MSmcDESPOT

A Brief SummaryApril 2, 2009

The Technique• mcDESPOT (multi-component driven equilibrium

single pulse observation of T1/T2) is a quantitative MR technique that characterizes many of the key parameters relevant to MRI

• A series of spoiled gradient echo (SPGR) and phase-cycled steady-state free precession (SSFP) scans are collected at different sets of flip angles

• The signal from a single voxel across all these scans is modeled as the combination of two different pools of water, a fast and slow pool in exchange with each other

The Technique

The Technique

Comparison of acquired data and single- and two-component SPGR and bSSFP signal intensity vs. flip angle curves in four different brain regions (shown by the box outlines). Plotted data points correspond to the mean values obtained from the ROI, and the error bars represent 1 SD. In all regions, the two-component model is observed to more closely agree with the acquired multiangle SPGR and bSSFP data.

• A fitting algorithm (stochastic region of contraction) computes the optimal set of parameters that characterizes the observed signal at each voxel in the brain

The Technique• The final result is a set of 10 maps defining MR parameters throughout the

entire brain:– Fast pool T1, T2, and residence time– Slow pool T1 and T2– Single pool T1, T2, and M0 – this is when we do not model each voxel as the sum of

two pools– B0 off-resonance– Fast volume fraction – this is how much each pool contributes to a voxel’s signal or

alternatively, what fraction of a voxel is occupied by each pool• We attribute the fast pool to water trapped between the lipid bilayers of the

myelin sheath, while the slower-relaxing species is believed to correspond to the less restricted intra- and extracellular pools– This needs further histological verification but we will continue under this premise– Thus we rename the fast volume fraction to the “myelin water fraction” (MWF), our

key parameter of interest

The Study• Given this technique, which we believe can characterize myelination

in the brain, we move our sights to examine a disease that is characterized by demyelination: multiple sclerosis

• 23 normals + 2 pending• 25 MS patients, 5 in each of 5 classes (low-risk CIS, high-risk CIS, RR,

SP, PP)• Each scanned at 1.5T to avoid B1 inhomogeneity and flip angle

inaccuracy:– mcDESPOT protocol at 2mm3 isotropic– 32-direction DTI sequence at 2.5mm3

– T2/PD FSE at 0.43mm2 in-plane and 6mm slice resolution– FLAIR at 0.86 mm2 in-plane and 3mm slice resolution– MPRAGE pre and post Gd constrast for patients at 1mm3

Preprocessing for mcDESPOT

• Prior to running the fitting algorithm, we must run the SPGR and SSFP images through a preprocessing pipeline

• Using the FMRIB Software Library (FSL)1. Linear coregistration – so that each voxel across

all the images is the same piece of physical tissue

2. Brain extraction from skull– to reduce computation time

Processing

• Now that the data is all prepared, it is run through the parameter fitting program

• The mcDESPOT volumes are processed with our own code

• The diffusion volumes are fitted with FSL’s dtifit

mcDESPOT Maps

mcDESPOT Maps - MWF

Postprocessing

• Postprocessing involves bringing these various maps and scans into a standard space so that they can be compared with each other on a voxel per voxel basis

• We use the 2mm2 MNI152 T1 standard space template

Standard Space Reg. – SPGR Target

• MNI

MNI152 2mm mcDESPOT SPGR FA 13 Registration Target

Analysis

• Initially, whole brain MWF, z-score based thresholding

• Wanted to move onto tissue-specific MWF study, particularly these types: WM, GM, NAWM (normal-appearing white matter), NAGM, and lesions only

• This brings us to the tricky issue of segmentation

Segmentation

• Difficulty of needing both lesion and tissue classification

• SPM gives WM/GM from MPRAGE but noisy• Lesion pre-selection through a voxel-based

FLAIR analysis– Defined two lesion classes: lesion cores and

penumbra/DAWM• Fairly extensive manual editing

Tissue Segmentation

• Initial issues with using mcDESPOT SPGR target as segmentation

• Tried permutations of multi-component segmentation (SPGR, FLAIR, T2, PD)

• Discussed with Allan Reiss’s group– 2mm3 too low res., 1mm3 MPRAGEs better but

more noisy– Homogeneity correction with SPM

P018 Uncorrected MPRAGE

P018 Corrected MPRAGE

Tissue Segmentation – Raw WM

Tissue Segmentation – Filtered WM

Tissue Segmentation – Edited WM

Tissue Seg. – WM Comparison

• FAST WM vs. Edited SPM WM• I don’t think FAST does as bad a job as the Reiss

group suggested

Lesion Seg. – Core Pre-selection

Lesion Segmentation – Edited Cores

Lesion Seg. – Penumbra/DAWM

More Analysis

• With these tissues defined, we can now analyze more complex compartments:– WM– NAWM = WM – lesion penumbra – cores– DAWM = lesion penumbra – cores– Lesion cores, focal demyelination

P018 mcDESPOT – Edited SPM WM

P018 mcDESPOT – NAWM

P018 mcDESPOT – DAWM

P018 mcDESPOT – Lesion Cores

P018 mcDESPOT – MWF

P018 mcDESPOT – MWF

Correlation with EDSSDemyelinated Volume in Whole Brain vs. EDSS

R2 = 0.4036

1

10

100

1000

10000

100000

1000000

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0EDSS

Dem

yelin

ated

Vol

ume

(mm

3)

Low-Risk CIS High-Risk CIS RRMS SPMS PPMS

Correlation with EDSSDemyelinated Volume in White Matter vs. EDSS

R2 = 0.4105

1

10

100

1000

10000

100000

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0EDSS

Dem

yelin

ated

Vol

ume

(mm

3)

Low-Risk CIS High-Risk CIS RRMS SPMS PPMS

Correlation with EDSSDemyelinated Volume in NAWM vs. EDSS

R2 = 0.4735

1

10

100

1000

10000

100000

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0EDSS

Dem

yelin

ated

Vol

ume

(mm

3)

Low-Risk CIS High-Risk CIS RRMS SPMS PPMS

Correlation with EDSSLesion Load vs. EDSS

R2 = 0.2449

0

20000

40000

60000

80000

100000

120000

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0EDSS

Lesi

on L

oad

(mm

3)

Low-Risk CIS High-Risk CIS RRMS SPMS PPMS

Statistical Methods

• We intend to use the Wilcoxon rank sum test as our workhorse for statistical comparison

• This is essentially a non-parametric version of t-test, comparing medians instead of means

• Unsure about the distribution of MWF and hard to determine with such small sampling

Preliminary Statistical Results• These is are old statistics using

old tissue classes (i.e. whole brain only)

• p-values from Wilcoxon rank sum test, reject null hypothesis if < .05

• First 6 rows compare patient populations vs normals

• Significant results:– Demyelinated voxel volume can

identify CIS patients– No difference between low and

high-risk CIS patients according to these measures

Demyelinated Voxel

Volume

Brain Fraction (WM+GM)/(WM+GM+

CSF)

All Patients 0.00000 0.01122

Low-Risk CIS 0.00072 0.84525

High-Risk CIS 0.00072 0.84525

All CIS 0.00001 1.00000

SPMS 0.00027 0.00098

PPMS 0.00027 0.00148RRMS vs.

SPMS 0.00866 0.01732Low vs high-

risk CIS 1.00000 0.54762

Open Questions and Future Work

• Other interesting measures– ROI Histogram-based: peak location, skewness,

kurtosis– Spatial distribution, true voxel-based analysis

• Further statistical analysis– Multiple variable regression (ANOVA) – worried

about normality constraints– Model selection

Questions?