INDI (in-vivo diffusion)

In-vivo diffusion analysis (INDI)
Post-processing pipeline for in-vivo cardiac diffusion tensor imaging.

Table of Contents

• Introduction
• Installation
• How to
• License
• Aknowledgements

Introduction

This software is a post-processing pipeline designed for in-vivo cardiac diffusion tensor imaging. It currently accepts Siemens and Philips diffusion weighted DICOM data, as well as anonymised NIFTI data. It also supports both STEAM and spin-echo data.

After the data is loaded, the pipeline performs the following steps:

• Image registration
• Image curation
• Tensor fitting
• Segmentation
• Export results

INDI runs from the command line, and when processing a dataset for the first time, INDI will require user input (pop-up matplotlib windows) which will be saved for future runs.

For more details:

See documentation for details on the post-processing pipeline.

See YAML settings for run configuration details.

Installation

Software has been tested on:

• macOS 15.0 with python 3.12
• Ubuntu 24.04 with python 3.12
• Windows 10 with python 3.12

Clone the repository

Install git for your OS.

Then clone the repository.

git clone https://github.com/ImperialCollegeLondon/INDI.git

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Installation in macOS (Intel and Apple silicon)

You may need to instal Xcode and Xcode’s Command Line Tools package as well, with this command:

xcode-select --install

Then install homebrew.

With homebrew install python 3.12:

brew install [email protected]

Also install imagemagick:

brew install imagemagick

Install the python environment in the INDI root directory:

python3.12 -m venv .venv
source .venv/bin/activate
pip install -U pip setuptools wheel pip-tools
pip-sync requirements_mac.txt

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Installation in Ubuntu 24.04

install imagemagick:

sudo apt install imagemagick

install development tools:

sudo apt install git-all
sudo apt install build-essential
sudo apt install python3.12-venv
sudo apt-get install python3-tk python3-dev

Create the python environment in the INDI root directory:

ppython3 -m venv .venv
source .venv/bin/activate
pip install -U pip setuptools wheel pip-tools
pip-sync requirements_linux.txt

You may need to allow the display to show matplotlib windows with the following terminal command:

xhost +

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Installation Windows 10 with conda

Install miniforge: Miniforge

Create a new environment with conda:

conda create --name indi python=3.12
conda activate indi

Then install the required packages:

pip install -U pip setuptools wheel pip-tools
pip-sync requirements_win.txt

Install imagemagick.

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For development

Additional packages are required for development:

Install pre-commit

pre-commit install

Now pre-commit will run automatically on git commit. You can also run it manually with:

pre-commit run --all-files

This is required to ensure code quality and style before committing code changes.

Update requirements

The packages are pinned to specific versions in the requirements.in file. If you want to update the packages, you can do so with the following command (change the requirements file name according to the OS):

pip-compile --output-file=requirements_mac.txt
pip-sync requirements_mac.txt

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Basic usage example

We are going to post-process a synthetic phantom dataset with non-rigid distortions. Please unzip the phantom data.

The test_phantom_cdti_dicoms folder contains a subfolder named diffusion_images with the cdti simulated DICOMs. The DICOM files contain noisy diffusion weighted images with periodic non-rigid distortions, simulating a typical in-vivo scan.

INDI always looks recursively for subfolders named diffusion_images. The DICOM files must be inside this folder.

Before running we should have a look at the settings.yaml file, and check if the parameters makes sense. See YAML settings for more information. For this phantom example, the default settings should be fine.

Then run in the INDI python environment:

python main.py <data_path>

Where <data_path> is a folder that must contain at least a subfolder named diffusion_images with all the DICOM files.

In the video tutorial below we show how to run INDI with the phantom data:

License

INDI is licensed under the terms of the BSD license. Please see the LICENSE file.

If you use this software, please credit this website and "The CMR unit, Royal Brompton Hospital".

Aknowledgements

• Royal Brompton Hospital (Guy's and St Thomas' NHS Foundation Trust), London UK.
• Imperial College, London UK.
• This work was supported by the British Heart Foundation RG/19/1/34160 and RG/F/23/110115.
• Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation: 2024-337787.
• EPSRC Healthcare Technologies EP/X014010/1.