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									LONI DTI Suite
   Kristi Clark
  July 30, 2009
                    DTI: Driving biological principles

A) Post-mortem freezing/dissection technique                       B) Water diffuses faster parallel to WM tracts




 A) Lateral view of the internal structures of the left cerebral hemisphere (reprinted from, Ludwig E, Klingler J:
 Atlas Cerebri Humani. Basel, S. Karger, 1956).
 B) Reinges et al., Eur J Radiol. 2004 Feb;49(2):91-104. Review.
 Things to know before starting an analysis
 What is the question that you want to answer with the
data
    Usually you will want to compare two (or more) populations or look at
   the effect of a continuous variable such as age
    MD, FA,  ,&   are all commonly used metrics that reflect underlying
   white matter integrity
    From tractography: can compute length, volume, mean FA, etc.
 What are the directions
    These are important to determine the preferred direction of diffusion
    Not usually in the DICOM data, sometimes wrong, so you need to know
   these objectively
    When in doubt, ask your local physicist
    If they are incorrect, the only thing it should affect is tractography &
   color maps
 What are the b values (diffusion weighting)
    This is the strength with which the diffusion sensitizing pulses were
   applied: important because this affects all calculations, including MD
    Usually you can find this on the scanner itself
                 Where to find tools
 http://www.loni.ucla.edu/twiki/bin/view/Main/KristiClark
 From the website, download: source code, compiled binaries
for linux, generic workflows, an example, & modules.pipe
                   LONI DTI Tools
 Data needs to be in either DICOM or Analyze format
(3D or 4D)
 4 atlases available, can substitute any atlas
 Can now do whole-brain fiber tracking once per
subject, and there are utilities to then work with the .ucf
    For example: OR, AND, NOT functions can be either
   images or coordinates (with radii)
    Also there are utilities to compute the histogram,
   and also do a length selector with a min and max
   number of points
                            Example 1
 Dataset 1: DICOM format, 6 directions with 4 repeats,
b=1100
    Data was acquired in two acquisitions:
        Each acquisition consisted of 5 non-diffusion-weighted images
       (b=0) and 6 diffusion-weighted directions collected in pairs of
       opposite polarity (e.g. (1, 0, 1) was followed by (-1, 0, -1) for a
       total of 12 diffusion-weighted images + 5 non-diffusion-weighted
       images = 17 volumes per input directory
 Target analysis: compute mean FA for the left SLF
 Method: use the JHU DTI atlas to define the left SLF in
a single subject’s native DTI space
      Example 1: Data processing overview
  DICOM & text file that         1    Convert imaging data to
describes diffusion gradient           analyze & correct the
   vectors and bvalues                   gradient vectors
                                                   2
Align the single subject’s                Correct the images for eddy current
T1 data to the single                       induced distortions and motion
subject’s DTI data                           artifacts (make corresponding
                     4                    adjustments to the gradient vectors)
      Align the reference                                        3
      anatomical image from the
      atlas to the single subject’s           Compute the diffusion tensor
      T1-data                                 at each voxel, and its
                                              associated eigenvectors and
                     4                        eigenvalues & FA
           Spatially transform                         3
           the SLF in the atlas to    4
           the single subject’s           Use the ROI as a
           native DTI space               mask and compute
                                          the mean FA
Example 1, part 1: convert data and correct
   gradient table for slice prescription




Step 1: Open correct_slice_prescription.pipe
Example 1, part 1: convert data and correct
   gradient table for slice prescription




 Step 2: delete scandiff module
Example 1, part 1: convert data and correct
   gradient table for slice prescription




  Total run time: ~3 minutes
   Example 1, part 2: correct eddy current
   induced distortions and motion artifacts




Step 2: Open dti_eddy_motion_datawith2repeats.pipe
Example 1, part 3: compute the tensor & FA




Step 3: Open DTI_analysis.pipe
                             Atlases
  AAL atlas: GM maps: that based on cytoarch boundaries
Postmortem GM regions atlas were manually traced on the
                 WM maps: atlas based on histology of the WM
   JHU_DTI_based atlas: Major white matter tracts from JHU
 27repeat single subj.Based on 10 normal controls.
Anatomical underlay: colin27T1_seg
(post mortem histology)
  white matter atlas.
 Anatomical 151x188x154 (1.0x1.0x1.0)
Dimensions: underlay: MNI_ss_cubicmm
Anatomical underlay: colin27T1_seg
  Anatomical underlay: ICBM_152_T1
 Dimensions: 182x218x182 from 10 subjects: value divided by
These are probabilistic maps(1.0x1.0x1.0)
Dimensions: 151x188x154 (1.0x1.0x1.0)
  Dimensions: 181x217x181 (1.0mm x 1.0mm x 1.0mm)
25 is are probabilistic maps fromA, Panzenboeck MM, Fallon
 Reference: Tzourio-Mazoyer N, had subjects: value voxel. D,
                                       that B, Papathanassiou
Thesethe number of subjects who Landeau area in the divided by
  Reference: Wakana S, Caprihan 10
These files were of subjects as Automated anatomical labelling
 Crivello F, Etard O, Delcroix N. had that area Originally bilateral
25 is the number downloadedwhopart of SPM5. inof quantitative
  JH, Perry M, Gollub RL, et al. Reproducibility the voxel.
 of activations methods applied orientation.
  tractography in in the proper part of SPM5. Originally
regions. Already spm using a macroscopic anatomical
These files were downloaded asto cerebral white matter.
References: Burgel,U. et MRIthe proper orientation.Neuroimage
 parcellation of the MNI al., single Neuroimage
bilateral regions. Already in (2006). subject brain.29, 1092-1105.
  Neuroimage 2007; 36: 630-44.
Burgel,U. et al., see SPM_anatomy_toolbox_Manual_v15.pdf
 15: 273-289. 2002.
Many references:(1999) Neuroimage. 10(5), 489-99.
Atlases are now on the pipeline




         Anatomical
    Spatially aligned ROIs
      Reference Image
           Example 1, part 4: ROI analysis




Step 4: Open atlas_based_ROI_analysis_for_dti_data.pipe
                Example 1 summary
 Result for the mean FA for the left SLF: 0.302377
 Total run time: >11 minutes per subject if the grid is
available
                  Example 1 tips
 Use find & replace

 Smartline
Tractography options
Atlas-based tractography
                       Acknowledgements
UCLA
                     This work was supported by the National Institutes of Health through
Roger Woods
                     the NIH Roadmap for Medical Research, Grant U54 RR021813 entitled
Arthur Toga          Center for Computational Biology (CCB). Information on the National
Jeffry Alger         Centers for Biomedical Computing can be obtained from
John Mazziotta       <http://nihroadmap.nih.gov/bioinformatics>.
Rico Magsipoc
                     Support for this work was provided by a grant from the Human Brain
Ivo Dinov
                     Project (Grant Numbers P20-MHDA52176 and 5P01-EB001955), the
Shruthi Chakrapani   National Institute of Biomedical Imaging and Bioengineering, National
Scott Neu            Institute of Mental Health, National Institute for Drug Abuse, National
Alen Zamanyan        Cancer Institute and the National Institute for Neurologic Disease and
JD Trout             Stroke. For generous support the authors also wish to thank the Brain
                     Mapping Medical Research Organization, Brain Mapping Support
Petros Petrosyan
                     Foundation, Pierson-Lovelace Foundation, The Ahmanson Foundation,
Zhizhong Liu         Tamkin Foundation, William M. and Linda R. Dietel Philanthropic Fund
Amanda Hammond       at the Northern Piedmont Community Foundation, Jennifer Jones-
                     Simon Foundation, Capital Group Companies Charitable Foundation,
JHU                  Robson Family and Northstar Fund. The project described was
                     supported by Grant Numbers RR12169, RR13642 and RR00865 from the
Kenichi Oishi
                     National Center for Research Resources (NCRR), a component of the
Susumu Mori          National Institutes of Health (NIH); its contents are solely the
                     responsibility of the authors and do not necessarily represent the
                     official views of NCR or NIH.

								
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