A signal-detection approach for analysis of functional neuroimages

IEEE Nuclear Science Symposium and Medical Imaging Conference

Volumn 3, Issue , 2001, Pages 1394-1398

  Lukic, Ana S ^a   Wernick, Miles N ^a   Galatsanos, Nikolas P ^a   Yang, Yongyi ^a   Strother, Stephen C ^a  

^a ILLINOIS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; IMAGE RECONSTRUCTION; MAGNETIC RESONANCE IMAGING; MARKOV PROCESSES; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; MONTE CARLO METHODS; NEUROLOGY; PATTERN RECOGNITION; POSITRON EMISSION TOMOGRAPHY; THREE DIMENSIONAL COMPUTER GRAPHICS; TWO DIMENSIONAL;

ACTIVATION PATTERN; MAXIMUM A POSTERIORI ESTIMATION; NEUROIMAGING; REVERSIBLE-JUMP MARKOV CHAIN MONTE CARLO METHOD;

IMAGE ANALYSIS;

EID: 0035554865     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. An overview and some new developments in the statistical analysis of PET and fMRI data
2. Imaging neuroscience - Principles or maps
3. An evaluation of methods for detection of brain activations from PET or fMRI images
4. Spatial mixture modeling of fMRI data
5. Deducing statistical properties of brain activation from real data for use in constructing phantoms
6. Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method
7. Reversible jump Markov Chain Monte Carlo computation and bayesian model determination
8. Reversible jump Markov Chain Monte Carlo for bayesian deconvolution of point sources