Log-Euclidean and DTI Introduction

This repository contains resources that helps in the understanding of Diffusion Tensor Imaging (DTI) processing using the Log-Euclidean framework. It is recommended to read the proposed articles in order to fully appreciate the Jupyter Notebook.

Articles

Diffusion Tensor Imaging Basics

• Mori, S., & Zhang, J. (2006). Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research.
• Maier-Hein, K. H., Neher, P. F., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., … Descoteaux, M. (2017). The challenge of mapping the human connectome based on diffusion tractography. Nature Communications

Log-Euclidean Framework

• Arsigny, V., Fillard, P., Pennec, X., & Ayache, N. (2006). Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magnetic Resonance in Medicine
• Arsigny, V., Fillard, P., Pennec, X., & Ayache, N. (2006). Geometric means in a novel vector space structure on symmetric positive-definite matrices. SIAM Journal on Matrix Analysis and Applications

Generative Adversarial Networks (GANs)

• Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems. Neural information processing systems foundation.
• Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN.
• Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision
• Yi, X., Walia, E., & Babyn, P. (2019). Generative adversarial network in medical imaging: A review. Medical Image Analysis

Deep Neural Networks for SPD Matrices Processing and DTI Synthesis

• Huang, Z., & Van Gool, L. (2017). A riemannian network for SPD matrix learning. In 31st AAAI Conference on Artificial Intelligence, AAAI 2017
• Huang, Z., Wu, J., & Van Gool, L. (2019). Manifold-Valued Image Generation with Wasserstein Generative Adversarial Nets. Proceedings of the AAAI Conference on Artificial Intelligence
• Gu, X., Knutsson, H., Nilsson, M., & Eklund, A. (2019). Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks. Lecture Notes in Computer Science
• Zhong, J., Wang, Y., Li, J., Xue, X., Liu, S., Wang, M., … Li, X. (2020). Inter-site harmonization based on dual generative adversarial networks for diffusion tensor imaging: Application to neonatal white matter development. BioMedical Engineering Online

Fiber Bundle Segmentation

• Wasserthal, J., Neher, P. F., & Maier-Hein, K. H. (2018). Fast and accurate white matter bundle segmentation.

Advanced Topics

• Ionescu, C., Vantzos, O., & Sminchisescu, C. (2015). Matrix backpropagation for deep networks with structured layers. In Proceedings of the IEEE International Conference on Computer Vision
• Brooks, D., Schwander, O., Barbaresco, F., Schneider, J.-Y., & Cord, M. (2019). Riemannian batch normalization for SPD neural networks.

Tools

Visualization

• 3D Slicer: https://www.slicer.org/
• Visdom: https://github.com/facebookresearch/visdom
• TrackVis: http://www.trackvis.org/

Deep Learning

• Pytorch: https://pytorch.org/
• Kerosene: https://github.com/banctilrobitaille/kerosene

Log-Euclidean Introduction Notebook

1. Clone the project
   • git clone https://github.com/banctilrobitaille/LogEuclidean-Intro.git
2. Setup a virtual environment
   • For Pycharm: https://www.jetbrains.com/help/pycharm/creating-virtual-environment.html
3. Install the dependencies:
   • Install Pytorch from https://pytorch.org/
   • Install other libraries: pip install -r requirements.txt
4. Install a Jupyter kernel
   • Run: ipython kernel install --user --name=.venv
   • See: https://medium.com/@eleroy/jupyter-notebook-in-a-virtual-environment-virtualenv-8f3c3448247
5. Run Jupyter and select the provided Jupyter notebook
   • Run: jupyter notebook
   • Select LogEuclideanIntro.ipynb and make sure that the selected kernel is : .venv