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: – 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-MPRAGET2-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 Editing Before Editing

CSF Removal After Editing Before 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 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 Brain Template Mean Brain Template 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?