Multimodal MRI Analysis of White Matter Degeneration

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Multimodal MRI Analysis of White Matter Degeneration

  1. 1. Multimodal MRI Analysis of White Matter Degeneration Wang Zhan, Ph.D. Tel: 415-221-4810x2454, Email: Wang.Zhan@ucsf.edu Center for Imaging of Neurodegenerative Diseases UCSF / Radiology / VA Medical Center 01/08/2007 Medical Imaging Informatics, 2008 --- W. Zhan
  2. 2. Technical Issues for Multimodal Analysis • Different image resolutions • Different geometric distortions • Different imaging mechanisms (contrasts) • Different signal variations • Different signal linearity • Different noise levels • Different noise distributions
  3. 3. MRI Modalities on WM Degeneration • Traditional Imaging: (FLAIR, T2W, T1W, PD) Aging Multiple sclerosis Dementia (AD/MCI/FTD/SIVD) Depression Schizophrenia Bipolar disorder Celiac disease Hypertension Diabetes Stroke AIDS Cancer Brain injury • Diffusion Tensor Imaging: (FA, MD, Tractography) Aging Multiple sclerosis Dementia (AD/MCI/FTD/SIVD) Depression Schizophrenia Bipolar disorder Celiac disease Stroke AIDS Cancer Brain injury Medical Imaging Informatics, 2008 --- W. Zhan
  4. 4. Fluid Attenuated Inversion Recovery (FLAIR) Parameters at 4T: TR = 6000 (ms) TE = 355 (ms) TI = 2030 (ms) ( ) ( ) ( )0 11 2exp / 1 exp / 1 exp / 2MI M TI T TR T TE T ρ= − − + −   Hg Medical Imaging Informatics, 2008 --- W. ZhanRef: http://www.mr-tip.com/serv1.php E. Mark Haacke, et al., “Magnetic Resonance Imaging: Physical Principles and Sequence Design”, 1999, Springer Verlag Zhi-Pei Liang, Paul C. Lauterbur, “Principles of Magnetic Resonance Imaging: A Signal Processing Perspective”, 2004, IEEE
  5. 5. Traditional MRI Contrasts FLAIR T1W T2W PD CSF Gray Matter White Matter WM Lesion Krishnan et al., 2005, Duke Silvio Conte Center Medical Imaging Informatics, 2008 --- W. Zhan
  6. 6. Diffusion in 3-D: Homogeneous Medium X Y Z Water in a Homogeneous Medium Water Motion Diffusion ‘Sphere’
  7. 7. Diffusion in 3-D: White Matter X Y Z Water in an Oriented Tissue Water Motion Diffusion ‘Ellipse’
  8. 8. Diffusion Tensor Imaging FA MD B0 FA ( ) ( ) ( ) 2 2 2 1 2 3 2 2 2 1 2 3 3 2 MD MD MD FA λ λ λ λ λ λ − + − + − = + + g 1 2 3 3 MD λ λ λ+ + = Medical Imaging Informatics, 2008 --- W. Zhan WMH
  9. 9. FLAIR Group Analysis of Correlations (DTI ↔ FLAIR) Medical Imaging Informatics, 2008 --- W. Zhan Mean DTI Mean WML ( ) ( )( ) ( ) ( ) , i i i i i j DTI DTI FLAIR FLAIR Corr DTI FLAIR Var DTI Var FLAIR − − = × ∑ DTI S1 S2 S3 Sn
  10. 10. Correlations (DTI ↔WML Volume) cba FA↔WML MD↔WML MD↔WML Mean FA Mean FA WMH Medical Imaging Informatics, 2008 --- W. ZhanSubjects: N=47 (F=26), Age=77±6, MMSE=27.3±3.3, WML=11±16 (ml)
  11. 11. Effects of Image Misregistration? Correlation / WML DTI / T1 Template ? EPI Read Out PhaseEncoding Medical Imaging Informatics, 2008 --- W. Zhan
  12. 12. Modeling for WM Degeneration Normal WM Lesion Progression Pure CSF DTI (FA/MD) FLAIR (WMH) MPRAGE (T1 Dark) T2W (WMH) 1H Dens (WMH) Medical Imaging Informatics, 2008 --- W. Zhan
  13. 13. Two-Compartment Model of Relaxation 1/ (1 ) / /eff WM CSFT f T f T= − + CSF WM Relaxation Times: Lesion Progression: f = 0 ~ 1 Medical Imaging Informatics, 2008 --- W. Zhan (T1/T2) (T1/T2)
  14. 14. Fluid Attenuated Inversion Recovery (FLAIR) Parameters at 4T: TR = 6000 (ms) TE = 355 (ms) TI = 2030 (ms) ( ) ( ) ( )0 11 2exp / 1 exp / 1 exp / 2MI M TI T TR T TE T ρ= − − + −   Hg WMH Medical Imaging Informatics, 2008 --- W. Zhan
  15. 15. Multimodal Contrasts for WML Progression Noise-Free Noise-Contaminated Medical Imaging Informatics, 2008 --- W. Zhan
  16. 16. Two-Compartment Model of Diffusion CSF WM Lesion Progression: f = 0 ~ 1 Medical Imaging Informatics, 2008 --- W. Zhan (DWM) ( ) ( )0 (1 )exp expWM CSFS S f b f b= − − + −  D Dg gSlow exchange: (DCSF) [ ]{ }0 exp (1 ) WM CSFS S b f f= − − +D Dg g gFast exchange:
  17. 17. Diffusion Tensor Imaging (Slow-Exchange) SNR = 80 Noise free Medical Imaging Informatics, 2008 --- W. Zhan
  18. 18. Diffusion Tensor Imaging (Fast-Exchange) SNR = 80 Noise free Medical Imaging Informatics, 2008 --- W. Zhan
  19. 19. DTI (FA) ↔ WML (FLAIR) Correlations SNR= 80, b = 1000 s/mm2 Medical Imaging Informatics, 2008 --- W. Zhan
  20. 20. DTI (MD) ↔ WML (FLAIR) Correlations SNR= 80, b = 1000 s/mm2 Medical Imaging Informatics, 2008 --- W. Zhan
  21. 21. DTI (FA) ↔ T1 Dark (MPARGE) Correlations SNR= 80, b = 1000 s/mm2 Medical Imaging Informatics, 2008 --- W. Zhan
  22. 22. FLAIR Phantom Simulations (N=20) Medical Imaging Informatics, 2008 --- W. Zhan
  23. 23. Correlations (DTI ↔WML Volume) cba FA↔WML MD↔WML MD↔WML Mean FA Mean FA WMH Medical Imaging Informatics, 2008 --- W. ZhanSubjects: N=47 (F=26), Age=77±6, MMSE=27.3±3.3, WML=11±16 (ml)
  24. 24. Summaries •Multimodal MRI analysis with both FLAIR and DTI may provide extra information for characterizing WM degeneration process, which may not be captured by using either of them of alone. •Special technical issues should addressed properly for multimodal analysis, including image registration, signal nonlinearity, and noise effects, etc. •In traditional modalities, FLAIR shows a significant signal nonlinearity to the WM degeneration. FLAIR signal reaches its maximum around lesion severity of 0.7. •In DTI modalities, signal sensitivity and nonlinearity depend on the b value of diffusion weighting and the water exchange rate of issue compartments. Moreover, image noises may have heterogeneous effects on different DTI indices and lesion severities. •The correlations between FLAIR and DTI may change signs when come across the minimum magnitude of correlation at the maximum WML intensity.
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