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Whole Brain Myelin Imaging with mcDESPOT in Multiple Sclerosis

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?.

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Whole Brain Myelin Imaging with mcDESPOT in Multiple Sclerosis

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

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

  3. 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

  4. 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

  5. T1 Mapping Motivation T1 mapping in multiple sclerosis DCE-MRI in tumors: [Gd] related to T1 grade II grade IV grade III Levesque et al. 2010 Tofts et al. 1999, 2003, Patankar et al. 2005

  6. 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

  7. T1 Mapping: Inversion 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

  8. DESPOT1 T1 mapping Christensen 1974, Homer 1984, Wang 1987, Deoni 2003

  9. 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

  10. 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

  11. 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) • Intra- and extra-cellular water, T2 ≈ 80ms • Myelin water, T2 ≈ 20ms

  12. FA vs MWF Fractional Anisotropy map (3T), MWF (qT2, 3T) MWF (1.5T, mcDESPOT)

  13. 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

  14. mcDESPOTModel: SPGR • SPGR equation • Single Component

  15. mcDESPOT Model: SPGR • SPGR Equation • Multi-Component

  16. mcDESPOT Model: SPGR • Single component fit of multi-component data Deoni et al. 2008

  17. 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

  18. 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

  19. 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.

  20. 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

  21. 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)

  22. mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Study

  23. 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

  24. 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

  25. 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

  26. 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.

  27. 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

  28. 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

  29. 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 Deficient MWF Voxels DV in NAWM DV in DAWM DV in Lesions

  30. 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.

  31. 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.

  32. 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

  33. 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

  34. 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

  35. 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

  36. 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.

  37. 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.

  38. 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

  39. 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

  40. 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

  41. 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

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