7T Thalamus and MS Studies

Jason SuSep 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:– 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

After EditingBefore Editing

CSF Removal

After EditingBefore 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

MPR

AGE

• Good gray/white contrast throughout brain

• Uniformity isn’t ideal

• Shows few lesions

WM

nMPR

AGE

• Extreme gray/white contrast throughout brain

• Uniformity seems better than MPRAGE, could be visual effect due to extreme contrast

• Shows some lesions

T2-C

UBE

• Poor gray/white contrast

• Decent uniformity

• Shows few lesions

FLAI

R

• 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

Prior Subject 3

New Subject

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

Mean BrainTemplate

Prior Subject 1

Prior Subject 2

Prior Subject 3

New Subject

Drawbacks• Not straightforward how to adapt this to an ROI-focused

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?