7T Thalamus and MS Studies

Report
7T Thalamus and MS Studies
Jason Su
Sep 16, 2013
Outline
• 7T Thalamus and MS studies
– Atrophy measures – PVF, thalamus, hippocampus
– Study-specific mean brain template construction
with ANTS
– Manual -> Automatic segmentation with label
fusion
7T Studies
• Thomas, Manoj, and Ives recently used
WMnMPRAGE to manually segment thalamic
nuclei in normal control
• Now looking to use this methodology in MS
patients at 7T with atrophy and disease markers:
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Brain atrophy, parenchymal volume fraction
T2-FLAIR lesions
Thalamic atrophy (whole or nuclei)
Thalamic lesions
Hippocampal atrophy
Brain Atrophy
• Want to measure the ratio of the brain
parenchyma to the intracranial cavity volume
• Previous MSmcDESPOT methodology
– FSL BET the “target” image (T1w SPGR, 18deg)
• The intracranial volume
– Segment WM+GM MPRAGE with SPM8
• The brain parenchyma volume
– Measures enlargement of ventricles mainly
T1w BET
T1w-MPRAGE
T2-CUBE
Brain Atrophy: New Method
• FSL BET T2-CUBE image
– Then edit to produce intracranial volume (~30min)
• Remove CSF by segmenting T2-CUBE w/ SPM8
– Note that T1w input is more typical for SPM8 but CSF
is so distinct on T2w it works
– Then edit to produce brain parenchyma
• Advantages:
– More true to the definition of intracranial cavity
– Better measure of the space between brain and skull
T2w BET
Before Editing
After Editing
CSF Removal
Before Editing
After Editing
?
Difficulties
• SPM8 is the weakest part of the pipeline
– Often needs manual tweaking between subjects to get
to a reasonable segmentation
– Registration to its atlas often goes haywire, likely
affected by 7T nonuniformity
• Would be easy to manually threshold bright
voxels as CSF but reviewers might prefer a proven
toolbox
– Is it better to be consistent or accurate?
– Does it matter in the end since always edited as a final
step?
T2-FLAIR Lesions
• Previous MSmcDESPOT methodology
– Nonlinearly register all subjects to MNI brain
– Normalize signal intensities to “robust” max in brain
– Group normal controls and find their voxel-wise mean
and std. dev.
– Compare new subject to the control population
• Voxels > 4 standard deviations above mean = lesion
• Voxels > 2 standard deviations above mean = DAWM
T2-FLAIR Lesions
• New methodology
– Create a study-specific mean brain template to
use instead of MNI
– More accurate registration should improve ability
to detect lesions, maybe cortical?
Mean Brain Template
• ANTs (Advanced Normalization Tools) is emerging as
the standard
– Available in pre-compiled OSX binary
– Provides useful parallelized script to compute a mean brain
(buildtemplateparallel.sh)
– But sometimes crashes whole computer!
• Which image contrast? Want something that:
– Provides useful image contrast throughout the brain
– Is less affected by B1+ inhomogeneity, as this variation
between subjects is not taken into account in these
registration algorithms
• Do typically correct for receive nonuniformity
– Shows few lesions so they do not misguide the registration
MPRAGE
• Good gray/white contrast
throughout brain
• Uniformity isn’t ideal
• Shows few lesions
WMnMPRAGE
• Extreme gray/white
contrast throughout brain
• Uniformity seems better
than MPRAGE, could be
visual effect due to
extreme contrast
• Shows some lesions
T2-CUBE
• Poor gray/white contrast
• Decent uniformity
• Shows few lesions
FLAIR
• Decent contrast
• Uniformity is poor, signal
loss in center of brain,
which we care about the
most for thalamus
• Shows many lesions
Decision
• My first attempt was with WMnMPRAGE
– Good contrast albeit unusual, but registration
algorithms should be indifferent to that
– Most importantly, this is the contrast we’re using
for thalamic segmentation so it is a natural choice
ANTs
• Sum all subject brains without registration
creating a crude template
• Rigidly register subjects to this crude base
• Sum these to produce the initial template
• Iterate until convergence:
– Nonlinearly register to the template
– Sum the new registrations to create a new
template
ANTs
Iteration 1
ANTs
Iteration 2
ANTs
Iteration 3
ANTs
Iteration 7
ANTs
Iteration 8
MNI T1w
Notes
• Cortical registration seems good
• Still not quite converged after 8 iterations
– 4 is the default number of iterations, way low
– Each iteration takes 12 hours, barring any crashes
• Once this is settled, can do segmentation of the
lesions
– Comparing FLAIR intensities to controls is fairly
straightforward
– May want to also do it on WMnMPRAGE for thalamic
lesions
Structural Atrophy
• After our review of label fusion and ASHS
papers, learned we can turn previous manual
segmentations into automatic
– Thomas has already manually segmented 2x6
controls, though in a non-accelerated
WMnMPRAGE
• Label fusion:
– Register the previous segmentations to the new
subject
– Use local information about the registration
accuracies to guide the decision
What We Want
Prior
Subject 1
Prior
Subject 2
New
Subject
Prior
Subject 3
Problem
• Nonlinear registration of N prior subjects is
expensive to every new subject
• Instead use the mean brain template as an
intermediate space
– Allows us to only need one registration for a new
subject
Proposed Approach
Prior
Subject 1
Prior
Subject 2
Mean Brain
Template
New
Subject
Prior
Subject 3
Drawbacks
• Not straightforward how to adapt this to an ROIfocused registration
– Accuracy of the mean template may be imperfect due to
the expense of registering whole brain
• As in ASHS, would like to first start from the whole
brain registration
– Then do a targeted registration with small FOV on
thalamus or hippocampus
– Do we take brains all the way to the subject space and
register there or stay in the template space?
– ASHS chooses to use template space, less intuitive
• Argument for it has to do with producing a label probability map
which plays nicely with linear interpolation unlike a binary mask
• But STEPS does not expose that intermediate map
Other Concerns
• STEPS suggested a library in the range of 15+
subjects
– May not be able to achieve reliable automatic
segmentation until after the study
– Before that we can at least try to get something
reasonable to reduce Thomas’s tracing work
Nuclei Separability by T1
• Another possibility is to incorporate T1 map
information to help improve label fusion
– Possibly as an included variable in AdaBoost like
ASHS
Thalamic Nuclei
Notes
• For 3/5 nuclei, the T1 dist. is distinct from
some or all of its neighbors
Summary
Brain atrophy, parenchymal volume fraction
• Pipeline complete, but needs CSF segmentation needs constant tweaking between subjects
• Intracranial cavity editing done, parenchyma editing in progress
• Label fusion can reduce the intracranial cavity editing work
T2-FLAIR lesions
• Need to iterate even more for mean brain template to converge
Thalamic atrophy (whole or nuclei)
• Once template is finalized, can begin playing with STEPS for label fusion
Thalamic lesions
• Try lesion segmentation pipeline with WMnMPRAGE
Hippocampal atrophy
• Thomas is working on measuring CA1 thickness
• Segmentation is open question, try ASHS?

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