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Improving magnetic resonance resolution with supervised learning
Amod Jog
, Aaron Carass
,
Jerry L. Prince
Whiting School of Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
32
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Scopus citations
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Keyphrases
T1-weighted
100%
Magnetic Resonance
100%
Supervised Learning
100%
T2-weighted Image
100%
Cardiac Magnetic Resonance Imaging (cMRI)
50%
Hallucinations
50%
Phantom
50%
Image Reconstruction
50%
Scan Time
50%
Super-resolution Algorithm
50%
High-resolution Data
50%
Slice Thickness
50%
Regression-based Method
50%
In-plane Resolution
50%
Ongoing Improvement
50%
Weighted Data
50%
Magnetization-prepared Rapid Gradient Echo
50%
Supervised Learning Algorithms
50%
Rousseau
50%
Longest Relaxation Time
50%
Computer Science
Supervised Learning
100%
super resolution
50%
Image Reconstruction
50%
Learning Algorithm
50%
Fundamental Problem
50%
Relaxation Time
50%
Physics
Magnetic Resonance
100%
Supervised Learning
100%
Relaxation Time
50%
High Resolution
50%
Image Reconstruction
50%
Earth and Planetary Sciences
Supervised Learning
100%
State of the Art
50%
Image Reconstruction
50%
Relaxation Time
50%
Neuroscience
Magnetic Resonance Imaging
100%
Hallucination
100%