1 / 25

Multimodal MRI Analysis of White Matter Degeneration

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.

renee-roth
Télécharger la présentation

Multimodal MRI Analysis of White Matter Degeneration

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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. 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. 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 MRI Modalities on WM Degeneration Medical Imaging Informatics, 2008 --- W. Zhan

  4. Fluid Attenuated Inversion Recovery (FLAIR) Parameters at 4T: TR = 6000 (ms) TE = 355 (ms) TI = 2030 (ms) 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 Ref: http://www.mr-tip.com/serv1.php Medical Imaging Informatics, 2008 --- W. Zhan

  5. FLAIR T1W PD T2W CSF Gray Matter White Matter WM Lesion Traditional MRI Contrasts Krishnan et al., 2005, Duke Silvio Conte Center Medical Imaging Informatics, 2008 --- W. Zhan

  6. X Z Y Diffusion ‘Sphere’ Diffusion in 3-D: Homogeneous Medium Water in a Homogeneous Medium Water Motion

  7. X Z Y Diffusion ‘Ellipse’ Diffusion in 3-D: White Matter Water in an Oriented Tissue Water Motion

  8. MD FA FA B0 Diffusion Tensor Imaging WMH Medical Imaging Informatics, 2008 --- W. Zhan

  9. S1 S2 S3 Sn Group Analysis of Correlations (DTI ↔ FLAIR) DTI FLAIR Mean DTI Mean WML Medical Imaging Informatics, 2008 --- W. Zhan

  10. FA↔WML MD↔WML MD↔WML a c b WMH Mean FA Mean FA Correlations (DTI ↔WML Volume) Subjects: N=47 (F=26), Age=77±6, MMSE=27.3±3.3, WML=11±16 (ml) Medical Imaging Informatics, 2008 --- W. Zhan

  11. EPI Read Out Phase Encoding ? Effects of Image Misregistration? DTI / T1 Template Correlation / WML Medical Imaging Informatics, 2008 --- W. Zhan

  12. Pure CSF Normal WM Lesion Progression MPRAGE (T1 Dark) 1H Dens (WMH) T2W (WMH) DTI (FA/MD) FLAIR (WMH) Modeling for WM Degeneration Medical Imaging Informatics, 2008 --- W. Zhan

  13. CSF WM Two-Compartment Model of Relaxation (T1/T2) (T1/T2) Lesion Progression: f = 0 ~ 1 Relaxation Times: Medical Imaging Informatics, 2008 --- W. Zhan

  14. Fluid Attenuated Inversion Recovery (FLAIR) Parameters at 4T:TR = 6000 (ms) TE = 355 (ms) TI = 2030 (ms) WMH Medical Imaging Informatics, 2008 --- W. Zhan

  15. Multimodal Contrasts for WML Progression Noise-Contaminated Noise-Free Medical Imaging Informatics, 2008 --- W. Zhan

  16. CSF WM Two-Compartment Model of Diffusion (DCSF) (DWM) Lesion Progression: f = 0 ~ 1 Slow exchange: Fast exchange: Medical Imaging Informatics, 2008 --- W. Zhan

  17. Diffusion Tensor Imaging (Slow-Exchange) Noise free SNR = 80 Medical Imaging Informatics, 2008 --- W. Zhan

  18. Diffusion Tensor Imaging (Fast-Exchange) Noise free SNR = 80 Medical Imaging Informatics, 2008 --- W. Zhan

  19. DTI (FA) ↔ WML (FLAIR) Correlations SNR= 80, b = 1000 s/mm2 Medical Imaging Informatics, 2008 --- W. Zhan

  20. DTI (MD) ↔ WML (FLAIR) Correlations SNR= 80, b = 1000 s/mm2 Medical Imaging Informatics, 2008 --- W. Zhan

  21. DTI (FA) ↔ T1 Dark (MPARGE) Correlations SNR= 80, b = 1000 s/mm2 Medical Imaging Informatics, 2008 --- W. Zhan

  22. FLAIR Phantom Simulations (N=20) Medical Imaging Informatics, 2008 --- W. Zhan

  23. FA↔WML MD↔WML MD↔WML a c b WMH Mean FA Mean FA Correlations (DTI ↔WML Volume) Subjects: N=47 (F=26), Age=77±6, MMSE=27.3±3.3, WML=11±16 (ml) Medical Imaging Informatics, 2008 --- W. Zhan

  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.

  25. Literatures [1] Le Bihan D, Breton E, Lallemand D, et al. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 1986; 161:401-407. [2] Pfefferbaum A, Sullivan EV, Hedehus M, et al., Age-related decline in brain white matter nisotropy measured with spatially corrected echo-planar diffusion tensor imaging. agn Reson Med 2000;44(2):259-8. [3] Lansberg MG, Albers GW, Beaulieu C, Marks MP. Comparison of diffusion-weighted MRI and CT in acute stroke. Neurology 2000;54(8):1557-61. [4] Meier-Ruge W, Ulrich J, Bruhlmann M, Meier E. Age-related white matter atrophy in the human brain. Ann N Y Acad Sci 1992;673:260-9. [5] Awad IA, Johnson PC, Spetzler RF, Hodak JA. Incidental subcortical lesions identified on magnetic resonance imaging in the elderly. II. Postmortem pathological correlations. Stroke 1986;17(6):1090-7. [6] Breteler MM, van Swieten JC, Bots ML, et al. Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study: the Rotterdam Study. Neurology 1994;44(7):1246-52. [7] Yoshita M, Fletcher E, Harvey D, et al., Extent and distribution of white matter hyperintensities in normal aging, MCI, and AD. Neurology 2006;67(12):2192-8. [8] DeCarli et al, Neurobiology of Aging, 1007 (In Press). [9] Smith SM, Jenkinson M, Woolrich MW, et al., Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 2004, 23(S1):208-219. [10] Smith SM, Jenkinson M, Johansen-Berg H, et al., Tract-based spatial statistics: Voxelwise analysis multi-subject diffusion data. NeuroImage 2006, 31:1487. [11] Hadjidemetriou S, Studholme C, Mueller S, et al., Restoration of MRI data for field nonuniformities using high order neighborhood statistics. Proc. Of SPIE Medical Image Processing, 6512, 2007. [12] Boykov Y, Veksler O, and Zabih R, Fast approximate energy minimization via graph cuts. IEEE Trans. on AMI, 23(11):1222–1239, 2001. [13] Christensen GE, Rabbitt RD, and Miller MI. Deformable templates using large deformation kinematics. Image Processing, IEEE Transactions on, 5(10):1435-1447, 1996. [14] Joshi S, Lorenzen P, Gerig G, and Bullitt E, Structural and radiometric asymmetry in brain images. Med Image Anal, 7(2):155-170, 2003. [15] Neter, J, Wasserman, W, Kutner, MH. Applied Linear Statistical Models: Regression, Analysis of Variance, and Experimental Designs, 3rd edition. Richard d Irwin Publishing. 1990. [16] Kamman RL, Go KG, Brouwer WH, Berendsen JC. Nuclear magnetic resonance relaxation in experimental brain edema: Effects of water concentration, protein concentration, and temperature. Med. Reson. Med. 6 (3): 265-274, 1988.

More Related