MSmcDESPOT

A look at the road behind and aheadOctober 30, 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

• 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 assumed 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

• All the following steps are achieved using the FMRIB Software Library (FSL)

• Throughout this presentation, we will focusing our attention on a single SPMS patient, affectionately known as P025

Preprocessing mcDESPOT – Step 1

1. Linear coregistration with trilinear interpolation (FSL FLIRT) – so that each voxel across all the images is the same piece of physical tissue

Preprocessing mcDESPOT – Step 2

2. Brain extraction from skull (FSL BET) – to reduce computation time

Preprocessing DTI – Step 1

• Similarly, the diffusion weighted images must also be coregistered for eddy current correction and brain extracted

Preprocessing DTI – Step 2

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

DTI Maps

Fraction Anisotropy Mean Diffusivity

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 and the 1mm2 FMRIB58 FA map, an average of FA maps from 58 subjects, each nonlinearly registered to the MNI brain

Postprocessing – mcDESPOT

• The mcDESPOT coregistration target for each subject is nonlinearly registered to the MNI brain and this warp field is in turn applied to the 10 maps

• The warp field is found with FSL’s FNIRT using an 8mm3 warp resolution

Standard Space Reg. – SPGR Target

• MNI

MNI152 2mm mcDESPOT SPGR Registration Target

Postprocessing – DTI

• The DTI FA map for each subject is nonlinearly registered to the FMRIB58 map

• Alternatively, we could register it to the mcDESPOT target and use the already computed warp field

Standard Space Reg. – FMRIB58 FA

Standard Space Reg. – DTI FA

Postprocessing – Clinical

• Each clinical scan for each patient is linearly registered to the mcDESPOT target

• Then the target->MNI warp is applied

Analysis

• Whole brain MWF, z-score based thresholding• Would like to move onto tissue-specific MWF

study, particularly in these types: WM, GM, NAWM (normal-appearing white matter), NAGM, and lesions only

• This brings us to the tricky issue of segmentation

Current Issues – Segmentation

• Lesion segmentation is proving to be a very difficult task

• Ultimately we’d like to use the lesion mask to subtract from our other tissue classifications to produce “normal-appearing” tissues

• Here’s some pictures of our results so far using FSL’s FAST with a variety of channels (SPGR, FLAIR, T2, PD)

Clinical-MPRAGE

WM Segmentation – SPGR 3 class

WM Segmentation – SPGR 4 class

WM Seg. – SPGR-FLAIR 4 class

GM Segmentation – SPGR 3 class

GM Segmentation – SPGR 4 class

Lesion Segmentation – SPGR 3 class

Lesion Seg. – SPGR-FLAIR 3 Class

CSF Segmentation – SPGR 3 Class

CSF Seg. – SPGR-T2-PD 3 class

Segmentation Questions

• What is the best way to obtain a lesion mask?• Should we do operations on the masks we

have, like subtracting the out-of-brain CSF from the SPGR-FLAIR 3 class CSF mask?

• Are there other existing tools out there for either automatic or semi-manual lesion segmentation?

Statistical Study

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

• Many of our variables are not intrinsically Gaussian so the t-test and ANOVA do not seem applicable

Open Questions

• DTI registration to standard space: FMRIB58 or via SPGR mcDESPOT target?

• Patient matching: gender or age first?• How to deal with lesions across patients:

Vrenken approach is to replace missing data with the mean of the group