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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
