Hi Cibu,<div><br></div><div>Many of these considerations would be discussed in our recent MRtrix paper: </div><blockquote style="margin:0 0 0 40px;border:none;padding:0px"><div><div><i>Tournier J, Calamante F, Connelly A. </i></div>
</div><div><div><i>MRtrix: Diffusion tractography in crossing fiber regions. </i></div></div><div><div><i>International Journal of Imaging Systems and Technology 2012;22:53–66. </i></div></div></blockquote><div><div><br>
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<div>I would personally discourage the use of SD_STREAM, for a number of reasons outlined in that paper. I would always recommend the use of SD_PROB since it is designed to deal with uncertainty, which is a prominent feature of tractography.</div>
<div><br></div><div>Otherwise, I'd recommend a step size of approximately 1/10th the voxel size, with a radius of curvature of around 5x the step size for SD_PROB (it will need to be much smaller than that for SD_STREAM). A cutoff of 0.1 typically works well, but will depend on the quality of your data - if you have very high SNR data, you may be able to lower that value somewhat. The minlength depends to some extent on your research question, but as a general rule I think a minlength of a few voxels is a good idea to reduce the incidence of short artefactual tracks arising from noisy regions, where a spurious FOD peak might make it above threshold over a short distance. </div>
<div><br></div><div>Finally, the number of tracks is always dependent on the research question. In general, more tracks are always preferred, especially with the probabilistic approaches, with the only limitation being the obvious increased computation and storage requirements. The only recommendation I can make here is to keep increasing until the results appear sufficiently stable for your application. To generate a TDI map using a whole-brain approach, I say the 3 million that you're using sounds like it should be about right. For a simple visualisation of whole-brain tracking, you might want to reduce this somewhat to ensure the tracks can actually be loaded into the viewer. For more targeted tracking, you should be able to use much lower numbers, depending on the exact pathways of interest. In short, as you can see this is very much application-dependent.</div>
<div><br></div><div>Hope this helps,</div><div><br></div><div>Donald.</div><div><br></div><br><div class="gmail_quote">On 20 September 2012 06:23, Thomas, Cibu (NIH/NICHD) [V] <span dir="ltr"><<a href="mailto:thomascp@mail.nih.gov" target="_blank">thomascp@mail.nih.gov</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hello<br>
I am trying tractography on ex-vivo macaque brain high angular DW data acquired at a resolution of 250 microns isotropic.<br>
I am using the following command to generate tracts in the whole brain<br>
streamtrack SD_STREAM CSD8.mif -seed mask.mif -mask mask.mif cst_csd.tck -step 0.0375 -curvature 0.049 -grad grads.txt -number 3000000 -minlength 2 -cutoff 0.10<br>
<br>
My question is, are there any recommendations regarding the optimal parameters to be used for tracking with the high res data that I have. Specifically, given the voxels size of the data, is the step size, curvature, number, minlength and cutoff appropriate? Is there an anatomically/empirically guided way to assign these parameters? I couldn't find any in the literature. What is a reasonable rule of thumb?<br>
<br>
Thank you<br>
<br>
Cibu Thomas<br>
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</blockquote></div><br><br clear="all"><div><br></div>-- <br><font color="#ff6600" size="1"><b>Dr Jacques-Donald Tournier<br></b></font><div><font color="#ff6600" size="1">Research Fellow</font></div><div><font size="1"><br>
</font></div><div><font size="1">The Florey Institute of Neuroscience and Mental Health</font></div><div><font size="1">Melbourne Brain Centre - Austin Campus</font></div><div><font size="1">245 Burgundy Street</font></div>
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