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Declaration of Conflict of Interest or RelationshipI have no conflicts of interest to disclose with regard to the subject matter of this presentation.

MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES ALONEJ.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

ISMRM 2011 E-POSTER #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

MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES ALONEISMRM 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)


Study

Demographic Data Healthy Controls

All Patients CIS RRMS SPMS PPMS

N 26 26 10 5 6 5

Mean age, yr(SD)

42(13)

49(12)

41(12)

48(12)

58(7)

55(7)

Male/Female ratio 10/16 7/19 3/7 0/5 0/6 4/1

Mean disease duration, yr(SD)

—14

(13)2

(2)15

(10)28(8)

20(12)

Mean EDSS score(SD) —

3.6(2.4)

1.7(0.9)

2.0(1.7)

6.4(1.1)

5.6(1.1)


Scanning Methods• 1.5T GE Signa HDx, 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


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., Magn Reson Med. 2008 Dec;60(6):1372-87


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


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.

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


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-AppearingWhite Matter


Segmentations and DVFLAIR NAWM DAWM Lesions

MWF DemyelinatedVoxels

WM

DV in NAWM DV in DAWM DV in Lesions


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.

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.


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


Results: Correlations with EDSS• Lesion load correlates

poorly with EDSS

• PVF and DVF are stronger indicators of decline


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


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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