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