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20192021
most citedQuantitative Susceptibility Inversion Through Parcellated Multiresolution Neural Networks and K-Space Substitution

6 citations · 12 across the 6 of their papers we have counts for

collaborators

6 papers

physics.med-ph2021

Unconstrained Kinematic MRI Tracking of Wrist Carpal Bones

Mohammad Zarenia, Volkan Emre Arpinar, Andrew S. Nencka +2

In this preliminary study technical methodology for kinematic tracking and profiling of wrist carpal bones during unconstrained movements is explored. Heavily under-sampled and fat…

physics.med-ph2021★ 3 cited

Diffusion propagator metrics are biased when simultaneous multi-slice acceleration is used

L. Tugan Muftuler, Andrew S. Nencka, Kevin M. Koch

Advanced diffusion MRI models are being explored to study the complex microstructure of the brain with higher accuracy. However, these techniques require long acquisition times. Si…

eess.IV2020★ 1 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-ph2019★ 6 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-ph2019★ 2 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…