19 citations · 128 across the 24 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…