joint detection-estimation of brain activity in fmri using graph cuts thesis for the master degree...

Joint Detection-Estimation of Brain Activity in fMRI using

Graph Cuts

Thesis for the Master degree in Biomedical EngineeringLisbon, 30th October 2008

Joana Maria Rosado da Silva Coelho

Contents:

1 Introduction

2 Objectives

3 Proposed Model

4 Results

5 Conclusions

• fMRI technique aims at identifying cerebral areas (Brain Mapping) that were activated by an external stimulus – paradigm.

• Classical tasks to induce neuronal responses:• visual activation (looking at changing patterns);• sensorimotor activation (sequence of defined finger movements).

• This modality is based on the assumption that activated regions present increased metabolic activity.

functional MRI:

• The higher proportion of hemoglobin molecules bound with oxygen (oxyhemoglobin) is observed as a signal increase on T2*- weighted images (increase in the BOLD signal).

• BOLD signal does not measure brain function directly.

fMRI-BOLD

Baseline Activation

• After a stimulus application there is a local hemodynamic change in capillaries and draining veins.

• This vascular response can be modeled by an hemodynamic response function.

Hemodynamic Response Function

Diagram of a typical fMRI data set.

Type of Data:

Example of a time course from a visual stimulation experiment.

Type of Data:

• Classical method: - Statistical parametric mapping (SPM) commonly based on GLM - 2 steps algorithm: Estimation and Inference - Inference step needs the tuning of the p-value

SPM-GLM

2211 xxy

Contents:

1 Introduction

2 Objectives

3 Proposed Model

4 Results

5 Conclusions

Objectives:

Estimate the hemodynamic response function (HRF)

Incorporate the drift removal

Statistical Model:

SPM-Drift-GC

Model spatial correlation

Detect activated regions

Contents:

1 Introduction

2 Objectives

3 Proposed Model

4 Results

5 Conclusions

Proposed Model:

Neuro-Hemodynamic

System

Stimuli BOLD Signal?

Bayesian Approach – MAP criterion

The Maximum a Posteriori (MAP) estimation is obtained by computing

where

)()(),,,(),,,( idihiiiiyiiii dEhEdhbyEdhbyE

Data Fidelity Term Prior Term

),,,(minarg)ˆ,ˆ,ˆ(,,

iiiidhb

iii dhbyEdhbiii

Algorithm

•For each voxel, the estimation of bi, hi

and di is performed iteratively.

•h0 is a gamma function as proposed by Friston et al in 1998 which provides a physiological reasonable waveform to the HRF.

• The final step models spatial correlation.• Since different tasks activate different brain regions, it is less probable that non-activated voxels appear inside of an activated region and the converse is also true.• Avoids misclassification inside activated regions. • Energy function:

• Dp – cost of attributing the label to the pixel p

• Vh,v – cost of attributing the labels , to the N neighbour pixels

Nvh

SCv

SChvh

Pp

SCppp

SCpp VDE

,, ),(),(),(

Spatial correlation step

SCp

SCh SC

v

Contents:

1 Introduction

2 Objectives

3 Proposed Model

4 Results

5 Conclusions

Results – Synthetic dataSPM-Drift SPM-Drift-GC

Results – Synthetic data

Example of an SNR=2 dB time course with the real and estimated drift.

Motor task – Right foot

SPM-GC-Drift

Loose result SPM-GLM

Reference result SPM-GLM

Restrict result SPM-GLM

Results – Real data

Verb generation task

Loose result SPM-GLM

Reference result SPM-GLM

Restrict result SPM-GLM

Results – Real data

SPM-GC-Drift

Motor task – Tongue

Loose result SPM-GLM

Reference result SPM-GLM

Restrict result SPM-GLM

Results – Real data

SPM-GC-Drift

Verb generation task

Loose result SPM-GLM

Reference result SPM-GLM

Restrict result SPM-GLM

Results – Real data

SPM-GC-Drift

Results – Real data

Contents:

1 Introduction

2 Objectives

3 Proposed Model

4 Results

5 Conclusions

• The Bayesian framework combined with Graph Cuts algorithm improves the sensitivity in the detection of activated areas.

• The proposed algorithm does not require the tuning of any parameter by the clinician.

• The beta coefficients are considered to be binary.

• SPM-GC-Drift leads to similar results as the ones obtained by SPM-GLM. However, other brain activated regions were also detected which requires future analysis.

Conclusions

• The present work has been published• 30th Annual International IEEE EMBS Conference in Vancouver, British Columbia, Canada• RecPad2008 – 14ª Conferência Portuguesa de Reconhecimento de Padrões

•… and submitted• Human Brain Mapping international journal

Thank you for your attention!!!

Conclusions