Whole Brain Myelin Imaging with mcDESPOT in Multiple Sclerosis July 18, 2012 Jason Su

Outline Introduction to parametric mapping Myelin imaging and MWF mcDESPOT measurement of 2-pool exchange mcDESPOT in multiple sclerosis Current and future challenges

What is Parametric Mapping? Start with a signal model for your data Collect a series of scans, typically with only 1 or 2 sequence variables changing Fit model to data Motivation Reveals quantifiable physical properties of tissue unlike conventional imaging Maps are ideally scanner independent Insert thumbnails of such maps? For example, DTI only changes the direction of the diffusion gradients T1 mapping gives an image in which the T1 longitudinal relaxation time is determined for every voxel in the volume, meaning each voxel has a unit associated with it (seconds). Relaxivity can tell you concentration of contrast via T1 and if measured over time, how fast agent is absorbed or removed, relevant in cancer tumors Maps should be scanner independent. But are they really? That is an interesting ongoing question.

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 You might call it FA or MD mapping from DTI since DTI is the acquisition strategy and sometimes all that people take from it are fiber tracking results, which I would not call parametric mapping. You might add T2 mapping

T1 mapping in multiple sclerosis T1 Mapping Motivation T1 mapping in multiple sclerosis DCE-MRI in tumors: [Gd] related to T1 grade II Here are some applications that use T1 mapping. In multiple sclerosis, the Gd enhancement of lesions can be quantitatively tracked with T1 over time, also seemingly normal appearing white matter shows elevated T1 values compared to normals. In DCE-MRI, the uptake of contrast agent in tumors can be tracked by use of T1 maps and knowledge of the relaxivity of the agent. The DCE-MRI example doesn’t absolutely require T1 mapping – a time course of T1-weighted images can be used and is probably more commonly used. But ideally you use dynamic quantitative T1 maps to feed into the tracer kinetic models. grade IV grade III Levesque et al. 2010 Tofts et al. 1999, 2003, Patankar et al. 2005

Relaxation Mapping T1 mapping T2 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 There are many different mapping methods, but each follows this paradigm of having a model and varying select parameters.

T1 Mapping: Inversion Recovery Acquire a series of images at different TIs, at each voxel fit curve across the data. Inversion or saturation recovery: 2DFT variant is slow, can be combined with FSE, single-shot imaging to accelerate Simple exponential recovery Gowland &Stevenson, in Tofts ed., QMRI of the Brain, 2003 Brix et al. MRI 1990; Ropele et al. MRM 1999; Wang et al. MRM 1987

DESPOT1 T1 mapping Christensen 1974, Homer 1984, Wang 1987, Deoni 2003 The method that’s of primary interest stems from the DESPOT1 method shown here. Peak is called the Ernst angle = acos(E1). Hope that gives a little bit of motivation for why parameter mapping is useful as well as how it is accomplished. Now I’ll delve a little further into the nitty gritty of steady state, DESPOT mapping methods. Christensen 1974, Homer 1984, Wang 1987, Deoni 2003

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 B1+ transmit field inhomogeneity – problem for >1.5T 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 Examine the integrity of myelin in the brain.

Myelin Water Fraction Recent 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) - qT2 is a well validated way to produce myelin water fraction maps which are representative of myelin in brain but limited by coverage and acq time - mcDESPOT is a newer technique that based around modeling relaxation as well and can produce maps that are conceptually similar to qT2's MWF, ie a long and short T2 pool in exchange, validation in progress Intra- and extra-cellular water, T2 ≈ 80ms Myelin water, T2 ≈ 20ms

Fractional Anisotropy map (3T), MWF (qT2, 3T) FA vs MWF FA and MWF-qT2 are of same normal subject MWF-mcD different 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 𝑓 𝐹 + 𝑓 𝑆 =1 Assume chemical equilibrium: 𝑓 𝐹 𝑘 𝐹𝑆 = 𝑓 𝑆 𝑘 𝑆𝐹 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 Chemical exchange means that no pool is actively growing, which is reasonable within a scanning session Eliminates 2 parameter to estimate, leaving 6 + B0/M0

mcDESPOT Model: SPGR SPGR equation Single Component 𝑆 𝑆𝑃𝐺𝑅 = 𝑀 0 1− 𝐸 1 sin⁡(𝛼) 1− 𝐸 1 cos⁡(𝛼) 𝐸 1 = e − 𝑇𝑅 𝑇 1 Here I’ve assumed TE is minimum close to 0 to eliminate that term. Importantly SPGR eliminates transverse magnetization which allows us to only need to treat longitudinal magn. and T1. In other words, SPGR allows us to separate T1 from T2 which is critical for getting DESPOT2 to work.

mcDESPOT Model: SPGR SPGR Equation Multi-Component 𝑆 𝑆𝑃𝐺𝑅 = 𝑀 0,𝑆𝑃𝐺𝑅 𝐼− 𝑒 𝐴 𝑆𝑃𝐺𝑅 𝑇𝑅 sin 𝛼 𝐼− 𝑒 𝐴 𝑆𝑃𝐺𝑅 𝑇𝑅 cos 𝛼 −1 𝑀 0,𝑆𝑃𝐺𝑅 = 𝑀 0 𝑓 𝐹 𝑓 𝑆 𝐴 𝑆𝑃𝐺𝑅 = − 1 𝑇 1,𝐹 − 𝑘 𝐹𝑆 𝑘 𝑆𝐹 𝑘 𝐹𝑆 − 1 𝑇 1,𝑆 − 𝑘 𝑆𝐹 M0 represents the longitudinal magn. of fast and slow pools

mcDESPOT Model: SPGR Single component fit of multi-component data f_F = .21; T1_F = 584; T1_S = 1400; T_FS = 67; Similarly there’s a matrix model for the SSFP equations, which is quite large because need to also treat transverse magnetization and I won’t go into it here. Deoni et al. 2008

The Technique Font is small, not very black, sometime you should re-create this slide in a much clearer simpler form

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 SRC is a perhaps lesser known optimization method that works by taking random samples from a range in the parameter space and gradually reducing the range until the solution is honed in on.

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

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.

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

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 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

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

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

Processing Methods: Deficient MWF mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 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 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 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-Appearing White Matter

Segmentations and DV FLAIR WM NAWM DAWM Lesions MWF Deficient MWF 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 Deficient MWF Voxels DV in NAWM DV in DAWM DV in Lesions

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 Parameter mapping allows for an explosion of variables which we can use to predict subject disability 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 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 Key idea is that we need whole brain mapping methods to allow computations like DVF against normal populations, MWF alone does not separate groups. This is one value of mcD over qT2.

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

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.

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 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

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 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 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.

Discussion & Conclusions Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 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 At 7T, nonuniformity is so great that there’s complete signal drop out, need a better solution that post-correction