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

[Samuel Pilgrim]

Ariel,
Thanks a lot.  I'll try that out today/tomorrow and get back to you.

Best,
Sam

On Fri, Oct 16, 2015 at 2:46 AM, Ariel Rokem <arokem at gmail.com> wrote:

> 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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>
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