MS Lesion CharacterizationA Multi-Compartment T2-Relaxometry Approach

June 2016

Sudhanya Chatterjee1, Olivier Commowick1, Simon Warfield2, Christian Barillot1

1VisAGeS INRIA/IRISA U746, F-35042 Rennes, France2Boston Children’s Hospital, Boston, MA 02115, USA

Contents

Introduction

Method

Observations

Future Work

References

Introduction— Problem Motivation

— Problem Description

“The association between common neuroradiological markers of multiple sclerosis (MS) and clinical disability is weak, a phenomenon known as the clinico-radiological paradox”[Hackmack K.,J Neurol (2012)]

Problem Motivation

—MRI in Multiple Sclerosis (MS)↗Pro: High Sensitivity for depiction of Plaques.

↘Con: Inability to differentiate MS plaques due to heterogeneous pathologic substrates of individual lesions [Rovira et al.2015]

—No single test* can provide a definite diagnosis of MS [Rovira et al.2015].

—MAGNIMS Consensus (Evidence-Based) Guidelines report of 2015 states [Rovira et al.2015]

—Early evidence suggests advanced MRI can predict disability progression.

—BUT no significant evidence to support its use for initial or differential diagnosis of MS.

* Includes tissue biopsyRovira À. et al. (2015). Nature Reviews Neurology. “Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis clinical implementation in the diagnostic process.”

Problem Description

—Design Framework to utilize advanced MRI techniques for better characterization to MS

—Heterogeneous onset/existence represents a major challenge—Quantify the Inflammation & Degeneration aspects of the disease.

—Advanced MRI technique under study: T2-Relaxometry

—Propose a multi-compartment T2-Relaxometry model based analysis to quantify the MS lesion in a patient.

—Myelin Water Fraction—How critical are other compartments?

→Free Fluids→Axons & Cells

Myelin Water Fraction

—[Mackay et al.1994] shows significant Myelin reduction in NAWM —Myelin is predominantly present in WM

—Myelin Water component has short 𝑇2 10 − 55𝑚𝑠

—[Whittall et al.1997] provides proof of claim: 𝑇2 information differentiates Healthy & Pathologic brain tissues

—Multi-exponential nature of 𝑇2 was concluded

—[Laule et al.2006] proves correlation b/w MWF & MS using histopathological evidences.

—MWF & Short-𝑇2 signal:—Myelin is a tightly wrapped & lipid-rich anatomy

MethodInhomogeneity Correction

Multi-Compartment Model

Inhomogeneity Correction

—Multicomponent T2 analysis assumes exponential decay

—Refocusing pulses are often significantly different* from 180⁰– Some Magnetization is trapped along the longitudinal axis

– Pure Exponential Decay: No more true

—Hennig J., “Multiecho Imaging Sequences with Low Refocusing Flip Angles”

– Stimulated echo: 𝐸𝑃𝐺(𝛼, 𝑇2, 𝑇1, ∆𝑡𝑒𝑐ℎ𝑜)

𝑆𝑇𝐸 =

0

∞

𝑔 𝜇, 𝜎; 𝑇2 𝐸𝑃𝐺 𝛼, 𝑇2, 𝑇1, ∆𝑡𝐸𝑐ℎ𝑜 𝑑𝑇2

*Inhomogeneities in B1 transmit field

Multi-Compartment Model

𝑆𝑇𝐸 =

0

∞

𝑔 𝜇, 𝜎; 𝑇2 𝐸𝑃𝐺 𝛼, 𝑇2, ∆𝑡𝐸𝑐ℎ𝑜 𝑑𝑇2

𝑔 𝜇, 𝜎; 𝑇2 : Model for 𝑇2 signal assumed.𝜇, 𝜎 : Model Parameters*

— Three compartments assumed with same distribution

𝒈 𝝁, 𝝈; 𝑻𝟐 = 𝒋={𝟏,𝟐,𝟑}𝒘𝒋 𝒈𝒋 𝝁𝒋, 𝝈𝒋; 𝑻𝟐 s.t. 𝑗𝑤𝑗 = 1

𝑔𝑗 𝜇𝑗 , 𝜎𝑗; 𝑇2 =1

2𝜋𝜎𝑗2

exp− 𝑇2 − 𝜇𝑗

2

2𝜎𝑗2

*In this case, Gaussian with mean (𝜇) and Variance (𝜎) as parameters

Representation µ σ

Myelin 20 5

Axon-Cells 100 10

Free-Fluids 2000 80

Observations— Data Specifications

— Lesion Study

— Lesion vs. Contralateral Study

Data Set Analyzed

• MS-SPI Data Set

• For each patient: 3 Acquisitions: 00mo – 12mo – 24mo

• GADO data also available*

• Analyzed Weights for 5 patients (15 datasets)

11 Echoes

‒ Acquisition Duration: 527291.0ms (approx. 8.78 minutes)‒ First echo: 8.4ms‒ Echo spacing: 8.4ms‒ Resolution: 1.3mm x 1.3mm x 3.0mm‒ Last echo: 92.4ms* Supplementary Info: Standard segmented spin-echo sequence. CHU Rennes -

SIEMENS - Verio 3.0T (MR)

*Radiologist Confirmed GADO Lesions in 5 patient cases

Weights• At Lesions:

• Presence of Free-Fluids

• Absence of Myelin

Temporal Study

—Temporal Trend:– Myelin Decreases– Axon-Cell Decreases– Free-Fluid increases

—Demyelination does not recover

—Axons & other cells deteriorate.

—Fluid accumulation increases over time.

Insights into the Weights in RoI

Contralateral Study

Example#1

16-004-Lesion-7

16-004-Lesion-7

Contralateral RoI

Lesion RoI

16-004-Lesion-7

16-004-Lesion-7

Example#2

16-010-Lesion-5

Contralateral RoI

Lesion RoI

16-010-Lesion-5

16-010-Lesion-5

Future Work— Method

— Analysis

Signal Model

Choice of Signal Model

—Skewed distribution for Myelin– Left Skewed distribution for

Myelin(?)

– Gaussian has a –ve tail

– Gamma Distribution as an alternative

– Observe differences

Estimation of Model Parameters

—Estimation of Signal Model’s parameters

– Multi-parametric Model Hierarchical vs Simultaneous

Estimation

Outlier Model for Free Fluids(?): FF data not in hand by virtue of the acquisition

Analysis

—How to get a verdict on Lesion Characteristics?

—Comparison with healthy controls– Same Sequence Parameters

– Same scanner preferably

—Healthy Subjects– Atlas based comparison w/ patient

– Same RoI comparison

For Further Reading

[Akhondi-Asl et al.2015] Akhondi-Asl, A., Afacan, O., Balasubramanian, M., Mulkern, R. V., and Warfield, S. K.(2015).Fast myelin water fraction estimation using 2d multislicecpmg. Magnetic resonance in medicine.

[Assaf and Pasternak2008] Assaf, Y. and Pasternak, O. (2008). Diffusion tensor imaging (dti)-based white matter mapping in brain research: a review. Journal of molecular neuroscience, 34(1):51–61.

[Enzinger et al.2015] Enzinger, C., Barkhof, F., Ciccarelli, O., Filippi, M., Kappos, L., Rocca, M. A., Ropele, S., Rovira, À., Schneider, T., de Stefano, N., et al. (2015). Nonconventional mri and microstructural cerebral changes in multiple sclerosis. Nature Reviews Neurology, 11(12):676–686.

[Jeurissen et al.2013] Jeurissen, B., Leemans, A., Tournier, J.-D., Jones, D. K., and Sijbers, J. (2013). Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Human brain mapping, 34(11):2747–2766.

[Laule et al.2006] Laule, C., Leung, E., Li, D. K., Traboulsee, A., Paty, D., MacKay, A., and Moore, G. R. (2006). Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology. Multiple Sclerosis, 12(6):747–753.

[Mackay et al.1994] Mackay, A., Whittall, K., Adler, J., Li, D., Paty, D., and Graeb, D. (1994). In vivo visualization of myelin water in brain by magnetic resonance. Magnetic Resonance in Medicine, 31(6):673–677.

[Rovaris et al.2005] Rovaris, M., Gass, A., Bammer, R., Hickman, S., Ciccarelli, O., Miller, D., and Filippi, M. (2005). Diffusion mri in multiple sclerosis. Neurology, 65(10):1526–1532.

[Rovira et al.2015] Rovira, À., Wattjes, M. P., Tintoré, M., Tur, C., Yousry, T. A., Sormani, M. P., De Stefano, N., Filippi, M., Auger, C., Rocca, M. A., et al. (2015). Evidence-based guidelines: Magnims consensus guidelines on the use of mri in multiple sclerosis [mdash] clinical implementation in the diagnostic process. Nature Reviews Neurology.

[Scherrer et al.2015] Scherrer, B., Schwartzman, A., Taquet, M., Sahin, M., Prabhu, S. P., and Warfield, S. K. (2015). Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (diamond). Magnetic resonance in medicine.

[Valk and Van der Knaap2005] Valk, J. and Van der Knaap, M. (2005). Magnetic resonance of myelin, myelination and myelin disorders. Science, 246:815–818.

[Whittall et al.1997] Whittall, K. P., Mackay, A. L., Graeb, D. A., Nugent, R. A., Li, D. K., and Paty, D. W. (1997). In vivo measurement of t2 distributions and water contents in normal human brain. Magnetic Resonance in Medicine, 37(1):34–43.

Thank You