Zobrazeno 1 - 10
of 122
pro vyhledávání: '"Cho, JaeJin"'
Autor:
Jun, Yohan, Liu, Qiang, Gong, Ting, Cho, Jaejin, Fujita, Shohei, Yong, Xingwang, Huang, Susie Y, Ning, Lipeng, Yendiki, Anastasia, Rathi, Yogesh, Bilgic, Berkin
Purpose: To develop and evaluate a new pulse sequence for highly accelerated distortion-free diffusion MRI (dMRI) by inserting an additional echo without prolonging TR, when generalized slice dithered enhanced resolution (gSlider) radiofrequency enco
Externí odkaz:
http://arxiv.org/abs/2409.07375
Typical quantitative MRI (qMRI) methods estimate parameter maps after image reconstructing, which is prone to biases and error propagation. We propose a Nonlinear Conjugate Gradient (NLCG) optimizer for model-based T2/T1 estimation, which incorporate
Externí odkaz:
http://arxiv.org/abs/2401.12004
Autor:
Yarach, Uten, Chatnuntawech, Itthi, Liao, Congyu, Teerapittayanon, Surat, Iyer, Siddharth Srinivasan, Kim, Tae Hyung, Haldar, Justin, Cho, Jaejin, Bilgic, Berkin, Hu, Yuxin, Hargreaves, Brian, Setsompop, Kawin
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* blurring. BUDA-cEPI reconstruction with S-based low-rank modeling of local
Externí odkaz:
http://arxiv.org/abs/2310.15939
Autor:
Srinivasa, Rakshith Sharma, Cho, Jaejin, Yang, Chouchang, Saidutta, Yashas Malur, Lee, Ching-Hua, Shen, Yilin, Jin, Hongxia
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 pairwise data. The learnt models in the latter domain can then be used f
Externí odkaz:
http://arxiv.org/abs/2309.14580
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 magnetic field inhomogeneity-related artifacts and blurring induced by T2-
Externí odkaz:
http://arxiv.org/abs/2308.05103
Autor:
Jun, Yohan, Arefeen, Yamin, Cho, Jaejin, Fujita, Shohei, Wang, Xiaoqing, Grant, P. Ellen, Gagoski, Borjan, Jaimes, Camilo, Gee, Michael S., Bilgic, Berkin
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-series images using a low-rank subspace method, which enables accurate an
Externí odkaz:
http://arxiv.org/abs/2307.01410
Autor:
Jun, Yohan, Cho, Jaejin, Wang, Xiaoqing, Gee, Michael, Grant, P. Ellen, Bilgic, Berkin, Gagoski, Borjan
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 interleaved Look-Locker acquisition sequence with T2 preparation pulse (
Externí odkaz:
http://arxiv.org/abs/2302.14240
Autor:
Chen, Zhifeng, Liao, Congyu, Cao, Xiaozhi, Poser, Benedikt A., Xu, Zhongbiao, Lo, Wei-Ching, Wen, Manyi, Cho, Jaejin, Tian, Qiyuan, Wang, Yaohui, Feng, Yanqiu, Xia, Ling, Chen, Wufan, Liu, Feng, Bilgic, Berkin
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 as quantitative T2* mapping. Methods: 3D-Blip-Up and -Down Acquisition
Externí odkaz:
http://arxiv.org/abs/2212.00687
Autor:
Cho, Jaejin, Gagoski, Borjan, Kim, Tae Hyung, Wang, Fuyixue, Splitthoff, Daniel Nico, Lo, Wei-Ching, Liu, Wei, Polak, Daniel, Cauley, Stephen, Setsompop, Kawin, Grant, P. Ellen, Bilgic, Berkin
Purpose: Volumetric, high-resolution, quantitative mapping of brain tissue relaxation properties is hindered by long acquisition times and signal-to-noise (SNR) challenges. This study, for the first time, combines the time-efficient wave-CAIPI readou
Externí odkaz:
http://arxiv.org/abs/2211.04426
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 utterance-level speech representation can be used in speech applications tha
Externí odkaz:
http://arxiv.org/abs/2208.05445