72 citations · 142 across the 6 of their papers we have counts for
6 papers
Stochastic Optimization of 3D Non-Cartesian Sampling Trajectory (SNOPY)
Guanhua Wang, Jon-Fredrik Nielsen, Jeffrey A. Fessler +1
Optimizing 3D k-space sampling trajectories for efficient MRI is important yet challenging. This work proposes a generalized framework for optimizing 3D non-Cartesian sampling patt…
Off-resonance artifact correction for magnetic resonance imaging: a review
Melissa W. Haskell, Jon-Fredrik Nielsen, Douglas C. Noll
In magnetic resonance imaging (MRI), inhomogeneity in the main magnetic field used for imaging, referred to as off-resonance, can lead to image artifacts ranging from mild to sever…
B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRI
Guanhua Wang, Tianrui Luo, Jon-Fredrik Nielsen +2
Optimizing k-space sampling trajectories is a promising yet challenging topic for fast magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method and…
Joint Design of RF and gradient waveforms via auto-differentiation for 3D tailored excitation in MRI
Tianrui Luo, Douglas C. Noll, Jeffrey A. Fessler +1
This paper proposes a new method for joint design of radiofrequency (RF) and gradient waveforms in Magnetic Resonance Imaging (MRI), and applies it to the design of 3D spatially ta…
Fast, Precise Myelin Water Quantification using DESS MRI and Kernel Learning
Gopal Nataraj, Jon-Fredrik Nielsen, Mingjie Gao +1
Purpose: To investigate the feasibility of myelin water content quantification using fast dual-echo steady-state (DESS) scans and machine learning with kernels. Methods: We optimiz…
Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels
Gopal Nataraj, Jon-Fredrik Nielsen, Clayton Scott +1
This paper introduces a fast, general method for dictionary-free parameter estimation in quantitative magnetic resonance imaging (QMRI) via regression with kernels (PERK). PERK fir…