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ISMRM 2011 E-Poster #4643. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone. J. Su 1 , H.H.Kitzler 2 , M. Zeineh 1 , S.C .Deoni 3 , C.Harper-Little 2 , A.Leung 2 , M.Kremenchutzky 2 , and B.K .Rutt 1

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  1. ISMRM 2011 E-Poster #4643 mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone J.Su1, H.H.Kitzler2, M.Zeineh1, S.C.Deoni3, C.Harper-Little2, A.Leung2, M.Kremenchutzky2, and B.K.Rutt1 1Stanford U, CA, USA, 2TU Dresden, SN, Germany, 2U of Western Ontario, ON, Canada, 3Brown U, RI, USA Declaration of Conflict of Interest or Relationship I have no conflicts of interest to disclose with regard to the subject matter of this presentation.

  2. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Background • Conventional MRI measures such as lesion load have been criticized with adding little new information on top of clinical scores for multiple sclerosis (MS) patients • Measures that quantify the hidden burden of disease in white matter are urgently needed

  3. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Purpose • To apply mcDESPOT, a whole-brain, myelin-selective, multi-component relaxometric imaging method, in a pilot MS study • Assess if the method can explain differences in disease course and severity by uncovering the burden of disease in normal-appearing white matter (NAWM)

  4. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Study

  5. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Scanning Methods • 1.5T GE SignaHDx, 8-channel head RF coil • mcDESPOT: 2mm3 isotropic covering whole brain, about 15 min. • SPGR: TE/TR = 2.1/6.7ms, α = {3,4,5,6,7,8,11,13,18}° • bSSFP: TE/TR = 1.8/3.6ms, α = {11,14,20,24,28,34,41,51,67}° • 2D T2 FLAIR: 0.86 mm2 in-plane and 3mm slice resolution • 3D T1 IR-SPGR: 1mm3 resolution with pre/post Gd contrast

  6. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Processing Methods: MWF • Linearly coregister and brain extract mcDESPOT SPGR and SSFP images with FSL1 • Find myelin water fraction maps using the established mcDESPOT fitting algorithm2 Myelin Water Fraction 1FMRIB Software Library. 2Deoni et al., MagnReson Med. 2008 Dec;60(6):1372-87

  7. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Processing Methods: Demyelination • Non-linearly register mcDESPOT MWF maps to MNI152 standard space • Combine normals together to form mean and standard deviation MWF volumes • For each subject, calculate a z-score ([x – μ]/σ) at every voxel to determine if it is significantly demyelinated, i.e. MWF < -4σ below the mean Demyelinated Voxels

  8. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Processing Methods: WM • Brain extract MPRAGE images • Segment white and gray matter with SPM83 • Filter tissue masks to reduce noise then manually edit by a trained neuroradiologist • Calculate parenchymal volume fraction (PVF) as WM+GM divided by the brain mask volume FLAIR WM 3Statistical Parametric Mapping software package.

  9. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Processing Methods: Lesions & DAWM • Non-linearly register T2-FLAIR images to MNI152 standard space • Combine normals together to form mean and standard deviation volumes • Segment lesions as those voxels with z-score > +4 and diffusely abnormal white matter > +2 • Edit masks by a trained neurologist DAWM Lesions

  10. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Processing Methods: NAWM & DVF • Segment normal-appearing white matter (NAWM) as WM – DAWM – lesions • Find demyelinated volume fraction (DVF) • Sum the volume of demyelinated voxels in each tissue compartment and normalize by the compartment’s volume • # demy. voxels in compartment * voxel volume / compartment volume Normal-Appearing White Matter

  11. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Segmentations and DV FLAIR WM NAWM DAWM Lesions MWF Demyelinated Voxels DV in NAWM DV in DAWM DV in Lesions

  12. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Statistical Methods • Use rank sum tests to compare patient groups to normals along different measures • Perform an exhaustive search to find the best multiple linear regression model for EDSS using Mallows’ Cp4 criterion among 21 possible image-derived predictors: • PVF • log-DVF in whole brain, log-DVF in WM, log-DVF in NAWM, log-DVF in lesions • log-DV in those four compartments • mean MWF in those four compartments • volumes of those four compartments (lesion volume = T2 lesion load) • volume fractions of those four compartments with respect to the whole brain mask volume 4Mallows C. Some comments on Cp. Technometrics. 1973;15(4):661-75.

  13. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Results: Mean MWF in Compartments • Dotted line shows mean MWF in WM for normals. Rank sum testing was done for each bar against this • Testing was also done for RRMS vs. SPMS and CIS vs. RRMS, any significant differences are shown with a connecting bracket • Significance levels: * p < 0.05 ** p < 0.01 *** p < 0.001.

  14. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Results: DVF in Compartments • Dotted line shows demyelinated volume fraction in WM for healthy controls • With DVF, all patient subclasses were significantly different from healthy controls • PVF, however, fails to distinguish CIS and RR patients from normals

  15. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Results: Correlations with EDSS • Lesion load correlates poorly with EDSS • PVF and DVF are stronger indicators of decline

  16. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Results: Multiple Linear Regression • The best linear model for EDSS contains PVF (p < 0.001), mean MWF in whole brain (p < 0.001), and WM volume fraction (p < 0.01) • Whole-brain MWF and WM volume fraction significantly improve the prediction of EDSS over that produced by PVF alone • Explains 76% of the variance in EDSS (R2 = 0.76, adjusted R2 = 0.73) compared to 56% with only PVF

  17. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Discussion & Conclusions • DVF is able to differentiate CIS and RRMS patients from normals, whereas other measures such as PVF and mean MWF cannot • The invisible burden of disease may be more important than lesions in determining disability, since we observe a higher correlation of EDSS with DVF in NAWM than lesion load • A combination of established atrophy measures with new mcDESPOT-derived MWF are more capable in accurately estimating disability than either quantity alone

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