[Neuroimaging] nlmeans in HCP data

[Ariel Rokem]

As for this:

On Sat, Feb 6, 2016 at 3:03 AM, Samuel St-Jean <stjeansam at gmail.com> wrote:

> For starters, if you have motion between b0s volumes or a few of them, you
> might have problems and induce a larger variance because of that, but I
> guess if it works why not. As for a single voxel estimate, it might be
> unstable due to the small number of samples, but taking moving neighborhood
> could help. Actually they use it fr estimating mtion and pulsation artefact
> if I recall correctly [1]
>
> As for evaluating, predicting signal or not is one of the aspect you can
> look at from my opinion, but with all the local model fitting and
> tractography happening afterward, looking at a squared error value is not
> very informative, especially if it averaged over all the volume. Since a
> large error in a crossing voxel could be much worse than small errors in
> single fiber voxels, it depends on what yu want to get at the end of the
> day. I can be useful to judge an optimization scheme, but beyond that I
> don't feel like it reflect properties of the end goal.
>
>
I'd say that model is accuracy is a necessary condition for useful
inferences, though it might not always be sufficient. Wouldn't you agree?



> [1] https://www.ncbi.nlm.nih.gov/pubmed/21469191
>
> Le 2016-02-06 03:44, Ariel Rokem a écrit :
>
> Thanks for the answer. I actually hadn't read the GSoC thread before
> sending this question - just read that too.Â
>
> This might be a naive question: what do you think about estimating the
> noise in each voxel from the variance in the b0s image?Â
>
> When we noticed that the GE scanner at Stanford was masking out the
> background, we switched the implementation of RESTORE on vistasoft to use
> the variance between multiple b0 images as an estimate of the noise,
> including a correction for bias due to small sample:Â
>
>
> https://github.com/vistalab/vistasoft/blob/master/mrDiffusion/utils/dtiComputeImageNoise.m#L58
>
> In this case, we take a median to have one number for the entire volume,
> but we could also just keep the variance in each voxel. Do you see any
> obvious problems with that?
>
> From my point of view, it is rather straightforward to quantitatively
> evaluate whether a denoising method is improving your analysis. Either your
> model of the diffusion data fits the data better (in the cross-validation
> sense) following denoising, or it doesn't, in which case the method's
> probably no good.
>
>
> On Fri, Feb 5, 2016 at 8:13 AM, Samuel St-Jean <stjeansam at gmail.com>
> wrote:
>
>> To partly answer the question, you should pick N=1 as the HCP data is
>> using a SENSE1 reconstruction, and thus always give a rician distribution
>> [1].
>> As for using estimate sigma, it tends to overblur stuff for higher
>> b-value/spatially varying noise (it has a hard time on our philips 3T data
>> for example, edges are overblurred and center is untouched).
>>
>> Regarding these shortcomings, I linked to some ideas to solve some of
>> these caveats in the gsoc discussion thread though.
>>
>> [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657588/
>>
>> 2016-02-05 0:58 GMT+01:00 Ariel Rokem <arokem at gmail.com>:
>>
>>> Hi everyone,Â
>>>
>>> does anyone use the Dipy nlmeans with HCP diffusion data? Is that a good
>>> idea? What do you use to estimate the sigma input? If you use
>>> dipy.denoise.noise_estimate.estimate_sigma, how do you set the `n` keyword
>>> argument for these data? Since the preprocessed data has gone through some
>>> heavy preprocessing, I am not sure whether assuming that 32 (the number of
>>> channels in these machines, if I understand correctly) is a good number is
>>> reasonable.Â
>>>
>>> Thanks!Â
>>>
>>> Ariel
>>>
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