Transcript Final Presentation

Multimodal Visualization for
neurosurgical planning
CMPS 261
June 8th 2010
Uliana Popov
Motivation
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Each year more than 200,000 people in the
United States are diagnosed with a primary or
metastatic brain tumor.
Brain cancer remains one of the most
incurable forms of cancer, with an average
survival period of one to two years.
The chances of surviving for a person with a
brain tumor greatly depends on all of the
following:

type of tumor
What is a brain tumor?
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A group (mass) of abnormal cells that starts in
the brain
There are over 120 different types of brain
tumors, which makes effective treatment
complicated.
Diagnosis of brain tumors
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Diagnostic tools include: patient history, a
brain scan, CT scan, MRI.
MRI provides a much greater contrast
between the different soft tissues of the body
than computed tomography (CT) does
The first MR image was published in 1973
MRI
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How does it work?
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The body is largely composed of water
molecules.
Each water molecule has two hydrogen nuclei or
protons
A powerful magnetic field causes the magnetic
moments of some of these protons to align with
the direction of the field.
The protons in different tissues return to their
equilibrium state at different rates
MRI sequences

echo time - TE
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repetition time – TR
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T1
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T1-weighted scans use a gradient echo (GRE) sequence, with
short Te and short Tr
This scan runs very fast allowing the
→ easy too collect high resolution 3D datasets.
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T2
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T1-weighted scans provide good gray matter/white matter
contrast.
MRI sequences (cont)
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Diffusion MRI
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Diffusion MRI measures the diffusion of water molecules in biological tissues.
If molecules in a particular voxel diffuse principally in one direction
→ the majority of the fibers in this area are going parallel to that direction.
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Fluid Attenuated Inversion Recovery (FLAIR)
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Inversion-recovery pulse sequence used to null signal from fluids.
Susceptibility weighted imaging (SWI)

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Produces an enhanced contrast magnitude image
very sensitive to venous blood, hemorrhage and iron
storage.
Used to enhance the detection and diagnosis of
tumors
Goal
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Determine:

Number of tumors
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Number of abnormal cells*
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Tumor margins
*voxels
Previous Work
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Probabilistic segmentation of brain tumors
based on multi-modality magnetic resonance
images. 2007
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Acqire results using 21 patients.
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Test it on 22nd
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2D
3D brain tumor segmentation in MRI using
fuzzy classification, symmetry analysis and
spatially constrained deformable models.
2008
My approach
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Combine together
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*voxel
Apply symmetry criteria to filter out ”not
interesting” regions
Use multimodality to vote for each cell*
Data
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IEEE contest organizers provided the data*
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MRI sequences
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Sequence name
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Dimensions
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Data type
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Voxel to World matrix
* The dataset is courtesy of Prof. B. Terwey, Klinikum Mitte, Bremen, Germany.
Data (cont)
The process
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For each sequence in the data
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Convert to physical space
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Register
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Calculate Symmetry
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Calculate Gradient
The process, step 1
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Convert from computational space to physical
The process, step 2
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Registeration
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MedINRIA
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Algorithm: manual landmark based
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Non linear transformation
The process, step 3
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Find asymmetry :
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If a[i,j] ~= a[n-i, j]
a[i,j] = 0;
a[n-i,j] = 0;
The process, step 4
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Calculate gradient
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Data values are scalars
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Scalar' = Vector
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Calculate Magnitude of the vector
Process (cont)
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Reference – healthy brain
Generated by using BrainWeb* - Simulated
MRI Volumes for Normal Brain
*McConnell Brain Imaging Centre (BIC) of the Montreal Neurological Institute, McGill
University.
Process (cont)
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Calculate probability
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Voting function
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If Healthy cell gradient < X && tumor cell gradient > Y
Higher probability that the cell is abnormal
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Treat noise
clear skull boundaries
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manual registration
Results
Results (cont)
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Voxel size
0.924mm x 1.14169mm x 2.38699mm
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Total brain voxels
1,222,332
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Tumor voxels
14,878
→ 0.012171816 %
Tools
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Algorithm implementation
C++ using VTK libraries
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Viewers
Paraview
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Registration
MedINRIA
Future Work
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Make a tool
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Add DTI processing
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Determine spatial relationship between tumor
and WM fiber tracts