the blip-up, blip-down circular echo planar imaging (BUDA-cEPI) sequence with readout and phase partial Fourier to reduced off-resonance effect and T2* blur…
CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss
Rakshith Sharma Srinivasa, Jaejin Cho, Chouchang Yang +4
This paper considers contrastive training for cross-modal 0-shot transfer wherein a pre-trained model in one modality is used for representation learning in another domain using pa…
Improved Multi-Shot Diffusion-Weighted MRI with Zero-Shot Self-Supervised Learning Reconstruction
Jaejin Cho, Yohan Jun, Xiaoqing Wang +2
Diffusion MRI is commonly performed using echo-planar imaging (EPI) due to its rapid acquisition time. However, the resolution of diffusion-weighted images is often limited by magn…
Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Rapid Multiparametric Quantitative MRI Using 3D-QALAS
Yohan Jun, Yamin Arefeen, Jaejin Cho +7
Purpose: To develop and evaluate methods for 1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) time-ser…
SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS
Yohan Jun, Jaejin Cho, Xiaoqing Wang +4
Purpose: To develop and evaluate a method for rapid estimation of multiparametric T1, T2, proton density (PD), and inversion efficiency (IE) maps from 3D-quantification using an in…
3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping
Zhifeng Chen, Congyu Liao, Xiaozhi Cao +12
Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well a…
Time-efficient, High Resolution 3T Whole Brain Quantitative Relaxometry using 3D-QALAS with Wave-CAIPI Readouts
Jaejin Cho, Borjan Gagoski, Tae Hyung Kim +9
Purpose: Volumetric, high-resolution, quantitative mapping of brain tissue relaxation properties is hindered by long acquisition times and signal-to-noise (SNR) challenges. This st…
Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech
Jaejin Cho, Jes'us Villalba, Laureano Moro-Velazquez +1
In recent studies, self-supervised pre-trained models tend to outperform supervised pre-trained models in transfer learning. In particular, self-supervised learning (SSL) of uttera…
Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations
Jaejin Cho, Raghavendra Pappagari, Piotr Żelasko +3
Considering the abundance of unlabeled speech data and the high labeling costs, unsupervised learning methods can be essential for better system development. One of the most succes…
Wave-Encoded Model-based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction
Jaejin Cho, Borjan Gagoski, Taehyung Kim +4
Purpose: To propose a wave-encoded model-based deep learning (wave-MoDL) strategy for highly accelerated 3D imaging and joint multi-contrast image reconstruction, and further exten…
The JHU submission to VoxSRC-21: Track 3
Jejin Cho, Jesus Villalba, Najim Dehak
This technical report describes Johns Hopkins University speaker recognition system submitted to Voxceleb Speaker Recognition Challenge 2021 Track 3: Self-supervised speaker verifi…
BUDA-SAGE with self-supervised denoising enables fast, distortion-free, high-resolution T2, T2*, para- and dia-magnetic susceptibility mapping
Zijing Zhang, Long Wang, Jaejin Cho +10
To rapidly obtain high resolution T2, T2* and quantitative susceptibility mapping (QSM) source separation maps with whole-brain coverage and high geometric fidelity. We propose Bli…
Highly Accelerated EPI with Wave Encoding and Multi-shot Simultaneous Multi-Slice Imaging
Jaejin Cho, Congyu Liao, Qiyuan Tian +8
We introduce wave encoded acquisition and reconstruction techniques for highly accelerated echo planar imaging (EPI) with reduced g-factor penalty and image artifacts. Wave-EPI inv…
Learning Speaker Embedding from Text-to-Speech
Jaejin Cho, Piotr Zelasko, Jesus Villalba +2
Zero-shot multi-speaker Text-to-Speech (TTS) generates target speaker voices given an input text and the corresponding speaker embedding. In this work, we investigate the effective…
Scan-specific, Parameter-free Artifact Reduction in K-space (SPARK)
Onur Beker, Congyu Liao, Jaejin Cho +3
We propose a convolutional neural network (CNN) approach that works synergistically with physics-based reconstruction methods to reduce artifacts in accelerated MRI. Given reconstr…
Deep neural networks for emotion recognition combining audio and transcripts
Jaejin Cho, Raghavendra Pappagari, Purva Kulkarni +3
In this paper, we propose to improve emotion recognition by combining acoustic information and conversation transcripts. On the one hand, an LSTM network was used to detect emotion…
Highly efficient MRI through multi-shot echo planar imaging
Congyu Liao, Xiaozhi Cao, Jaejin Cho +3
Multi-shot echo planar imaging (msEPI) is a promising approach to achieve high in-plane resolution with high sampling efficiency and low T2* blurring. However, due to the geometric…
Language model integration based on memory control for sequence to sequence speech recognition
Jaejin Cho, Shinji Watanabe, Takaaki Hori +4
In this paper, we explore several new schemes to train a seq2seq model to integrate a pre-trained LM. Our proposed fusion methods focus on the memory cell state and the hidden stat…
Transfer learning of language-independent end-to-end ASR with language model fusion
Hirofumi Inaguma, Jaejin Cho, Murali Karthick Baskar +2
This work explores better adaptation methods to low-resource languages using an external language model (LM) under the framework of transfer learning. We first build a language-ind…
Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling
Jaejin Cho, Murali Karthick Baskar, Ruizhi Li +6
Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively new direction in speech research. The approach benefits by performing model training without using lexi…
End-to-end Speech Recognition with Word-based RNN Language Models
Takaaki Hori, Jaejin Cho, Shinji Watanabe
This paper investigates the impact of word-based RNN language models (RNN-LMs) on the performance of end-to-end automatic speech recognition (ASR). In our prior work, we have propo…