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

[Ariel Rokem]

Hi Sam,

You might be able to draw inspiration from the new DKI example, in which
denoising is first applied:

https://github.com/nipy/dipy/blob/master/doc/examples/reconst_dki.py#L124

And then a model is fit (in this case the DKI model, but the TensorModel
would look very similar):

https://github.com/nipy/dipy/blob/master/doc/examples/reconst_dki.py#L124
https://github.com/nipy/dipy/blob/master/doc/examples/reconst_dki.py#L145

Does that work for you?

Cheers,

Ariel



On Wed, Oct 14, 2015 at 2:21 PM, Samuel Pilgrim <spilgrim at umail.iu.edu>
wrote:

> 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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