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
Title: I prefer the wording “Compared to Atrophy Measures Alone” over “Compared to Only Atrophy Measures”
Are we allowed to change the title so that it’s different from the original abstract?
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. Study Demographic Data Healthy Controls All Patients CIS RRMS SPMS
mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone
ISMRM 2011 #4643
Study
Demographic Data
Healthy Controls
All Patients
CIS
RRMS
SPMS
PPMS N 26 10 5 6
Mean age, yr
(SD) 42
(13) 49
(12) 41 48 58
(7) 55
Male/Female ratio
10/16
7/19
3/7
0/5
0/6
4/1
Mean disease duration, yr — 14 2
(2) 15
(10) 28
(8) 20
Mean EDSS score
3.6
(2.4)
1.7
(0.9)
2.0
(1.7)
6.4
(1.1)
5.6

5. Scanning Methods 1.5T GE Signa HDx, 8-channel head RF coil
mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone
ISMRM 2011 #4643
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

6. The Technique

7. Processing Methods: MWF
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., Magn Reson Med Dec;60(6):

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

9. Processing Methods: Demyelination
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. Processing Methods: WM
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. Processing Methods: Lesions & DAWM
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. Processing Methods: NAWM & DVF
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. Segmentations and DV FLAIR WM NAWM DAWM Lesions MWF Demyelinated
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
After all that processing, we can visualize the end result in a very intuitive way.
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):

15. Results: Mean MWF in Compartments
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 <

16. Results: DVF in Compartments
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. Results: Correlations with EDSS
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. Results: Multiple Linear Regression
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. Discussion & Conclusions
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. Study Demographic Data Healthy Controls All Patients CIS RRMS SPMS
Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT
ISMRM 2011 #7224
Study
Demographic Data
Healthy Controls
All Patients
CIS
RRMS
SPMS
PPMS
N at baseline 26 10 5 6
N at 1-year 4 23 9
Mean age at baseline, yr
(SD) 42
(13) 49
(12) 41 48 58
(7) 55
Mean disease duration at baseline, yr — 14 2
(2) 15
(10) 28
(8) 20
Mean EDSS score at baseline
3.6
(2.4)
1.7
(0.9)
2.0
(1.7)
6.4
(1.1)
5.6
N with EDSS change 3 1

23. Processing Methods: 1-year & DVF
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. Results: Mean MWF in Whole Brain
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 <

25. Results: DVF Change Colors denote subject type
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. Results: DVF in Whole Brain
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
In previous bar charts, you used connecting brackets to show significant differences, but here you aren’t. Any reason for that?
There was no significance difference between CIS vs RR and RR vs SP despite the apparent visual differential in the bars.

27. Discussion & Conclusions
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