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# Neural Networks and Deep Learning
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Deep learning models for image denoising, enhancement, and reconstruction of diffusion MRI data. DIPY provides neural network implementations for improving data quality and accelerating processing workflows.
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## Capabilities
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### Denoising Networks
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Deep learning approaches for removing noise from diffusion MRI data while preserving anatomical structures.
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```python { .api }
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def patch2self(data, bvals, patch_radius=0, model='ols', b0_threshold=50, out_dtype=None, alpha=1.0, verbose=False):
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"""
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Self-supervised denoising using patch-based learning.
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Parameters:
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data (array): 4D diffusion data (x, y, z, volumes)
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bvals (array): b-values for each volume
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patch_radius (int): radius for patch extraction
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model (str): regression model ('ols', 'ridge', 'lasso')
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b0_threshold (float): threshold for b=0 identification
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out_dtype (dtype): output data type
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alpha (float): regularization parameter
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verbose (bool): print progress information
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Returns:
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array: denoised diffusion data
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"""
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class Patch2SelfDenoiser:
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"""Self-supervised patch-based denoiser."""
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def __init__(self, patch_radius=0, model='ols', alpha=1.0):
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"""
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Initialize Patch2Self denoiser.
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Parameters:
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patch_radius (int): patch extraction radius
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model (str): regression model type
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alpha (float): regularization strength
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"""
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def denoise(self, data, bvals, b0_threshold=50):
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"""
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Apply denoising to diffusion data.
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Parameters:
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data (array): input diffusion data
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bvals (array): b-values
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b0_threshold (float): b=0 threshold
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Returns:
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array: denoised data
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"""
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def deepn4(vol, affine=None, mask=None, dtype=None):
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"""
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Deep learning-based N4 bias field correction.
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Parameters:
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vol (array): input volume data
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affine (array): voxel-to-world transformation
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mask (array): brain mask
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dtype (dtype): output data type
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Returns:
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array: bias-corrected volume
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"""
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```
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### Enhancement Networks
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Networks for improving image quality, resolution, and contrast in diffusion MRI.
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```python { .api }
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class EVAC:
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"""Enhanced Volume And Contrast using deep learning."""
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def __init__(self, model_path=None):
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"""
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Initialize EVAC enhancement model.
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Parameters:
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model_path (str): path to pre-trained model weights
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"""
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def predict(self, low_res_data, **kwargs):
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"""
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Enhance low-resolution diffusion data.
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Parameters:
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low_res_data (array): input low-resolution data
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Returns:
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array: enhanced high-resolution data
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"""
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class HistoResDNN:
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"""Histogram Restoration Deep Neural Network."""
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def __init__(self, model_path=None):
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"""Initialize histogram restoration model."""
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def predict(self, input_data, **kwargs):
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"""
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Restore signal histograms using deep learning.
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Parameters:
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input_data (array): input diffusion data
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Returns:
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array: histogram-restored data
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"""
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def synb0_normalize(dwi_data, bvals, b0_threshold=50):
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"""
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Normalize DWI data for SynB0 processing.
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Parameters:
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dwi_data (array): diffusion weighted images
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bvals (array): b-values
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b0_threshold (float): b=0 threshold
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Returns:
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array: normalized DWI data
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"""
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```
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### Synthesis Networks
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Networks for synthesizing missing contrasts or b=0 images from existing data.
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```python { .api }
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class SynB0:
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"""Synthetic b=0 image generation."""
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def __init__(self, model_path=None):
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"""
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Initialize SynB0 synthesis model.
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Parameters:
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model_path (str): path to trained model
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"""
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def predict(self, dwi_data, **kwargs):
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"""
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Generate synthetic b=0 image from DWI data.
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Parameters:
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dwi_data (array): diffusion weighted images
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Returns:
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array: synthetic b=0 image
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"""
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def synth_b0(dwi_vol, bvals, bvecs, model=None):
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"""
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Synthesize b=0 volume from DWI data.
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Parameters:
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dwi_vol (array): DWI volume data
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bvals (array): b-values
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bvecs (array): gradient directions
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model: trained synthesis model
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Returns:
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array: synthesized b=0 volume
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"""
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class ContrastSynthesis:
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"""Multi-contrast synthesis for diffusion MRI."""
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def __init__(self, source_contrast, target_contrast):
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"""
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Initialize contrast synthesis.
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Parameters:
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source_contrast (str): input contrast type
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target_contrast (str): desired output contrast
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"""
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def train(self, paired_data):
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"""Train synthesis model on paired data."""
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def synthesize(self, source_data):
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"""Synthesize target contrast from source."""
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```
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### Neural Network Utilities
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Supporting utilities for neural network training, evaluation, and deployment.
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```python { .api }
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class DummyNeuralNetwork:
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"""Dummy neural network for testing and development."""
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def __init__(self, input_shape=None, output_shape=None):
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"""
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Initialize dummy network.
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Parameters:
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input_shape (tuple): expected input dimensions
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output_shape (tuple): output dimensions
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"""
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def predict(self, data, **kwargs):
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"""
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Dummy prediction function.
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Parameters:
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data (array): input data
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Returns:
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array: processed output data
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"""
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def load_model_weights(model_path, framework='tensorflow'):
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"""
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Load pre-trained model weights.
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Parameters:
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model_path (str): path to model file
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framework (str): deep learning framework ('tensorflow', 'pytorch')
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Returns:
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object: loaded model with weights
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"""
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def preprocess_for_network(data, normalization='z_score'):
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"""
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Preprocess data for neural network input.
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Parameters:
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data (array): input diffusion data
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normalization (str): normalization method
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Returns:
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array: preprocessed data ready for network
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"""
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def postprocess_network_output(output, original_data):
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"""
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Postprocess network output to match original data characteristics.
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Parameters:
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output (array): network output
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original_data (array): original input data for reference
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Returns:
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array: postprocessed output
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"""
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```
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### Training and Validation
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Tools for training custom neural networks on diffusion MRI data.
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```python { .api }
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class DiffusionNetworkTrainer:
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"""Trainer for diffusion MRI neural networks."""
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def __init__(self, model, loss_function='mse', optimizer='adam'):
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"""
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Initialize network trainer.
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Parameters:
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model: neural network model
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loss_function (str): training loss function
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optimizer (str): optimization algorithm
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"""
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def train(self, train_data, val_data, epochs=100, batch_size=32):
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"""
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Train the neural network.
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Parameters:
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train_data (tuple): (X_train, y_train) training data
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val_data (tuple): (X_val, y_val) validation data
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epochs (int): number of training epochs
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batch_size (int): batch size for training
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Returns:
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dict: training history and metrics
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"""
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def evaluate(self, test_data):
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"""Evaluate model on test data."""
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class DataAugmentation:
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"""Data augmentation for diffusion MRI training."""
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def __init__(self, rotation_range=10, noise_level=0.1):
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"""Initialize augmentation parameters."""
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def augment_batch(self, batch_data):
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"""Apply augmentation to training batch."""
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def cross_validation(model, data, k_folds=5):
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"""
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Perform k-fold cross-validation.
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Parameters:
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model: neural network model
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data (tuple): (X, y) dataset
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k_folds (int): number of folds
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Returns:
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dict: cross-validation results
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"""
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```
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### Model Zoo
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Pre-trained models for common diffusion MRI tasks.
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```python { .api }
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def get_pretrained_model(task='denoising', architecture='patch2self'):
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"""
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Load pre-trained model for specific task.
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Parameters:
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task (str): target task ('denoising', 'enhancement', 'synthesis')
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architecture (str): model architecture name
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Returns:
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object: loaded pre-trained model
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"""
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class ModelRegistry:
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"""Registry of available pre-trained models."""
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@staticmethod
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def list_models():
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"""List all available pre-trained models."""
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@staticmethod
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def download_model(model_name, cache_dir=None):
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"""Download and cache model weights."""
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@staticmethod
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def load_model(model_name):
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"""Load model from registry."""
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def benchmark_model(model, test_data, metrics=['psnr', 'ssim', 'mse']):
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"""
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Benchmark model performance on test data.
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Parameters:
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model: trained model
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test_data (tuple): (X_test, y_test) test dataset
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metrics (list): evaluation metrics to compute
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Returns:
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dict: performance metrics
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"""
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```
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### Usage Examples
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```python
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# Patch2Self denoising example
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from dipy.nn.patch2self import patch2self
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from dipy.data import read_stanford_hardi
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import numpy as np
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# Load noisy diffusion data
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img, gtab = read_stanford_hardi()
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data = img.get_fdata()
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# Add artificial noise for demonstration
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noisy_data = data + np.random.normal(0, 0.1 * data.mean(), data.shape)
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# Apply Patch2Self denoising
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denoised_data = patch2self(
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noisy_data,
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gtab.bvals,
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patch_radius=1,
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model='ridge',
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alpha=1.0,
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verbose=True
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)
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print(f"Original data shape: {data.shape}")
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print(f"Denoised data shape: {denoised_data.shape}")
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# Calculate denoising performance
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mse_before = np.mean((noisy_data - data) ** 2)
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mse_after = np.mean((denoised_data - data) ** 2)
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print(f"MSE before denoising: {mse_before:.6f}")
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print(f"MSE after denoising: {mse_after:.6f}")
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print(f"Improvement: {(mse_before - mse_after) / mse_before * 100:.1f}%")
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# Deep N4 bias correction
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from dipy.nn.deepn4 import deepn4
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# Apply bias field correction (simulated)
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bias_corrected = deepn4(data[..., 0], affine=img.affine) # Correct b=0 image
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print(f"Bias correction applied to shape: {bias_corrected.shape}")
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# SynB0 synthetic b=0 generation
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from dipy.nn.synb0 import SynB0
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# Initialize SynB0 model
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synb0_model = SynB0()
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# Generate synthetic b=0 from DWI data
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dwi_only = data[..., gtab.bvals > 50] # Extract DWI volumes
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synthetic_b0 = synb0_model.predict(dwi_only)
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print(f"Synthetic b=0 shape: {synthetic_b0.shape}")
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# Compare with actual b=0
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actual_b0 = data[..., gtab.bvals <= 50].mean(axis=-1)
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correlation = np.corrcoef(actual_b0.flatten(), synthetic_b0.flatten())[0, 1]
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print(f"Correlation with actual b=0: {correlation:.3f}")
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# Custom network training example
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from dipy.nn.utils import DiffusionNetworkTrainer, DataAugmentation
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# Prepare training data (simulated)
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n_samples = 1000
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patch_size = 16
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n_directions = len(gtab.bvals)
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# Create patches for training
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X_train = np.random.random((n_samples, patch_size, patch_size, n_directions))
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y_train = np.random.random((n_samples, patch_size, patch_size, 1)) # Target (e.g., FA)
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X_val = np.random.random((200, patch_size, patch_size, n_directions))
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y_val = np.random.random((200, patch_size, patch_size, 1))
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# Initialize trainer
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trainer = DiffusionNetworkTrainer(
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model=None, # Would be actual model
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loss_function='mse',
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optimizer='adam'
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)
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# Data augmentation
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augmenter = DataAugmentation(rotation_range=15, noise_level=0.05)
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print("Training setup complete")
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print(f"Training data shape: {X_train.shape}")
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print(f"Validation data shape: {X_val.shape}")
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# Model evaluation and benchmarking
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from dipy.nn.utils import benchmark_model
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# Benchmark denoising performance
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test_metrics = {
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'mse': mse_after,
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'psnr': 20 * np.log10(data.max() / np.sqrt(mse_after)),
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'snr_improvement': 10 * np.log10(mse_before / mse_after)
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}
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print("Denoising Performance Metrics:")
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for metric, value in test_metrics.items():
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print(f" {metric.upper()}: {value:.3f}")
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# Load pre-trained model from registry
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from dipy.nn.model_zoo import get_pretrained_model, ModelRegistry
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# List available models
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available_models = ModelRegistry.list_models()
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print(f"Available pre-trained models: {len(available_models)}")
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# Load specific model
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denoising_model = get_pretrained_model(task='denoising', architecture='patch2self')
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print(f"Loaded pre-trained denoising model: {type(denoising_model)}")
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```