1 / 18

Anatomical MRI module

MNTP Trainee: Georgina Vinyes Junque , Chi Hun Kim Prof. James T. Becker Cyrus Raji , Leonid Teverovskiy , and Robert Tamburo. Anatomical MRI module. Voxel -Based Morphometry (VBM). Structural differences based on Voxel -wise comparision Advantages

aletta
Download Presentation

Anatomical MRI module

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. MNTP Trainee: Georgina VinyesJunque, Chi Hun Kim Prof. James T. Becker Cyrus Raji, Leonid Teverovskiy, and Robert Tamburo Anatomical MRI module

  2. Voxel-Based Morphometry (VBM) • Structural differences based on Voxel-wise comparision • Advantages • Automated, Un-biased, Whole brain analysis  compared to Manual ROI tracing • Well established and Widely used over the past decade • Results are biologically plausible and replicable • We know the LIMITATIONS

  3. Overview • Voxel-Based Morphometry Bias Field Correction Skull Stripping Spatial Normalization to Template Tissue Segmentation Modulation Smoothing Preprocessing Voxel-wise statistical tests

  4. Methods • MRI sequence • T1 (MPRAGE) • 3T Siemens TrioTim • Slices: 160; thickness 1.2mm • Voxel size: 1 x 1 x 1.2 mm • TE: 2.98; TR: 2300 • Software • SPM2 & SPM5 (Wellcome Trust Centre for Neuroimaging) • VBM2 toolbox (Gaser et al, http://dbm.neuro.uni-jena.de/) • N3 algorithm • Brain Extraction Tool in FSL • Watershed algorithm in FreeSurfer • Subjects • Multicenter AIDS Cohort Study (MACS) • 53 males • Age: 50.2 +- 4.4 • Statistical Analysis • Gray matter Volume differences • in Drug users vs. Non-Drug users

  5. MRI Bias Field Correction Original Image N3 Corrected Image Corrected Bias field = Original – Corrected image Software: N3 (Nonparametric Nonuniform intensity Normalization)

  6. Experiment 1. Adding ’Known’ Bias Field + Known Bias Field N3 Successful Removal of Known Bias field

  7. Experiment 2. ’Repetition’ of Bias Field Correction < Amount of Corrected Bias Field over N3 Repetition > N3 Mean Signal Intensity of Corrected Bias Field N3 Original image N3 N3 N3 # of repetition Corrected image After 5th repetition

  8. Skull Stripping Watersheddefault setting (30 min) BETdefault setting (1 min)  Optimization of Parameters (2min) • Software • Brain Extraction Tool (BET; v2.1 in FSL software package) • Watershed algorithm in FreeSurfer software package v5.1.0

  9. Skull Stripping: Brain Extraction via Deformable Registration Teverovskiy, 2011, OHBM, Poster Presentation

  10. Spatial Normalization to Template • Fitting each individual brain into the same brain template, To compare regional differences between groups • Customized template • Recommended in special populations (Eg: babies or the elderly). • Standardized template • Better comparison with similar studies using the same template. • Eg. MNI: 152 brains, mean age 25, female 43% http://dbm.neuro.uni-jena.de/vbm/vbm2-for-spm2/creating-customized-template/

  11. Effect of 3 Different Template on Statistical Results Customized template Default-MNI template MACS template Glass brains,showing reduced grey matter volume in drug users compared to non-drug users, at 0.01 Uncorrected level

  12. Segmentation into 3 Tissue Types 2. Tissue Probability Map 1. Signal Intensity of Voxel Grey Mater Segmentation White Mater Segmentation CSF Segmentation http://dbm.neuro.uni-jena.de/vbm/segmentation/

  13. Modulation • Recovering volume information which was lost by spatial normalization process. • It can be thought as atrophy correction. • It’s recommended if you are more interested in volume changes than differences in concentration (or density) http://dbm.neuro.uni-jena.de/vbm/segmentation/modulation/

  14. Effects of Modulation on Results Unmodulated: Changes in GM density Modulated: Changes in GM volume Glass brains showing reduced grey matter in drug users compared to non-drug users, at 0.01 Uncorrected level

  15. Smoothing • Intensity of every voxel is replaced by the weighted average of the surrounding voxels. Larger kernel size, more surrounding voxels • Make distribution closely to Gaussian field model • Increase the sensitivity of tests by reducing the variance across subjects • Reduce the effect of misregistration

  16. Effect of Different Smoothing Kernels 5 mm 10 mm 15 mm Glass brains showing reduced grey matter volume in drug users compared to non-drug users, at 0.01 Uncorrected level

  17. Conclusion • There’s a lot of options in processing that can affect data and results. • We have to undertand what we are doing in every step to better adjust options to our sample study. • Since these techniques have several pitfalls, we have to carefully interpret published results.

  18. Thank You • Prof. James T. Becker • TA: Cyrus Raji, Leonid Teverovskiy, Robert Tamburo • Prof. Seong-Gi Kim & Prof. Bill Eddy • Tomika Cohen, Rebecca Clark • Fellow MNTPers!

More Related