Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

W. Thomas Dixon

dblp:46/6563 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%
Computer graphics and multimedia
1 paper
Image and video processing · 50% Audio and music processing · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics › medical imaging
magnetic resonance imaging
0.112005
Single Image Phase-Based MRI Fat Suppression Expectation Maximization Algorithm · CVPR (2) 2005
Medical and health informatics
medical imaging
0.112005
Single Image Phase-Based MRI Fat Suppression Expectation Maximization Algorithm · CVPR (2) 2005
Image and video processing
image reconstruction
0.012005
Single Image Phase-Based MRI Fat Suppression Expectation Maximization Algorithm · CVPR (2) 2005
Audio and music processing › acoustic signal processing › audio signal reconstruction
phase reconstruction
0.012005
Single Image Phase-Based MRI Fat Suppression Expectation Maximization Algorithm · CVPR (2) 2005

Methods — techniques the papers use, named apart from their topics

phase-based estimation · 0.1expectation-maximization · 0.1
YearPublicationVenuePosition
2005 Single Image Phase-Based MRI Fat Suppression Expectation Maximization Algorithm
abstract
MRI fat suppression techniques are primarily approached by variations of the phase based methods. Multipoint methods dominate the literature, specifically methods that require the acquisition of three images at different echo times. These methods estimate the contribution of fat and water signals by solving a set of equations using echos acquired with the relative phase of fat and water at -/spl pi/, 0, /spl pi/. We present a novel approach to fat suppression that requires only a single image. Using an expectation maximization algorithm on a gradient echo image with the relative phase of fat and water at approximately /spl pi//2, the pixel composition and inhomogeneity of the magnetic field are estimated. Fat suppressed images are generated from estimated inhomogeneity. Our method does not need custom pulse sequences. Results from mathematical phantoms and volunteer images are reported.
Daniel J. Blezek, W. Thomas Dixon, Paritosh J. Dhawale
CVPR (2)2