1. MSmcDESPOT A Brief Summary April 2, 2009

2. 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

3. The Technique

4. 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

5. 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 M 0 – 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 bilayers of 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

6. 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 2mm 3 isotropic – 32-direction DTI sequence at 2.5mm 3 – T2/PD FSE at 0.43mm 2 in-plane and 6mm slice resolution – FLAIR at 0.86 mm 2 in-plane and 3mm slice resolution – MPRAGE pre and post Gd constrast for patients at 1mm 3

7. 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) 1.Linear coregistration – so that each voxel across all the images is the same piece of physical tissue 2.Brain extraction from skull– to reduce computation time

8. Preprocessing DTI Similarly, the diffusion weighted images must also be coregistered for eddy current correction and brain extracted

9. 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

10. mcDESPOT Maps

11. mcDESPOT Maps - MWF

12. DTI Maps Fraction AnisotropyMean Diffusivity

13. 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 2mm 2 MNI152 T1 standard space template

14. 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 mcDESPOT maps The warp field is found with FSL’s FNIRT using an 8mm 3 warp resolution

15. Standard Space Reg. – SPGR Target MNI MNI152 2mmmcDESPOT SPGR FA 13 Registration Target

16. 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

17. Standard Space Reg. – FMRIB58 FA

18. Standard Space Reg. – DTI FA

19. Postprocessing – Clinical Each clinical scan for each patient is linearly registered to the mcDESPOT target Then the target->MNI warp is applied

20. 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

21. 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

22. 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 – 2mm 3 too low res., 1mm 3 MPRAGEs better but more noisy – Homogeneity correction with SPM

23. P018 Uncorrected MPRAGE

24. P018 Corrected MPRAGE

25. Tissue Segmentation – Raw WM

26. Tissue Segmentation – Filtered WM

27. Tissue Segmentation – Edited WM

28. 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

29. Lesion Seg. – Core Pre-selection

30. Lesion Segmentation – Edited Cores

31. Lesion Seg. – Penumbra/DAWM

32. 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

33. P018 mcDESPOT – Edited SPM WM

34. P018 mcDESPOT – NAWM

35. P018 mcDESPOT – DAWM

36. P018 mcDESPOT – Lesion Cores

37. P018 mcDESPOT – MWF

39. Correlation with EDSS

43. 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

44. 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 Demyelinated Voxel Volume Brain Fraction (WM+GM)/ (WM+GM+ CSF) All Patients0.000000.01122 Low-Risk CIS0.000720.84525 High-Risk CIS0.000720.84525 All CIS0.000011.00000 SPMS0.000270.00098 PPMS0.000270.00148 RRMS vs. SPMS0.008660.01732 Low vs high- risk CIS1.000000.54762

45. Open Questions and Future Work 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

46. Questions?