Whole Brain Myelin Imaging with mcDESPOT in Multiple Sclerosis

Whole Brain Myelin Imaging with mcDESPOT in Multiple Sclerosis

July 18, 2012Jason Su

Outline1. Introduction to parametric mapping2. Myelin imaging and MWF3. mcDESPOT measurement of 2-pool exchange4. mcDESPOT in multiple sclerosis5. Current and future challenges

What is Parametric Mapping?

1. Start with a signal model for your data2. Collect a series of scans, typically with only 1 or

2 sequence variables changing3. Fit model to data

• Motivation– Reveals quantifiable physical properties of tissue

unlike conventional imaging– Maps are ideally scanner independent

Parametric Mapping• Some examples

– FA/MD mapping with DTI – most widely known mapping sequence

– T1 mapping – relevant in study of contrast agent relaxivity and diseases

– B1 mapping – important for high field applications

T1 Mapping MotivationT1 mapping in multiple sclerosis

grade IV

grade II

grade III

DCE-MRI in tumors:[Gd] related to T1

Levesque et al. 2010 Tofts et al. 1999, 2003, Patankar et al. 2005

Relaxation Mapping• T1 mapping

– IR SE – gold standard, vary TI– Look-Locker – use multiple readout pulses to collect many

TIs– DESPOT1 – vary flip angle

• T2 mapping– Dual SE – vary TE– CPMG – use multiple spin echoes to collect many TEs– DESPOT2 – vary flip angle

T1 Mapping: Inversion Recovery

Gowland &Stevenson, in Tofts ed., QMRI of the Brain, 2003Brix et al. MRI 1990; Ropele et al. MRM 1999; Wang et al. MRM 1987

1e10TTIMS

DESPOT1 T1 mapping

Christensen 1974, Homer 1984, Wang 1987, Deoni 2003

S M0 sin e TE T2* 1 e TR T1

1 cos e TR T1

DESPOT Methods• Vary flip angle in steady state sequences like SPGR and SSFP• Fast, whole brain, higher resolution 1-2mm isotropic• Requires accurate knowledge of flip angle

1. B1+ transmit field inhomogeneity – problem for >1.5T2. Excitation slab profile – typically known and accounted for

• DESPOT1 – T1 mapping– DESPOT-HIFI – add an inversion to allow T1 and B1+ mapping

• DESPOT2 – T1 and T2 mapping– DESPOT-FM – collect multiple SSFP phase cycles to map B0

• mcDESPOT – multi-component T1 and T2 mapping

Relaxation Based Myelin Imaging• DTI is not an ideal measure

of myelin (low resolution, crossing fibers problem)

• T2 (or R2) has been used in the past as a crude correlate of myelin– Myelination reduces water

content in brain, lower T2 – T2w FLAIR is used in MS to

highlight lesions– T2 mapping gives a more

sensitive indicator

Myelin Water FractionRecent methods have focused on a more specific measure: myelin water fraction (MWF)• Multiecho qT2 – vary TE, decomposes the

signal into a spectrum of T2 times (UBC, MacKay)

– Well validated way to produce MWF maps that represent myelin

– Few slices, long acquisition time• mcDESPOT – vary flip angle, models SPGR

and SSFP steady state signal– Also based on modeling relaxation and two pool

exchange– Validation in progress– High resolution, whole brain, but long

processing time (24 hours)

• Intra- and extra-cellular water, T2 ≈ 80ms

• Myelin water, T2 ≈ 20ms

FA vs MWF

Fractional Anisotropy map (3T), MWF (qT2, 3T) MWF (1.5T, mcDESPOT)

mcDESPOT• Models tissue as two water pools in exchange

– Fast relaxing water pool– Slow relaxing water pool

• Assume chemical equilibrium:

• The SPGR and SSFP signal equations must be adapted to take into account this model

T1,F T2,F

fF

kFS

T1,S T2,S

fS

kSF

mcDESPOT Model: SPGR• SPGR equation

– Single Component

mcDESPOT Model: SPGR• SPGR Equation

– Multi-Component

mcDESPOT Model: SPGR• Single component fit of multi-component data

Deoni et al. 2008

mcDESPOT Model Fitting• Expensive non-linear curve fitting problem

– 24 hour per 2mm isotropic brain with 12-core CPU

• Previous implementations used genetic algorithms

• Currently using stochastic region of contraction

mcDESPOT Maps in NormalT1single T1fast MWF

T2single T2slow

T1slow

T2fast Residence Time

0 – 0.234

0 – 137ms

0 – 555ms

0 – 9.26ms

0 – 1172ms

0 – 123ms

0 – 2345ms

0 – 328ms

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

ControlsAll

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: Deficient MWF• 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 deficient, i.e. MWF < -4σ below the mean

Deficient MWF 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 deficient MWF volume fraction (DVF)– Sum the volume of deficient

voxels in each tissue compartment and normalize by the compartment’s volume

– # deficient voxels in compartment * voxel volume / compartment volume

Normal-AppearingWhite Matter


Segmentations and DVFLAIR NAWM DAWM Lesions

MWF Deficient MWFVoxels

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 deficient

MWF 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


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

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

ISMRM 2011 E-POSTER #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.

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


Normals

CIS

RRMS

SPMS

PPMS

Results: DVF in Whole Brain• Dotted line shows mean

deficient MWF volume fraction change for normals

• Definite MS patients are losing significantly more myelin than normals

• Progressive patients have a greater rate of DVF increase


Discussion & Conclusions• DVF shows 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 appear to measure different aspects of the disease.– Patients with changes in EDSS did not actually have the

largest DVF changes


Current and Future Work• High-Field mcDESPOT

– 3T: 6 min acq. @ 2mm isotropic, post-correction with a B1+ map is sufficient

– 7T: k-T points pulse design is showing promise in flattening the transmitted field

• Accelerated mcDESPOT– DISCO-based view-sharing working with DESPOT1

• SSFP (DESPOT2) more challenging

• Possible new applications– Alzheimer’s Disease: the myelin hypothesis– Traumatic brain injury– Novel segmentation

Whole Brain Myelin Imaging with mcDESPOT in Multiple Sclerosis

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