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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. The Technique

  7. 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

  8. mcDESPOT Maps in Normal T1single T1slow MWF T1fast 0 – 0.234 0 – 1172ms 0 – 2345ms 0 – 555ms 0 – 137ms 0 – 9.26ms 0 – 123ms 0 – 328ms T2fast Residence Time T2single T2slow

  9. 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

  10. 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.

  11. 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

  12. 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

  13. 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

  14. 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.

  15. 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.

  16. 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

  17. 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

  18. 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

  19. 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

  20. ISMRM 2011 E-Poster #7224 Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT 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.

  21. Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Purpose • To apply mcDESPOT, a whole-brain, myelin-selective, multi-component relaxometric imaging method, in a 1-year longitudinal pilot MS study • Assess the ability of the method to sense different rates of demyelination for different MS courses and compare it to changes in EDSS

  22. Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Study

  23. Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Processing Methods: 1-year & DVF • At 1-year, demyelinated voxels are based on z-scores with respect to the combined baseline and 1-year normal group • Find demyelinated volume fraction (DVF) • Sum the volume of demyelinated voxels and normalize by brain mask volume • # demy. voxels in compartment * voxel volume / compartment volume

  24. Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Results: Mean MWF in Whole Brain • Dotted line shows mean MWF for normals. Rank sum testing was done for each bar against this value • 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.

  25. Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Results: DVF Change • Colors denote subject type • Arrowheads indicate the direction of change and the DVF at 1-year • Dashed lines show subjects who also had a change in EDSS PPMS SPMS RRMS CIS Normals

  26. Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Results: DVF in Whole Brain • Dotted line shows mean demyelinated volume fraction change for normals • Definite MS patients are losing significantly more myelin than normals • Progressive patients have a greater rate of demyelination

  27. Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Discussion & Conclusions • The normal pool at 1-year is currently too small to show significance for the changes in mean MWF • DVF, however, is sensitive enough to show statistically significant changes in brain myelination over the study period • Progressive patients show greater disease decline that are not reflected in their EDSS disability score • EDSS and DVF measure different aspects of the disease. Patients with changes in EDSS did not actually have the largest demyelination changes

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