This repository contains concise Python-based MRI analysis processing pipeline that provide simple and accessible solutions for researchers, clinicians, and students working with MRI data (i.e., structural, fMRI, MRS, DTI data)

This DTI processing pipeline includes DICOMS to NIfTI conversion, skull stripping, eddy correction using FSL, fits a tensor model, and generates Fractional Anisotropy (FA), Mean Diffusivity (MD), Radial Diffusivity (RD), and Axial Diffusivity (AD) maps. aims to Diffusion Tensor Imaging data. The pipeline is designed with user-friendliness in mind, enabling users to process their DTI data with minimal setup and prior knowledge of the field.

The script consolidates several essential processing steps into a single, easy-to-use pipeline. This allows users to focus on their research objectives without getting bogged down in the complexities of multiple tools and manual processing steps. Moreover, the pipeline integrates well-known tools, such as dcm2niix and FSL, ensuring the quality and reliability of the processing.

By offering a streamlined and accessible approach to DTI data processing, this script empowers users to explore and analyze their data efficiently, ultimately contributing to advancements in research and clinical applications related to Diffusion Tensor Imaging.

• Python 3
• Dipy
• NiBabel
• Nipype
• FSL
• Matplotlib

1. Clone this repository and navigate to the project folder.

2. Install the required dependencies.

3. Update the path, input_dir, output_dir, bval_file, bvec_file, and TotalReadoutTime variables in the if __name__ == "__main__": block of the dti_processing.py script to match your local setup.

4. Run the dti_processing.py script:

python dti_processing.py <path> <input_dir> <output_dir> <bval_file> <bvec_file> <TotalReadoutTime>

The script will generate DTI maps (FA, MD, RD, and AD) and save them as NIfTI files in the specified output directory. It will also display a 2x2 plot of the generated maps.

Example output showing FA, MD, RD, and AD maps:

License [MIT License]

Author [Mason Kadem; [email protected]/[email protected]]

Feel free to contact me for help :)