most citedQuantitative Susceptibility Inversion Through Parcellated Multiresolution Neural Networks and K-Space Substitution

6 citations · 18 across the 6 of their papers we have counts for

collaborators

6 papers

physics.med-ph20204 cited

Reducing the Dimensionality of Optimal Experiment Design for Magnetic Resonance Fingerprinting

Nikolai J. Mickevicius, Andrew S. Nencka, Eric S. Paulson

Nuclear magnetic resonance signal dynamics as described by the Bloch equations are highly complex and often are without closed form solutions. This is especially the case for quant…

eess.IV20201 cited

Split Slice Training Augmentation and Hyperparameter Tuning of RAKI Networks for Simultaneous Multi-Slice Reconstruction

Andrew S. Nencka, PhD, Volkan E. Arpinar +7

Split-slice augmentation for simultaneous multi-slice RAKI networks positively impacts network performance. Hyperparameter tuning of such reconstruction networks can lead to furthe…

physics.med-ph20205 cited

Optimization of hyperparameters for SMS reconstruction

L. Tugan Muftuler, Volkan Emre Arpinar, Kevin Koch +5

Simultaneous multi-slice (SMS) imaging accelerates MRI data acquisition by exciting multiple image slices simultaneously. Overlapping slices are then separated using a mathematical…

physics.med-ph20196 cited

Quantitative Susceptibility Inversion Through Parcellated Multiresolution Neural Networks and K-Space Substitution

Juan Liu, Andrew S. Nencka, L. Tugan Muftuler +3

Purpose: Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging inverse problem driven by poor conditioning of the field to susceptibility transformation. State-…

physics.med-ph20192 cited

Application of a k-Space Interpolating Artificial Neural Network to In-Plane Accelerated Simultaneous Multislice Imaging

Nikolai J. Mickevicius, Eric S. Paulson, L. Tugan Muftuler +1

Purpose: The goal of this work is to extend the capabilities of RAKI, a k-space interpolating neural network, to reconstruct high-quality images from in-plane accelerated simultane…

physics.med-ph2019

Build-A-FLAIR: Synthetic T2-FLAIR Contrast Generation through Physics Informed Deep Learning

Andrew S. Nencka, Andrew Klein, Kevin M. Koch +7

Purpose: Magnetic resonance imaging (MRI) exams include multiple series with varying contrast and redundant information. For instance, T2-FLAIR contrast is based upon tissue T2 dec…