STIR Atlas Summer Work

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Brain Atlas

Dan Diner

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Introduction

• PET is a nuclear imaging technique• Tracer concentrations in brain are recorded • The information collected is used for brain

study, disorder analysis, and diagnosis

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ROI/VOI Mapping

• ROIs(Regions of Interest) are small regions mapped onto brains

• Ussualy mapped out manually- very time-consuming and requires highly-paid personnel (raters)

• Raters have inter- and intra- subject variation• As PET scan quality increases, so does the

time it takes for raters to do their job

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• PET has poor spatial resolution, high temporal

• MRI has high spatial resolution• ROIs are therefore mapped onto the

MRI of a subject, onto which the PET scan is co-registered

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MRI PET Co-Registered

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Brain Atlases• An “average brain” with previously labeled

VOIs (Volumes of Interest)• labels those VOIs onto inputted images• Never have been as accurate as manual

methods, but very precise - no inter- or intra- subject variation

• much faster than manual methods

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Brain Atlases

• Many labs make their own. • Each different because each lab has own

labelling method• Could be single- or multiple-sibject• Could be made for only one specific region or

many• Probabilistic atlases tell you the probability of

a certain voxel belonging to a certain ROI

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Methods and Materials: Subject and Image Acquisition

• MRIs and PETs from 176 subjects of past studies

• 123 from a 1.5 Tesla MRI• 53 from a 3.0 Tesla

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Image Analysis Setup

• Worked on a PowerMac G5 using Mac OS X 10.5.4 Leopard

Major Software used:• Matlab (R2007a)• FSLVIEW and FLIRT• ART• iView

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Image Pre-Processing

• Raw MRI files were

copied to a common

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• Raw MRI images multiplied(matrix multiplication) by GM, WM, and CSF masks

• This got rid of all extraneous material (neck, skull, dura matter)

• Extraneous material messes with result data

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• Used iView to compare new images against raw ones

• This was done to look for undeleted extraneous material and accidentally segmented brain

• I used a scale of 0-3 to rate the new images (0= perfect, 1=some dura, 2=much dura, 3=segmented brain)

• Most images were 0s and 1s.

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Normalization Procedure

• Normalization is moving an image into a standard space

• MNI template to which were were normalizing was reoriented towards raw MRI native space

• New MRI images also normalized to this template

• We examined the normalized images, and found that some were very well aligned, but others were misplaced or improperly warped

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• warping is normalization Degrees of Freedom 9-12

• ART uses a 2-step process to warp MRIs: linear registration, then warp

• Because the rotation was bad, we thought that the linear registration had gone wrong

• We tried linear registration with FLIRT, and then warping with ART

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• The rotations came out looking good, but close inspection revealed that some leftover dura got warped into the new brain

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• An MRI with a particularly large amount of dura was manually cleaned, and then had FLIRT and ART run on it

• The resulting image was perfect

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Conclusion

• ART is very sensitive to extraneous material

• all dura needs to be properly removed from all images before normalization

• A better automatic method for cleaning up dura needs to be developed

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