<div dir="ltr">Hi Jan,<div><br></div><div>This is an important point, and one that we sometimes forget that we (as in, the MRtrix dev team) think about quite differently to others in Diffusion MR.</div><div>We will draw attention to this issue in an upcoming publication, but I'll try to give a succinct explanation here.</div><div><br></div><div>Conventionally, the log-transform with respect to the b=0 image converts a signal amplitude to an apparent diffusion coefficient; nothing controversial here. However if you were to then apply a spherical deconvolution transform, the FOD amplitude along a particular direction would be proportional to the ADC of the fibre population oriented along that direction. This isn't particularly useful information; it doesn't tell us much about differences between fibre populations throughout the image, or indeed within a voxel.</div><div><br></div><div>Ideally what we actually want for a number of applications is the volume of each fibre population element, in all voxels throughout the image. Based on David Raffelt's early <a href="http://www.sciencedirect.com/science/article/pii/S1053811911012092">simulations</a>, it turns out that (under certain conditions) the radial component of the DWI signal amplitude is actually a pretty decent marker for intra-cellular volume. Therefore, by ignoring the b=0 images completely and just running SD on the raw DWI intensities, we get pretty useful biological information and interpretation from the FOD; we also conveniently bypass the issue of Gibbs ringing in the b=0 images. Caveat is that you need a uniform B1 field (i.e. intensity bias field correction); for applications like AFD you also need inter-subject intensity normalisation, but that's not necessarily a problem for SIFT depending on how you're using it.</div><div><br></div><div>That's all for now. Hope that clarifies why we choose to apply SD in this way; in fact, this approach dates all the way back to the original SD paper.</div><div>Rob</div></div><div class="gmail_extra"><br clear="all"><div><div class="gmail_signature"><div dir="ltr"><br>--<br><br><span style="color:rgb(255,102,0)"><b>Robert Smith, Ph.D</b><br>Research Officer, Imaging Division</span><br><br>The Florey Institute of Neuroscience and Mental Health<br>Melbourne Brain Centre - Austin Campus<br>245 Burgundy Street<br>Heidelberg Vic 3084<br>Ph: +61 3 9035 7128<br>Fax: +61 3 9035 7301<br><a href="http://www.florey.edu.au/" target="_blank">www.florey.edu.au</a><br></div></div></div>
<br><div class="gmail_quote">On Sat, Dec 13, 2014 at 1:37 AM, Jan Schreiber <span dir="ltr"><<a href="mailto:schreiber@cbs.mpg.de" target="_blank">schreiber@cbs.mpg.de</a>></span> wrote:<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Dear MRtrix Team,<br>
<br>
thank you very much for this great software and for making it freely<br>
available!<br>
<br>
In your publication "SIFT: Spherical-deconvolution informed filtering of<br>
tractograms" you state<br>
<br>
"The diffusion signal must not be normalised to the b = 0 image<br>
intensity. This preserves the linearity of the spherical deconvolution<br>
transform between the measured DW signal and the resulting FOD."<br>
<br>
Shouldn't we preserve the linearity of the spherical deconvolution<br>
transform between the FOD and the DW _signal attenuation_ rather than<br>
the DW _signal_?<br>
<br>
Thanks,<br>
Jan<br>
<br>
</blockquote></div></div>