NODDI - Neurite Orientation Dispersion and Density Imaging

With this tutorial you will learn how to fit the NODDI model to a sample dataset.

Prepare the dataset

Download the sample dataset from the NODDI official website:

• NODDI_example_dataset.zip

After unzipping, your directory structure should look like this:

NODDI_example_dataset/
├── brain_mask.hdr
├── brain_mask.img
├── NODDI_DWI.hdr
├── NODDI_DWI.img
├── NODDI_protocol.bval
└── NODDI_protocol.bvec

Preprocess the data

Usually, DWI images need some preprocessing (e.g., eddy current correction, head movement correction, and skull stripping). You need to perform these pre-preprocessing steps before fitting the model. Assuming this pre-processing has already been done for this sample dataset, we skip those steps here.

Run AMICO

Initialization

Move into the NODDI_example_dataset directory and run a Python interpreter:

cd NODDI_example_dataset
python

In the Python shell, import the AMICO library and setup/initialize the framework:

import amico
amico.setup()

-> Precomputing rotation matrices:
   [ DONE ]

[!NOTE] This step will precompute all the necessary rotation matrices and store them in ~/.dipy. This initialization step is necessary only the first time you use AMICO.

Now you can instantiate an Evaluation object and start the analysis:

ae = amico.Evaluation()

You can generate the scheme file from the bvals/bvecs files of your acquisition with the fsl2scheme() method:

amico.util.fsl2scheme('NODDI_protocol.bval', 'NODDI_protocol.bvec')

-> Writing scheme file to [ NODDI_protocol.scheme ]
'NODDI_protocol.scheme'

Load the data

Load your data with the load_data() method:

ae.load_data('NODDI_DWI.img', 'NODDI_protocol.scheme', mask_filename='brain_mask.img', b0_thr=0)

-> Loading data:
	* DWI signal
		- dim    = 128 x 128 x 50 x 81
		- pixdim = 1.875 x 1.875 x 2.500
	* Acquisition scheme
		- 81 samples, 2 shells
		- 9 @ b=0 , 24 @ b=700.0 , 48 @ b=2000.0 
	* Binary mask
		- dim    = 128 x 128 x 50
		- pixdim = 1.875 x 1.875 x 2.500
		- voxels = 178924
   [ 0.3 seconds ]

-> Preprocessing:
	* Normalizing to b0... [ min=0.00,  mean=2.78, max=2862.00 ]
	* Keeping all b0 volume(s)
   [ 8.2 seconds ]

Compute the response functions

Set the NODDI model with the set_model() method and generate the response functions with the generate_kernels() method:

ae.set_model('NODDI')
ae.generate_kernels(regenerate=True)

-> Creating LUT for "NODDI" model:
   [ 1.8 seconds ]

[!IMPORTANT]

• This example uses the default parameters for the NODDI model. You can change them with the model.set() method. Refer to the Model Configuration page for more information on model-specific parameters.
• You need to compute the reponse functions only once per study; in fact, scheme files with same b-values but different number/distribution of samples on each shell will result in the same precomputed kernels (which are actually computed at higher angular resolution). The method generate_kernels() does not recompute the kernels if they already exist, unless the flag regenerate is set, e.g. generate_kernels(regenerate=True).

Load the precomputed kernels (at higher resolution) and adapt them to the actual scheme (distribution of points on each shell) of the current subject with the load_kernels() method:

ae.load_kernels()

-> Resampling LUT for subject ".":
   [ 0.4 seconds ]

Fit the model to the data

Fit the model to the data with the fit() method:

ae.fit()

-> Estimating principal directions (OLS):
   [ 00h 00m 01s ]

-> Fitting 'NODDI' model to 178924 voxels (using 32 threads):
   [ 00h 00m 04s ]

Save the results

Finally, save the results as NIfTI images with the save_results() method:

ae.save_results()

-> Saving output to "AMICO/NODDI/*":
	- configuration  [OK]
	- fit_dir.nii.gz  [OK]
	- fit_NDI.nii.gz  [OK]
	- fit_ODI.nii.gz  [OK]
	- fit_FWF.nii.gz  [OK]
   [ DONE ]

Visualize the results

🎉Congratulations! You have successfully fitted the NODDI model to your data.🎉 You will find the estimated parameters in the NODDI_example_dataset/AMICO/NODDI directory:

NODDI_example_dataset/AMICO/NODDI/
├── config.pickle
├── fit_dir.nii.gz
├── fit_FWF.nii.gz
├── fit_NDI.nii.gz
└── fit_ODI.nii.gz

Open them with your favorite viewer.