Ariel, Thank you so much for your prompt email! This looks like it should get at the problems that I was having loading the data. If I have any further questions I will be sure to ask, but I am hopeful this will solve my issues. On Fri, May 8, 2020 at 2:11 PM Ariel Rokem <arokem@gmail.com> wrote:
Hi Katie,
Thanks for your email. Let me try to answer these one-by-one:
On Fri, May 8, 2020 at 10:58 AM Katie Jobson <tuj96493@temple.edu> wrote:
Hello DIPY experts,
I am attempting to apply a free water correction to my DWI data in order to look at structures such as the fornix. Normally I process data within FSL, though I do have experience coding with Python. To use the free water correction, do I need to do all of my preprocessing within DIPY, or am I able to use the data I have already preprocessed within FSL?
You can analyze the data that you have preprocessed with FSL. To be completely honest, I think that it's fair to say that we don't have a very good preprocessing pipeline yet. Some of us are working on this in https://github.com/nipreps/dmriprep, but that's still under heavy development. So: for now data that has been preprocessed with your existing preprocessing pipeline should work.
Are there also any tutorials in using my own data for processing, rather than examples? I am able to process examples with no issue, but I am struggling with applying these commands to my own data. I have mainly been confused with where to house my data, and how to proceed when I have a different type of data than the example (I have multi-shelled HYDI data, but many examples are with HARDI). I have not been successful at loading my own data for any of the examples as of yet.
We should improve this. There are some tutorials on the command line interfaces that will be included in the upcoming release.
For now, notice that this code: https://github.com/dipy/dipy/blob/master/doc/examples/reconst_fwdti.py#L66, creates two objects from the example data. One variable, called `img` is a nibabel image object. The other, `gtab` is a gradient table object.
Instead of that line of code, you can run the following:
import nibabel as nib img = nib.load('full/path/to/your/preprocessed/dwi.nii.gz')
import dipy.gradients as dpg gtab = dpg.gradient_table('full/path/to/your/preprocessed/dwi.bval', 'full/path/to/your/preprocessed/dwi.bvec')
After you have done that, the rest of the code in that example should run as-is (but let me know if I am missing something -- I am not 100% sure that I understand how the HYDI acquisition might affect this).
Any advice or resources would be extremely helpful. My lab believes that DIPY will be the way forward, so I am more than willing to learn how to do preprocessing within DIPY, it would just be convenient for my current project to use my already preprocessed data.
I hope it's clear that this is possible. Let me know if more information would be helpful.
Cheers,
Ariel
Thank you for your help, Katie _______________________________________________ Dipy mailing list -- dipy@python.org To unsubscribe send an email to dipy-leave@python.org https://mail.python.org/mailman3/lists/dipy.python.org/ Member address: arokem@gmail.com
-- Katie Jobson, M.S. Research Assistant | Aphasia Rehabilitation Research Lab Ph.D. Student | Cognitive Neuroscience Lab Temple University