[Neuroimaging] Reconstruction of the diffusion signal with the Tensor model after de-noising

[Samuel St-Jean]

That's right, your problem comes from this line. To keep the example
runnable in a reasonable time, only a single diffusion weighted volume is
selected, hence why your tensor fitting does not work anymore.

You can simply remove this single line and run the rest as is, that should
process your 4D block on each volume separately and them you can fit the
tensor model on the denoised version. I'll guess I'll add a small comment
that this line is for example purposes only.

2015-10-14 23:21 GMT+02:00 Samuel Pilgrim <spilgrim at umail.iu.edu>:

> Neuroimaging mailing list,
>
> I'm an undergraduate experimenting with dipy and am currently working on
> a project that would combine a few of the examples given in the
> documentation.  In particular, I'd like to be able to take one of the
> sample data-sets, apply the de-noising procedure described in the
> documentation under "Denoise images using Non-Local Means" and then use
> the tensor model to reconstruct the diffusion signal using the de-noised
> data.  I have scripts that do each of these things separately, but when I
> try to do reconstruction with de-noised data, I get an alignment error on
> the line that reads "  tenfit = tenmodel.fit(maskdata)  ".  I believe the
> issue is caused by a command in the de-noising part of the script that goes
>  "  data = data[..., 1]  ".  After this line, the dimension of the object
> data has been reduced by one, (i.e. the command print(data.shape) returns a
> tuple of 3 integers instead of 4.  I think this is what's causing the
> alignment issue later, but I also can't get it to run without this line.
> Any assistance would be greatly appreciated.
>
> Best,
> Sam
>
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