Functional Magnetic Resonance Imaging (fMRI)

Alexander Wolf (12568449)

Supervisor : Dr. Peter Tischer

Presentation Overview

- Project Objectives

- Project Background

- Results

- Conclusion

- Future Work

Project Objectives

- Investigate filtering of fMRI data.

- Develop new filtering techniques.

- Develop software tools for fMRI analysis.

fMRI Introduction

- fMRI is a sequence of MRI’s over time.

- Images contain White Matter, Grey Matter, Cerebral-Spinal Fluid and Other.

- Basic unit in fMRI is the volume element (voxel).

fMRI Importance

- Neurologists use them to determine what regions of the brain where active/inactive for a known task performed by the subject.

- 3D fMRI image is segmented into active/inactive regions.

Filtering fMRI

- Anomalous values can occur due to motion, image capture.

- Leads to incorrect analysis, false conclusions.

- To account for anomalous values, consider filtering approach.

- Classical methodologies in fMRI such as FSL(FMRIB Software Library) and SPM(Statistical Parametric Mapping) have not considered filtering of any kind.

Filtering Objectives

- Recognition of anomalous data.

- Determine unreliable and reliable data.

- Determine whether voxel represents the same kind of voxels that belong together.

Filtering Importance

- Can determine whether we have enough data to work with so we can eliminate the need for motion compensation altogether.

- Determines proportion of voxels that are affected by motion compensation.

- Replace voxels by more meaningful values.

- Determine which voxels represent activation.

Filters Investigated

- Vector Median Filter(VMF)

- Tischer Least Change Filter(TLC)

- n-preserve Filter

- n-preserve Filter(New method).

- Least Change Filter(New method)

Vector Median Filter

- Replaces the center pixel by the pixel in the mask whose value is most inlying.

- Inlying is the value whose total sum of the absolute difference to every other pixel in the mask is minimised.

Original Noisy

Noise : Impulse, Probability: 0.1, Maximum Value : 255.

Vector Median Filtering

Original Filtered

RMSE : 3.92

Vector Median Filtering

Filtered

Noise Free. RMSE : 2.86

Vector Median Filtering

Tischer Least Change Filter

- Only replaces the center pixel if it is the most outlying value in the mask.

- Replaces it by the most similar value in the mask.

Original

Noise Free

Tischer Least Change

Original Filtered

RMSE : 0.38

Tischer Least Change

n-preserve Filter

- Given n, find the nth most nearest neighbour to the center pixel.

- Replace center pixel by the value which is the nth nearest neighbour

Original

Noise Free.

n-preserve Filter

n-preserve filter(New method)

- Extends n-preserve approach but instead finds the nth nearest neighbour whose total sum compared to its neighbours in the current region is minimised.

Filtered

Iteration = 1, n=2, Noise Free. RMSE: 1.41

n-preserve filter(New method)

Filtered

Iteration = 2, n=2, Noise Free. RMSE: 1.57

n-preserve filter(New method)

Least Change Filter (New Method)

- Extends TLC to preserve n-pixel features only if the center pixel is most outlying.

- Leave center pixel unchanged if it is not the most outlying.

Original Filtered

Iteration = 2, n=2, Noise Free. RMSE: 0.38

Least Change Filter (New Method)

Original Filtered

Iteration = 1, n=2, Noise Free. RMSE: 6.00

n-preserve Filter

Original Filtered

Iteration = 1, n=2, Noise Free. RMSE: 1.41

n-preserve filter(New method)

Original Filtered

Iteration = 1, n=2, Noise Free. RMSE: 0.38

Least Change Filter (New Method)

Conclusion

- VMF removes noise better than the other filters.

- VMF can blur edges and corners.

- VMF performance on noise-free images is not as good as other filters.

Conclusion cont’d

- TLC doesn’t work well when removing noise.

- TLC works well when preserving 2 pixel features.

- TLC is more suited to noise-free images to preserve original structure.

Conclusion cont’d

- n-preserve filter is good at preserving n pixel regions in the original image.

- n-preserve filter tends to destroy smaller pixel regions.

- Filters tend too have unavoidable consequences.

Conclusion cont’d

- New n-preserve filter works better on noisy/ noise-free images compared to original n-preserve filter.

- New Least Change Filter works almost as well as TLC to preserve 2 pixel regions.

Future Work

- Consider different fMRI parts of the anatomy such as heart, lungs, kidney and liver.

- Apply concept to different areas.

- Binarize the image to show active/inactive regions of the brain. Show connected regions.

THE END