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MSmcDESPOT. A Brief Summary April 2, 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 Brief Summary April 2, 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
The Technique Comparison of acquired data and single- and two-component SPGR and bSSFP signal intensity vs. flip angle curves in four different brain regions (shown by the box outlines). Plotted data points correspond to the mean values obtained from the ROI, and the error bars represent 1 SD. In all regions, the two-component model is observed to more closely agree with the acquired multiangle SPGR and bSSFP data. • 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 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 Gd constrast 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 • Using the FMRIB Software Library (FSL) • Linear coregistration – so that each voxel across all the images is the same piece of physical tissue • Brain extraction from skull– to reduce computation time
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’s dtifit
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
Standard Space Reg. – SPGR Target • MNI MNI152 2mm mcDESPOT SPGR FA 13 Registration Target
Analysis • Initially, whole brain MWF, z-score based thresholding • Wanted to move onto tissue-specific MWF study, particularly these types: WM, GM, NAWM (normal-appearing white matter), NAGM, and lesions only • This brings us to the tricky issue of segmentation
Segmentation • Difficulty of needing both lesion and tissue classification • SPM gives WM/GM from MPRAGE but noisy • Lesion pre-selection through a voxel-based FLAIR analysis • Defined two lesion classes: lesion cores and penumbra/DAWM • Fairly extensive manual editing
Tissue Segmentation • Initial issues with using mcDESPOT SPGR target as segmentation • Tried permutations of multi-component segmentation (SPGR, FLAIR, T2, PD) • Discussed with Allan Reiss’s group • 2mm3 too low res., 1mm3 MPRAGEs better but more noisy • Homogeneity correction with SPM
Tissue Seg. – WM Comparison • FAST WM vs. Edited SPM WM • I don’t think FAST does as bad a job as the Reiss group suggested
More Analysis • With these tissues defined, we can now analyze more complex compartments: • WM • NAWM = WM – lesion penumbra – cores • DAWM = lesion penumbra – cores • Lesion cores, focal demyelination
Statistical Methods • We intend to use the Wilcoxon rank sum test as our workhorse for statistical comparison • This is essentially a non-parametric version of t-test, comparing medians instead of means • Unsure about the distribution of MWF and hard to determine with such small sampling
Preliminary Statistical Results • These is are old statistics using old tissue classes (i.e. whole brain only) • p-values from Wilcoxon rank sum test, reject null hypothesis if < .05 • First 6 rows compare patient populations vs normals • Significant results: • Demyelinated voxel volume can identify CIS patients • No difference between low and high-risk CIS patients according to these measures
Other interesting measures ROI Histogram-based: peak location, skewness, kurtosis Spatial distribution, true voxel-based analysis Further statistical analysis Multiple variable regression (ANOVA) – worried about normality constraints Model selection Open Questions and Future Work
