11 citations · 27 across the 6 of their papers we have counts for
11 papers
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…