ISMRM 2011 E-Poster
This presentation is the property of its rightful owner.
Sponsored Links
1 / 27

Declaration of Conflict of Interest or Relationship PowerPoint PPT Presentation


  • 79 Views
  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

Declaration of Conflict of Interest or Relationship

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Declaration of conflict of interest or relationship

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.


Background

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


Purpose

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)


Study

mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone

ISMRM 2011 #4643

Study


Scanning methods

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


The technique

The Technique


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


Mcdespot maps in normal

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


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


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.


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


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


Segmentations and dv

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


Statistical methods

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.


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 < 0.001.


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


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


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


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


Declaration of conflict of interest or relationship

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.


Purpose1

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


Study1

Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT

ISMRM 2011 #7224

Study


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


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 < 0.001.


Results dvf change

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


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


Discussion conclusions1

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


  • Login