[Neuroimaging] nlmeans in HCP data

[Ariel Rokem]

Thanks for the answer. I actually hadn't read the GSoC thread before
sending this question - just read that too.

This might be a naive question: what do you think about estimating the
noise in each voxel from the variance in the b0s image?

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:

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,
>>
>> 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.
>>
>> Thanks!
>>
>> Ariel
>>
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