⚕️ MRI Image Reconstruction using K-Space ⚕️

🧩 With Fourier Transform Demo

A Streamlit application that demonstrates step-by-step reconstruction of MRI images from their frequency domain (k-space) data using the 2D Fourier Transform.

This interactive tool helps users understand how low and high-frequency components contribute to the final image, showing live reconstruction of the image as more of the frequency space is progressively filled.

👉 Live App:
https://mri-image-reconstruction-using-kspace.streamlit.app/

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⚕️ Live Image Reconstruction

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🖼️ Features

• 📂 Upload your own .dcm (DICOM) MRI image, or use the provided default sample.
• 🌀 Visualize the magnitude spectrum of the Fourier Transform (k-space).
• 🔍 Step-by-step live image reconstruction from k-space data.
• ⚙️ Session management using Streamlit's session_state.
• 🚫 Robust error handling — fallback to default image if upload fails.

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🚀 How It Works

1. Image Loading:
   The app accepts .dcm files (commonly used for medical imaging). If none is provided, it loads a default MRI image.

2. Fourier Transform:
   The image is converted into its frequency domain using Fast Fourier Transform (FFT). The k-space represents spatial frequencies present in the image.

3. Coordinate Processing:
   The app focuses first on reconstructing the left half of the frequency spectrum (progressive loading), prioritizing pixels closer to the center (lower frequencies).

4. Progressive Reconstruction:
   Using live_fourier_reconstruction(), the image is reconstructed in real-time as frequency components are progressively added back.

5. Display:
   View both the k-space magnitude and the live-updating reconstructed image.

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📦 Installation & Local Development

To run the project locally:

git clone https://github.com/MohitGupta0123/MRI-Image-reconstruction-using-kspace.git
cd MRI-Image-reconstruction-using-kspace
pip install -r requirements.txt
streamlit run kspace.py

Note: Replace kspace.py with your actual Streamlit script filename.

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🖥️ Usage

• Upload a .dcm image file.
• Watch the k-space magnitude spectrum.
• Observe real-time image reconstruction.
• Understand how different frequency components affect image quality.

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📂 Project Structure

📦 Your Project Name
├── .streamlit/                # Streamlit configuration
│   └── config.toml
├── docs/                      # assets
│   ├── demo.gif
│   └── fourier_reconstruction.gif
├── images/                    # Images and icons
│   ├── default.dcm
│   └── icon.ico
├── kspace.py                  # Main Python script
├── readme.md                  # Project README
├── requirements.txt           # Python dependencies

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🧠 Learn More

• MRI Physics and k-space Explained

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🌐 Try it Online

No need to set up locally!
Access the live version here:
👉 Launch App

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🛠️ Tech Stack

• Python 🐍
• Streamlit 🎈
• NumPy ⚙️
• Matplotlib (optional for visualization)
• DICOM image handling (assumed via pydicom or similar)

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🤝 Contributing

Contributions are welcome! If you have ideas to improve the visualization, add more reconstruction modes, or extend this tool, feel free to open an issue or PR.

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✨ Acknowledgements

• Inspiration from medical imaging techniques and MRI physics.
• Powered by Streamlit for fast web app development.

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Disclaimer and limitations

This software is not intended for medical use. Even if a scanner original DICOM file is used, the resulting k-space is not equivalent to the scanner raw data as it contains all post-acquisition modifications applied by the scanner software.

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I hope you like it 🌟