activity
20182020
most citedDeep Learning Methods for Parallel Magnetic Resonance Image Reconstruction

37 citations · 39 across the 2 of their papers we have counts for

collaborators

9 papers

eess.IV20202 cited

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…

physics.med-ph2020

Magnetic-resonance-based electrical property mapping using Global Maxwell Tomography with an 8-channel head coil at 7 Tesla: a simulation study

Ilias I. Giannakopoulos, José E. C. Serrallés, Luca Daniel +4

Objective: Global Maxwell Tomography (GMT) is a recently introduced volumetric technique for noninvasive estimation of electrical properties (EP) from magnetic resonance measuremen…

eess.IV2020

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…

eess.IV2019

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…

eess.IV2019

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…

eess.SP201937 cited

Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction

Florian Knoll, Kerstin Hammernik, Chi Zhang +4

Following the success of deep learning in a wide range of applications, neural network-based machine learning techniques have received interest as a means of accelerating magnetic…