MINC meeting 2003 Pipelines: analyzing structural MR data Jason Lerch.
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Transcript of MINC meeting 2003 Pipelines: analyzing structural MR data Jason Lerch.
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Whither this talk?
• Provide a brief overview of what we can do with anatomical MRI (aMRI)
• Describe the procedure, including the necessary commands, for running Voxel Based Morphometry (VBM)
• Give a quick overview of our visualization tools.
• Outline other analysis possibilities
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A plug for aMRI
• Clinical: – how is the patients’ anatomy different from controls– use of anatomical information to diagnose disorders
• Anatomy - identification of macroscopic features.
• Psychology: does anatomy change with behavioural function?– ex: right hippocampus bigger in London taxi drivers
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Population Analyses
Voxel Based Morphometry Tissue densities at every voxels
Asymmetry AnalysisTissue asymmetry at every
voxel
Comparison of VolumesWhole brain, tissue type, or
structure volumes
Cortical Thickness Analysis Thickness at every vertex
Deformation AnalysisDeformation difference in
registration towards model
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The image processing pipeline
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Voxel Based Morphometry (VBM)
• Looks at changes in tissue concentration.
• Fully automatic.• Useable for all tissue
types.• Fairly simple
procedure.• Widely used.
From Paus et al, Science, 1999
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Non-uniformity correction
Native Corrected
nu_correct native.mnc corrected.mnc
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Registration
Subject Model
mritotal native.mnc to-tal.xfm
Transform
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mincresample -like model.mnc -transform to-tal.xfm corrected.mnc final.mnc
Resampling
• Creates a volume that is:– Non-uniformity corrected using the output of nu_correct.– Transformed using the output of mritotal.– Given the same sampling as a model.
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Classification
classify_clean final.mnc classified.mnc
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Classification explained
• Set of spatial priors:– Based on talairach space.– Generated from tissue probability
maps.– Used to train classifier for each
individual subject.
• Can handle multiple inputs (t1, t2, PD).
• Multiple outputs– GM, WM, CSF– Lesions.
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Masking
cortical_surface classified.mnc mask.obj 1.5surface_mask2 classified.mnc mask.obj masked.mnc
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Tissue MapsG
rey
Mat
ter
Wh i
te M
a tt e
r
CS
F
smooth_mask -binvalue x -fwhm x masked_cls.mnc map.mnc
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Tissue Density Explained
• Tissue Density:– Neighbourhood average of
one tissue type.– Size of neighbourhood can
be specified.– Resulting voxels have
values between 0 (no voxels of that tissue type in that neighbourhood) and 1 (all voxels in neighbourhood of that tissue type).
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Statistical Modelling
Linear Model
= tissue density map= matrix of predictors= slope of predictors= random variance
= significance of whole model= significance of term within model
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The GLIM file
Subject_001_smooth_gm.mnc 1 25Subject_002_smooth_gm.mnc 1 32Subject_005_smooth_gm.mnc 1 38Subject_013_smooth_gm.mnc 1 15
Filename corresponding to tissue density file of each subject
Intercept - always 1 Predictor
Text file
glim_image -t_stat output.mnc t_stat voxel column 2 matrix.glim
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An aside on visualization
File 1
File 2
Merged Files
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Visualization 2: JIV
• JIV– Java: runs
over the internet.
– Great for sharing data with others.
– Able to show multiple volumes.
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Visualization 3: Display
• Display:– Brain painting.– Viewing object files.– Placing tags.– Basic visualization
Volume Window
Menu Window
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Segmentation
QuickTime™ and aYUV420 codec decompressor
are needed to see this picture.
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Cortical Thickness estimation
QuickTime™ and aVideo decompressor
are needed to see this picture.