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To utilize NiFreeze functionalities within your Python module or script, follow these steps:
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1. **Import Eddymotion Components**: Start by importing necessary components from the Eddymotion package:
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1. **Import NiFreeze Components**: Start by importing necessary components from the NiFreeze package:
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.. code-block:: python
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# Import required components from the Eddymotion package
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# Import required components from the nifreeze package
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from nifreeze.data import dmri
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from nifreeze.estimator importEddyMotionEstimator
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from nifreeze.estimator importEstimator
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2. **Load DWI Data**: Load diffusion MRI (dMRI) data into a `DWI` object using the `load` function. Ensure the gradient table is provided. It should have one row per diffusion-weighted image. The first three columns represent the gradient directions, and the last column indicates the timing and strength of the gradients in units of s/mm² [ R A S+ b ]. If your data are in NIfTI file format, include a file containing the gradient information with the argument "gradients_file":
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@@ -32,16 +32,16 @@ To utilize Eddymotion functionalities within your Python module or script, follo
3. **Instantiate an Eddymotion Estimator Object**: Create an instance of the `EddyMotionEstimator` class, which encapsulates tools for estimating rigid-body head motion and distortions due to eddy currents.
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3. **Instantiate an NiFreeze Estimator Object**: Create an instance of the `Estimator` class, which encapsulates tools for estimating rigid-body head motion and distortions due to eddy currents.
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.. code-block:: python
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# Create an instance of the EddyMotionEstimator class
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estimator =EddyMotionEstimator()
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# Create an instance of the Estimator class
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estimator =Estimator()
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4. **Fit the Models to Estimate the Affine Transformation**:
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Use the `estimate` method of the `EddyMotionEstimator` object to estimate the affine transformation parameters:
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Use the `estimate` method of the `Estimator` object to estimate the affine transformation parameters:
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