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19982025
most citedRandomness enhances cooperation: a resonance type phenomenon in evolutionary games

176 citations

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9 papers · 1 filter

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…

physics.med-ph2020★ 4 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…

physics.med-ph2020★ 5 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-ph2020★ 1 cited

Model-based Learning for Quantitative Susceptibility Mapping

Juan Liu, Kevin M. Koch

Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that estimates magnetic susceptibility of tissue from Larmor frequency offset measurements…

physics.med-ph2019★ 4 cited

Meta-QSM: An Image-Resolution-Arbitrary Network for QSM Reconstruction

Juan Liu, Kevin M. Koch

Quantitative Susceptibility Mapping (QSM) can estimate the underlying tissue magnetic susceptibility and reveal pathology. Current deep-learning-based approaches to solve the QSM i…

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

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…

physics.med-ph2018★ 15 cited

Simultaneous Motion Monitoring and Truth-In-Delivery Analysis Imaging Framework for MR-guided Radiotherapy

Nikolai J. Mickevicius, Xinfeng Chen, Zachary Boyd +3

Intrafraction motion can play a pivotal role in the success of abdominal and thoracic radiation therapy. Hybrid magnetic resonance-guided radiotherapy systems have the potential to…