Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Jun, Yohan"'
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
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
Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fie
Externí odkaz:
http://arxiv.org/abs/2305.11012
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
Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance when in fully supervised condition. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical ima
Externí odkaz:
http://arxiv.org/abs/2203.16557
With the advances of deep learning, many medical image segmentation studies achieve human-level performance when in fully supervised condition. However, it is extremely expensive to acquire annotation on every data in medical fields, especially on ma
Externí odkaz:
http://arxiv.org/abs/2109.10674
Autor:
Muckley, Matthew J., Riemenschneider, Bruno, Radmanesh, Alireza, Kim, Sunwoo, Jeong, Geunu, Ko, Jingyu, Jun, Yohan, Shin, Hyungseob, Hwang, Dosik, Mostapha, Mahmoud, Arberet, Simon, Nickel, Dominik, Ramzi, Zaccharie, Ciuciu, Philippe, Starck, Jean-Luc, Teuwen, Jonas, Karkalousos, Dimitrios, Zhang, Chaoping, Sriram, Anuroop, Huang, Zhengnan, Yakubova, Nafissa, Lui, Yvonne, Knoll, Florian
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided partici
Externí odkaz:
http://arxiv.org/abs/2012.06318
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