22 citations · 76 across the 16 of their papers we have counts for
5 papers · 1 filter
Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic results
Efrat Shimron, Jonathan I. Tamir, Ke Wang +1
While open databases are an important resource in the Deep Learning (DL) era, they are sometimes used "off-label": data published for one task are used for training algorithms for…
High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Loss
Ke Wang, Jonathan I Tamir, Alfredo De Goyeneche +4
Purpose: To improve reconstruction fidelity of fine structures and textures in deep learning (DL) based reconstructions. Methods: A novel patch-based Unsupervised Feature Loss (UFL…
eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction
Heng Yu, Zijing Dong, Yamin Arefeen +3
RAKI can perform database-free MRI reconstruction by training models using only auto-calibration signal (ACS) from each specific scan. As it trains a separate model for each indivi…
Memory-efficient Learning for High-Dimensional MRI Reconstruction
Ke Wang, Michael Kellman, Christopher M. Sandino +5
Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI). Similar to compressed sensing, DL can…
Wave-encoding and Shuffling Enables Rapid Time Resolved Structural Imaging
Siddharth Iyer, Daniel Polak, Congyu Liao +6
T2-Shuffling reconstructs multiple sharp T2-weighted images from a single volumetric fast spin-echo (3D-FSE) scan. Wave-CAIPI is a parallel imaging technique that achieves good rec…