37 citations · 42 across the 4 of their papers we have counts for
6 papers · 1 filter
DeepEMC-T2 Mapping: Deep Learning-Enabled T2 Mapping Based on Echo Modulation Curve Modeling
Haoyang Pei, Timothy M. Shepherd, Yao Wang +4
Purpose: Echo modulation curve (EMC) modeling can provide accurate and reproducible quantification of T2 relaxation times. The standard EMC-T2 mapping framework, however, requires…
On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis
Divyam Madaan, Daniel Sodickson, Kyunghyun Cho +1
Magnetic Resonance Imaging (MRI) is considered the gold standard of medical imaging because of the excellent soft-tissue contrast exhibited in the images reconstructed by the MRI p…
Differences between human and machine perception in medical diagnosis
Taro Makino, Stanislaw Jastrzebski, Witold Oleszkiewicz +18
Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since their performance can be severely degraded by dataset shifts to which h…
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Florian Knoll, Tullie Murrell, Anuroop Sriram +8
Purpose: To advance research in the field of machine learning for MR image reconstruction with an open challenge. Methods: We provided participants with a dataset of raw k-space da…
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction
Anuroop Sriram, Jure Zbontar, Tullie Murrell +3
Magnetic Resonance Image (MRI) acquisition is an inherently slow process which has spurred the development of two different acceleration methods: acquiring multiple correlated samp…
Training a Neural Network for Gibbs and Noise Removal in Diffusion MRI
Matthew J. Muckley, Benjamin Ades-Aron, Antonios Papaioannou +7
We develop and evaluate a neural network-based method for Gibbs artifact and noise removal. A convolutional neural network (CNN) was designed for artifact removal in diffusion-weig…