45 citations · 92 across the 4 of their papers we have counts for
4 papers
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…
Accurate parameter estimation using scan-specific unsupervised deep learning for relaxometry and MR fingerprinting
Mengze Gao, Huihui Ye, Tae Hyung Kim +3
We propose an unsupervised convolutional neural network (CNN) for relaxation parameter estimation. This network incorporates signal relaxation and Bloch simulations while taking ad…
LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive MRI Reconstruction in k-Space
Tae Hyung Kim, Pratyush Garg, Justin P. Haldar
We propose and evaluate a new MRI reconstruction method named LORAKI that trains an autocalibrated scan-specific recurrent neural network (RNN) to recover missing k-space data. Met…
Navigator-free EPI Ghost Correction with Structured Low-Rank Matrix Models: New Theory and Methods
Rodrigo A. Lobos, Tae Hyung Kim, W. Scott Hoge +1
Structured low-rank matrix models have previously been introduced to enable calibrationless MR image reconstruction from sub-Nyquist data, and such ideas have recently been extende…