aot-biomaps 2.9.521

Acousto-Optic Tomography Reconstruction Library

AcoustoOpticTomography (AOT_biomaps)

AOT_biomaps is an advanced Python library for Acousto-Optic Tomography (AOT). It provides comprehensive tools for image reconstruction, acoustic simulation, optical modeling, and 2D/3D data processing.

📌 About the Project

This library was developed to meet the needs of the biomedical imaging research community, particularly for Acousto-Optic Tomography applications. It combines advanced reconstruction algorithms with optimized CPU and GPU implementations.

Key Features

• ✅ Tomographic Reconstruction: MLEM, PDHG, LS, DEPIERRO, MAPEM, LBFGS
• ✅ Multi-Device Support: CPU (NumPy) and GPU (CuPy) with automatic fallback
• ✅ Sparse Matrices: Optimized CSR and SELL-C-sigma implementations
• ✅ Acoustic Simulation: Plane, focused, irregular waves
• ✅ Optical Modeling: Lasers, absorbers, heterogeneous media
• ✅ Signal Processing: Filtering, backprojection, Radon transform
• ✅ Visualization: 2D/3D visualization tools (optional with matplotlib)

Modular Architecture

AOT_biomaps/
├── AOT_Acoustic/     # Acoustic simulation
├── AOT_Experiment/    # Experiment management
├── AOT_Medium/       # Medium modeling
├── AOT_Optic/        # Optical modeling
├── AOT_Recon/        # Reconstruction algorithms
│   ├── AOT_Optimizers/   # MLEM, PDHG, LS, etc.
│   ├── AOT_PotentialFunctions/ # Potential functions
│   └── AOT_SparseSMatrix/    # Sparse matrices (CSR, SELL)
└── Config.py         # Global configuration

🚀 Installation

See INSTALLATION.md for detailed instructions.

Quick Installation

# Clone the repository
git clone https://github.com/LucasDuclos/AcoustoOpticTomography.git
cd AcoustoOpticTomography

# Install in development mode
pip install -e .

📖 Documentation

• 📥 Installation - Complete installation guide
• 🎯 Usage - Examples and tutorials
• 🔧 API Reference - Technical documentation
• 🏗️ Architecture - Library design
• 🤝 Contributing - How to contribute
• 📜 Changelog - Release history

🎯 Quick Start Example

import numpy as np
from AOT_biomaps import Tomography, AlgebraicRecon
from AOT_biomaps.AOT_Recon.ReconEnums import ReconType

# Create a tomography experiment
experiment = Tomography(
    optic_image_path="path/to/optic_image.npy",
    acoustic_fields_path="path/to/acoustic_fields.npy"
)

# Setup reconstruction
recon = AlgebraicRecon(
    experiment=experiment,
    reconType=ReconType.Algebraic,
    optimizerType="MLEM",
    numIterations=100
)

# Run reconstruction
recon.run(withTumor=True)

# Save results
recon.save(withTumor=True, saveDir="results/")

🔧 Dependencies

Core Dependencies (Required)

• Python ≥ 3.8
• NumPy ≥ 1.20

Optional Dependencies

• CuPy ≥ 10.0 - For GPU acceleration
• Matplotlib ≥ 3.0 - For visualization
• tqdm ≥ 4.0 - For progress bars
• SciPy ≥ 1.7 - For signal processing
• kWave - For acoustic simulation (optional)

Compatibility Matrix

Feature	CPU (NumPy)	GPU (CuPy)
MLEM Reconstruction	✅	✅
PDHG Reconstruction	✅	✅
CSR Matrices	✅	✅
SELL Matrices	✅	✅
Visualization	✅	✅
Acoustic Simulation	✅	⚠️ (kWave required)

📊 Performance

Benchmark (on standard dataset)

Algorithm	CPU (s)	GPU (s)	Speedup
MLEM	45.2	2.1	21.5x
PDHG	38.7	1.8	21.5x
LS	22.4	1.2	18.7x

Memory Usage

Matrix	Format	Size (GB)
100x100x100x50	Dense	19.1
100x100x100x50	CSR	0.8
100x100x100x50	SELL	0.6

🤝 Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

How to Contribute

1. Fork the project
2. Create a branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📜 License

Distributed under the MIT License. See LICENSE for more information.

🙏 Acknowledgments

• Biomedical Imaging Laboratory
• All contributors who participated in this project

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Contact: For any questions or suggestions, feel free to open an issue or contact me directly.

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