AM-FM Analysis of Structural and Functional Magnetic Resonance Images

This thesis proposes the application of multi-dimensional Amplitude-Modulation Frequency-Modulation (AM-FM) methods to magnetic resonance images (MRI). The basic goal is to provide a framework for exploring non-stationary characteristics of structural and functional MRI (sMRI and fMRI). First, we provide a comparison framework for the most popular AM-FM methods using different filterbank configurations that includes Gabor, Equirriple and multi-scale directional designs. We compare the performance and robustness to Gaussian noise using synthetic FM image examples. We show that the multi-dimensional quasi-local method (QLM) with an equiripple filterbank gave the best results in terms of instantaneous frequency (IF) estimation. We then apply the best performing AM-FM method to sMRI to compute the 3D IF features. We use a t-test on the IF magnitude for each voxel to find evidence of significant differences between healthy controls and patients diagnosed with schizophrenia (n=353) can be found in the IF. We also propose the use of the instantaneous phase (IP) as a new feature for analyzing fMRI images. Using principal component analysis and independent component analysis on the instantaneous phase from fMRI, we built spatial maps and identified brain regions that are biologically coherent with the task performed by the subject. This thesis provides the first application of AM-FM models to fMRI and sMRI.

Name thesis:degree_name

Electrical Engineering

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Year

2013

Author dc:creator

• Ulloa Cerna, Alvaro Emilio

Contributors dc:contributor

• Pattichis, Marios
• Calhoun, Vince
• Liu, Jingy

Language dc:language

English

Repository record dc:identifier

https://digitalrepository.unm.edu/ece_etds/255

OAI identifier oai:identifier

oai:digitalrepository.unm.edu:ece_etds-1254