Blind Separation of sources in function MRI Sequences

Presented By: Eldad Klaiman

Limor Goldenberg

Supervised By: Michael & Alex Bronstein

Dr. Michael Zibulevsky

The Kasher Contest - In memory of Yehoraz Kasher

functional MRI:• Important tool for studying

the human brain activity.• High spatial resolution,

flexibility, harmlessness – made it popular.

• The BOLD technique: produce an image of the blood oxygenation level throughout the brain.

• A sequence of scans is in a short period of time, when the subject is asked to perform some task. High oxygenation levels represent high activity of the brain regions responsible for the task.

Blind Source Separation• Linear mixture of

independent sources• No a priori information is

known about their properties.

• “Blind Source Separation" = the problem of separating such sources.

• There exist powerful tools to solve it.

• Focus on the approach of sparse representations, which has proved its advantages in different works in the field.

The Problem

fMRI-BSS Model

fMRI

S1

S2

Sn

M1

Mn

Sources Mixtures

PreProcessing

M1’

Mn’

sAm

Separation

S1’

Sn’

Separated Sources

1As m

-Noise removal

-Identify Background

-Sparse Representation

fMRI Simulation

• Background – Brain Image.

• Spatial Function:

• Hemodynamics:

• Gaussian Noise

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fMRI simulator GUI

fMRI Simulator - Results

“fMRI” frames Hemodynamics

Preprocessing – Sparse Representations

• Wavelet Packets is used to create sparse images.

• “Best” Node is selected by sparseness Criteria

• Scatter plot of resulting images:chasing the illusive “X”

Geometric Separation

• Clustering - FCM.

• Angle Histogram.

Separation Example

Source #1:3 spatial

components

Source #2:2 spatial

components

Issues Encountered

• Preprocessing : Zero-mean, LPF, etc.

• Sparseness Criteria : Shannon entropy selected.

• Stability / Parametric Sensitivity : thresholds.

Principal Component Analysis

• Problems of high order:more mixtures than sources

• Problem dimension reduced using PCA

PRINCOMP( )

PCA Revelations

(1) Background Separated from activity sources

(2) No need to know the exact number of sources.

ICA - Infomax

• Artificial Neural Network Viewpoint,maximize output Entropy.

• InfoMax ICA Matlab Toolbox:(courtesy of Scott Makeig & Co.)

• Preliminary Results can be obtained without mixture preprocessing.

ICA Separation Example

ICA Notes

• Sign and Order limitations.

• Improved robustness and quality, compared to geometric separation.

• In most cases, the sparse representation improved the quality of separation.

Application on the Real Thing

Real fMRI Issues

False Artifact Sources – created due to head movement, Noise.

Background separated from activity sources.

Conclusions

• Achieved good results by geometric and ICA separation.

• ICA – robustness, quality.

• PCA – model selection, added values.

• Potential as fMRI analysis tool. – Quick, low cost.

– Exact knowledge of simulation flow - not needed.

– Not relying on high time resolution.

Further Progress

• A new horizon for fMRI-ICA academic research and projects.

• A “friendly” and enhanced fMRI-ICA application was developed for simple, user-oriented application of algorithm.

• Experimental application of the separation algorithm on LORETA (EEG-CAT).

• Nethaniel’s Brain

• Dr. Michael Zibulevsky

• Johanan Erez and the Lab team

• Michael & Alex Bronstein

• Anat Grinfeld

Thanks to…

Sparseness Criteria

• L1:

• L0:

• Shannon Entropy:

• Clusters:

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