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Tractography Analysis Toolkit

Overview

The Tractography Analysis Toolkit is a Python program designed to derive statistical measures from white matter tracts obtained via tractography. This program quantifies the morphology and structural characteristics of various white matter tracts, enabling objective comparisons between tracts, such as the left and right Arcuate Fasciculus (AF_L and AF_R), or determining differences in curvature due to pathological alterations like tumors.

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Objectives

  • Compute a set of statistical measures for white matter tracts from tractography data.
  • Follow methodologies and metrics outlined in the paper "Shape analysis of the human association pathways" by Fang-Cheng Yeh.
  • Aggregate data from multiple subjects into structured Pandas DataFrames for easy analysis and visualization.

Features

  • Extraction of Tract Metrics: Calculate metrics such as the number of tracts, mean length, span, curl, elongation, diameter, volume, surface area, and irregularity.
  • Data Input and Organization: Dynamically handle input of tractography data files and organize data for each tract.
  • Output Format and Labeling: Output a structured report with the calculated metrics for each tract.
  • Automated Data Aggregation: Aggregate experiment results from multiple subjects stored across different directories into a Pandas DataFrame.

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Installation

  1. Clone the repository: 
    git clone https://github.com/your_username/tractography-analysis.git
cd tractography-analysis
  2. Install required dependencies 
    pip install -r requirements.txt

Usage

Compute Tract Statistics ```bash

from tract_analysis.data_aggregation import aggregate_results_to_dataframe, save_to_excel

# Define parameters
root_directory = '/path/to/root_directory'
file_paths = ['AF_L.tck', 'AF_R.tck']
reference_image = "/path/to/reference_image.nii"
output_file = '/path/to/output_file.xlsx'

# Aggregate results into dataframes
statistical_dataframes = aggregate_results_to_dataframe(root_directory, file_paths, reference_image)

# Save the aggregated dataframes to an Excel file
save_to_excel(statistical_dataframes, output_file)

Command Line Interface (CLI) ```bash

python -m tract_analysis.main -r /path/to/root_directory -f AF_L.tck AF_R.tck -i /path/to/reference_image.nii -o /path/to/output_file.xlsx

Project Structure ''''bash

tract_analysis/
│
├── __init__.py
├── calculations.py
├── data_aggregation.py
├── main.py
├── tractogram_processing.py
├── utils.py
│
└── tests/
    ├── __init__.py
    ├── test_calculations.py
    ├── test_data_aggregation.py
    ├── test_main.py
    ├── test_tractogram_processing.py
    ├── test_utils.py

Acknowledgments

This toolkit follows methodologies and metrics outlined in the paper "Shape analysis of the human association pathways" by Fang-Cheng Yeh.