Magnetic Resonance Imaging (MRI) has revolutionized medical imaging, but the process is often slow and costly. Enter fastMRI, a remarkable initiative bringing together Facebook AI Research (FAIR) and NYU Langone Health to harness AI’s power in accelerating MRI scans. This blog will enlighten you about how to navigate the fastMRI repository, access its valuable resources, and troubleshoot common issues.

How to Get Started with fastMRI

Follow these steps to efficiently use the fastMRI data loaders and tools:

1. Access the Dataset: Datasets, including knee and brain MRIs, can be obtained from the fastMRI dataset page. The datasets are fully anonymized, ensuring patient privacy.
2. Install Required Packages: Begin by installing PyTorch. Visit the PyTorch Website to follow the specific instructions for your operating system and CUDA setup.
3. Clone the fastMRI Repository: Use Git to clone the repository to your local environment:

git clone https://github.com/facebookresearch/fastMRI.git

4. Install fastMRI: Navigate to the root directory of the cloned repository and run:

pip install -e .

5. Familiarize with Modules: The repository is structured under the fastmri module. Key components include:
   • fastmri.data: Data utilities.
   • fastmri.models: Reconstruction models (like U-Net).
   • fastmri.pl_modules: PyTorch Lightning modules for training.

Understanding the Code Structure

The framework of fastMRI can be compared to a well-crafted toolkit for an artist. Just as an artist has various tools — brushes for painting, chisels for sculpting — fastMRI provides different modules for different tasks. Each module serves a specific purpose:

• Data Handling: The fastmri.data tools help set the stage, just like canvases and sculpting blocks are essential for an artist’s work.
• Model Preferences: Similar to how an artist chooses their technique (be it oil, watercolor, or clay) for a project, a user can select between advanced reconstruction models like U-Net and VarNet.
• Training Modules: Like an art studio equipped for collaborative piece creation, these modules facilitate team efforts in data loading, processing, and model training.

Troubleshooting

Even the best tools can come with their challenges. Here are some common issues and solutions:

1. Memory Leak with h5py: If you encounter a memory leak when working with h5py, ensure you install a version of HDF5 that is older than 1.12.1. Using the conda version of h5py 3.6.0 avoids this issue.
2. Installation Issues: If you face any hiccups during installation, double-check the setup.cfg file and try installing with:

pip install --editable .[tests]

3. Integration Tests: If integration tests are skipped during pytest, and you want to run them, set SKIP_INTEGRATIONS to False in the conftest.py file.

For additional insights, updates, or collaboration on AI development projects, stay connected with fxis.ai.

Conclusion

By using fastMRI, researchers and developers can help reduce MRI scan times and enhance patient care. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.