Transcript Anatomical MRI module

MNTP Trainee: Georgina Vinyes Junque, Chi Hun Kim
Prof. James T. Becker
Cyrus Raji, Leonid Teverovskiy, and Robert Tamburo
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Structural differences based on Voxel-wise comparision
Advantages
 Automated, Un-biased, Whole brain analysis  compared to Manual
ROI tracing
 Well established and Widely used over the past decade
 Results are biologically plausible and replicable
 We know the LIMITATIONS
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Voxel-Based Morphometry
Bias Field Correction
Skull Stripping
Spatial Normalization to Template
Tissue Segmentation
Modulation
Smoothing
Voxel-wise statistical tests
Preprocessing
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MRI sequence
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 T1 (MPRAGE)
 Multicenter AIDS Cohort
 3T Siemens TrioTim
Study (MACS)
 53 males
 Age: 50.2 +- 4.4
 Slices: 160; thickness 1.2mm
 Voxel size: 1 x 1 x 1.2 mm
 TE: 2.98; TR: 2300
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Subjects
Software
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SPM2 & SPM5 (Wellcome Trust Centre
for Neuroimaging)
VBM2 toolbox (Gaser et al,
http://dbm.neuro.uni-jena.de/)
N3 algorithm
Brain Extraction Tool in FSL
Watershed algorithm in FreeSurfer
Statistical Analysis
 Gray matter Volume
differences
 in Drug users vs. Non-Drug
users
Original
Image
N3
Corrected
Image
Corrected Bias field
= Original – Corrected image
Software: N3 (Nonparametric Nonuniform intensity Normalization)
+
Known Bias Field
N3
Successful Removal of
Known Bias field
< Amount of Corrected Bias Field
over N3 Repetition >
7
N3
Original image
N3
N3
N3
Mean Signal Intensity of
Corrected Bias Field
N3
6
Mean
5
4
3
2
1
0
1
2
3
4
5
# of
repetition
Corrected image
After 5th repetition
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Software
 Brain Extraction Tool (BET; v2.1 in FSL software package)
 Watershed algorithm in FreeSurfer software package v5.1.0
BET default setting (1 min)
 Optimization of Parameters (2min)
Watershed default setting (30 min)
Teverovskiy, 2011, OHBM, Poster Presentation
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Fitting each individual brain into the same brain template,
To compare regional differences between groups
1.
Customized template
 Recommended in special populations (Eg:
babies or the elderly).
2.
Standardized template
 Better comparison with similar studies using
the same template.
 Eg. MNI: 152 brains, mean age 25,
female 43%
http://dbm.neuro.uni-jena.de/vbm/vbm2-for-spm2/creating-customized-template/
MACS template
Customized template
Default-MNI template
Glass brains, showing reduced grey matter volume in drug users
compared to non-drug users, at 0.01 Uncorrected level
1. Signal Intensity of Voxel
Grey Mater Segmentation
White Mater
Segmentation
2. Tissue Probability Map
CSF
Segmentation
http://dbm.neuro.uni-jena.de/vbm/segmentation/
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Recovering volume information which was lost by spatial normalization p
rocess.
It can be thought as atrophy correction.
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It’s recommended if you are more
interested in volume changes than
differences in concentration (or
density)
http://dbm.neuro.uni-jena.de/vbm/segmentation/modulation/
Modulated:
Changes in GM volume
Unmodulated:
Changes in GM density
Glass brains showing reduced grey matter in drug users compared to
non-drug users, at 0.01 Uncorrected level
Intensity of every voxel is replaced by the weighted
average of the surrounding voxels. Larger kernel
size, more surrounding voxels
 Make distribution closely to Gaussian field model
 Increase the sensitivity of tests by reducing the
variance across subjects
 Reduce the effect of misregistration
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5 mm
10 mm
Glass brains showing reduced grey matter volume in drug users
compared to non-drug users, at 0.01 Uncorrected level
15 mm
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There’s a lot of options in processing that can
affect data and results.
We have to undertand what we are doing in
every step to better adjust options to our
sample study.
Since these techniques have several pitfalls,
we have to carefully interpret published
results.
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Prof. James T. Becker
TA: Cyrus Raji, Leonid Teverovskiy, Robert
Tamburo
Prof. Seong-Gi Kim & Prof. Bill Eddy
Tomika Cohen, Rebecca Clark
Fellow MNTPers!