Hi Romain,<div><br></div><div>In terms of the number of directions, I don't think it'll be worth increasing the number beyond around 60 - I had an ISMRM abstract about this a while back (which I'll hopefully get published at some point soon) showing that you really can't see much in the SH terms above lmax=8. There's just a suggestion of an lmax=10 term at b>=3000, but it'll be drowned in the noise for any realistic protocol. That mean you'll need at least 45 directions (for lmax=8), and I'd recommend going beyond that minimum number to make sure the problem is comfortably over-determined. This is particularly important if you're going to perform motion-correction (which you should given the length of your scan) with rotation of the gradient directions, a process that will tend to reduce the uniformity of your gradient directions. That said, you'll probably find it near-impossible to perform good motion correction - more on that later... You might want to push the number of directions beyond 66 if you really want to get the lmax=10 terms (maybe ~80 to make sure it's over-determined), but I'm not sure it'll necessarily make much of a difference. Otherwise, I'd recommend you repeat those directions, which at least gives you the potential to do something with the redundancy...</div>
<div><br></div><div>A bigger problem you'll face is the consequence of the low SNR you'll have given your small desired voxel size and higher b-value. While I'd consider that b-value to be optimal in terms of contrast-to-noise ratio, it does cause other problems when the SNR is low: Rician bias, and poor performance on just about any motion correction algorithm. The problem with the Rician bias is that even with a lot of averaging, the noise floor on magnitude data will push up the low signal intensities, and this tends to reduce the contrast between low SNR amplitudes. If you have a small voxel size (low SNR in each DW image), then the noise level will probably be of the same order as the DW signal, and this will tend to flatten out the angular contrast. The other problem is that this non-zero background signal will tend to introduce noisy peaks in the FODs, since the CSD process will interpret them as signal and try to fit lobes to match the noise - you'll probably see lots of noisy peaks in the ventricles for example. There have been a few methods proposed to rectify the Rician bias, and you may find that they can help, but I have no experience with this.</div>
<div><br></div><div>Motion correction is another problem in high b-value HARDI, because of the low SNR on the one hand, and because the contrast between the different images is further exaggerated, with makes life very difficult for the usual image matching metrics (mutual information, typically). In my experience, most methods currently available introduce more artefacts into the reconstruction than they solve. We had an abstract on a mask-based method to perform robust motion correction on high b-value data at this year's ISMRM (Dave Raffelt) and Jesper Andersson also had a method based on Gaussian processes. You might find that one or both of these approaches could be used, but as far as I know neither is currently available. We're planning on making the mask-based method available in a future version of MRtrix, but it's not ready yet... </div>
<div><br></div><div>One much easier option for motion correction when you're confident your subject won't move suddenly, is to intersperse b=0 images every ~10 DW images, so you can estimate the motion based on these b=0 images and interpolate the motion parameters to apply them to the DWIs. This is the approach I'd originally used for my 2004 spherical deconvolution paper, which also was a ~1h acquisition, and that worked pretty well. Not robust enough for patients though, they'll tend to move in jerks, which introduces higher frequencies in the motion than you can capture with the b=0 images, and also tends to corrupt the signal due to intra-volume mis-registration and signal dropout.</div>
<div><br></div><div>Hope that helps.</div><div>Cheers,</div><div><br></div><div>Donald.</div><div><br><br><div class="gmail_quote">On 14 August 2012 22:19, romain quentin <span dir="ltr"><<a href="mailto:rom.quentin@gmail.com" target="_blank">rom.quentin@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Dear experts,<br>
I'm using CSD from mrtrix with a diffusion sequence with b = 1500, 32 channels coil, 60 directions and isotropic 1.7mm voxels. This sequence takes about 15 minutes.<br>
I want to acquire a better diffusion sequence with more scanning time (around 1 hour). I think to use b value = 3000 but i'm not sure about the voxel size and numbers of directions.<br>
Do you think it's better to do 4 x 60 directions, 2 x 120 or around 200 directions of diffusions?<br>
And I know that reduces the voxel size is risky for the SNR but do you think that I can keep a 1.7 isotropic regard to the time scanning (1 hour)?<br>
Thank you very much for your help.<br>
Best regards,<br>
<br>
Romain QUENTIN<br>
<a href="mailto:rom.quentin@gmail.com">rom.quentin@gmail.com</a><br>
01 42 16 00 67<br>
06 80 33 15 74<br>
<br>
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</blockquote></div><br><br clear="all"><div><br></div>-- <br>Jacques-Donald Tournier (PhD)<br>Brain Research Institute, Melbourne, Australia<br>Tel: +61 (0)3 9035 7033<br>
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