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MSmcDESPOT. A look at the road behind and ahead October 30, 2009. The Technique. mcDESPOT (multi-component driven equilibrium single pulse observation of T1 /T2) is a quantitative MR technique that characterizes many of the key parameters relevant to MRI
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MSmcDESPOT A look at the road behind and ahead October 30, 2009
The Technique • mcDESPOT (multi-component driven equilibrium single pulse observation of T1/T2) is a quantitative MR technique that characterizes many of the key parameters relevant to MRI • A series of spoiled gradient echo (SPGR) and phase-cycled steady-state free precession (SSFP) scans are collected at different sets of flip angles • The signal from a single voxel across all these scans is modeled as the combination of two different pools of water, a fast and slow pool in exchange with each other • A fitting algorithm (stochastic region of contraction) computes the optimal set of parameters that characterizes the observed signal at each voxel in the brain
The Technique • The final result is a set of 10 maps defining MR parameters throughout the entire brain: • Fast pool T1, T2, and residence time • Slow pool T1 and T2 • Single pool T1, T2, and M0 – this is when we do not model each voxel as the sum of two pools • B0 off-resonance • Fast volume fraction – this is how much each pool contributes to a voxel’s signal or alternatively, what fraction of a voxel is occupied by each pool • We attribute the fast pool to water trapped between the lipid bilayersof the myelin sheath, while the slower-relaxing species is believed to correspond to the less restricted intra- and extracellular pools • This needs further histological verification but we will continue under this premise • Thus we rename the fast volume fraction to the “myelin water fraction” (MWF), our key parameter of interest
The Study • Given this technique which we believe can characterize myelination in the brain, we move our sights to examine a disease that is characterized by demyelination: multiple sclerosis • 23 normals + 2 pending • 25 MS patients, 5 in each of 5 classes (low-risk CIS, high-risk CIS, RR, SP, PP) • Each scanned at 1.5T to avoid B1 inhomogeneity and assumed flip angle inaccuracy: • mcDESPOT protocol at 2mm3 isotropic • 32-direction DTI sequence at 2.5mm3 • T2/PD FSE at 0.43mm2 in-plane and 6mm slice resolution • FLAIR at 0.86 mm2 in-plane and 3mm slice resolution • MPRAGE pre and post Gdconstrast for patients at 1mm3
Preprocessing for mcDESPOT • Prior to running the fitting algorithm, we must run the SPGR and SSFP images through a preprocessing pipeline • All the following steps are achieved using the FMRIB Software Library (FSL) • Throughout this presentation, we will focusing our attention on a single SPMS patient, affectionately known as P025
Preprocessing mcDESPOT – Step 1 • Linear coregistration with trilinear interpolation (FSL FLIRT) – so that each voxel across all the images is the same piece of physical tissue
Preprocessing mcDESPOT – Step 2 • Brain extraction from skull (FSL BET) – to reduce computation time
Preprocessing DTI – Step 1 • Similarly, the diffusion weighted images must also be coregistered for eddy current correction and brain extracted
Processing • Now that the data is all prepared, it is run through the parameter fitting program • The mcDESPOT volumes are processed with our own code • The diffusion volumes are fitted with FSL’sdtifit
DTI Maps Fraction Anisotropy Mean Diffusivity
Postprocessing • Postprocessing involves bringing these various maps and scans into a standard space so that they can be compared with each other on a voxel per voxel basis • We use the 2mm2 MNI152 T1 standard space template and the 1mm2 FMRIB58 FA map, an average of FA maps from 58 subjects, each nonlinearly registered to the MNI brain
Postprocessing – mcDESPOT • The mcDESPOT coregistration target for each subject is nonlinearly registered to the MNI brain and this warp field is in turn applied to the 10 maps • The warp field is found with FSL’s FNIRT using an 8mm3 warp resolution
Standard Space Reg. – SPGR Target • MNI MNI152 2mm mcDESPOT SPGR Registration Target
Postprocessing – DTI • The DTI FA map for each subject is nonlinearly registered to the FMRIB58 map • Alternatively, we could register it to the mcDESPOT target and use the already computed warp field
Postprocessing – Clinical • Each clinical scan for each patient is linearly registered to the mcDESPOT target • Then the target->MNI warp is applied
Analysis • Whole brain MWF, z-score based thresholding • Would like to move onto tissue-specific MWF study, particularly in these types: WM, GM, NAWM (normal-appearing white matter), NAGM, and lesions only • This brings us to the tricky issue of segmentation
Current Issues – Segmentation • Lesion segmentation is proving to be a very difficult task • Ultimately we’d like to use the lesion mask to subtract from our other tissue classifications to produce “normal-appearing” tissues • Here’s some pictures of our results so far using FSL’s FAST with a variety of channels (SPGR, FLAIR, T2, PD)
Segmentation Questions • What is the best way to obtain a lesion mask? • Should we do operations on the masks we have, like subtracting the out-of-brain CSF from the SPGR-FLAIR 3 class CSF mask? • Are there other existing tools out there for either automatic or semi-manual lesion segmentation?
Statistical Study • We intend to use the Wilcoxon rank sum test as our workhorse for statistical comparison • Many of our variables are not intrinsically Gaussian so the t-test and ANOVA do not seem applicable
Open Questions • DTI registration to standard space: FMRIB58 or via SPGR mcDESPOT target? • Patient matching: gender or age first? • How to deal with lesions across patients: Vrenken approach is to replace missing data with the mean of the group
