Project 5. AI/ML in Diffusion MRI processing

[viusic]

Hi @pjsjongsung @sreekarchigurupati @skoudoro,
I hope you're doing well. I wanted to reach out regarding "Project 5: Project ideas using AI/ML in Diffusion MRI processing" for GSoC'24. I am particularly interested in the "Diffusion Tensor Imaging (DTI) Reconstruction using Deep Learning" idea and would like to discuss it further.
The prospect of leveraging deep learning to enhance the quality and resolution of DTI data is intriguing to me, aligning well with my interests in medical imaging and machine learning. Before diving into the project, I'd love to get your insights on the specific goals and expectations you have in mind.
I've started brainstorming on the project and was thinking of approaching it by Implement a deep learning model (CNN/RNN) to reconstruct high-resolution Diffusion Tensor Imaging (DTI) from low-resolution data. Train the model on paired datasets, validate its performance, and integrate it into existing diffusion MRI pipelines. I anticipate

challenges in

1. Limited Annotated Data: Mitigate data scarcity by exploring transfer learning and synthetic data generation.

2. Computational Resources: Optimize the model for efficiency, potentially exploring model compression techniques and leveraging cloud-based resources for training.

Plan to Overcome Challenges:

Limited Annotated Data:

Explore transfer learning: Pretrain the model on a large dataset from related imaging domains before fine-tuning on the specific DTI task.
Synthetic Data Generation: Augment the training set with synthetically generated data to enrich the diversity of the dataset.
Computational Resources:

Model Optimization: Optimize the model architecture to balance performance and computational efficiency.
Cloud-Based Training: Leverage cloud-based resources for model training, ensuring scalability and access to powerful computing resources.

I would greatly appreciate your guidance and feedback on my initial thoughts. Additionally, I'm open to adjusting my approach based on your suggestions.
Thank you for considering my interest in this project. I am really excited about the opportunity and look forward to contributing to its success.
Best Regards

[pjsjongsung]

Hello and thank you for your interest. Sounds reasonable and we would like to hear more about your idea later when you submit a proposal. For example, is there a reason you are choosing DTI rather than DWI? (So a tensor model instead of an image volume?) Both challenges do seem like valid points, so would like to see what detailed solution you would have in mind.

One thing to note: we are a sub-organization under Python Software Foundation to promote open source. This means we would need some kind of contribution to our open source package before acceptance. If you could search for ways to contribute to DIPY, that would be awesome.

Looking forward to reading your proposal!

[sreekarchigurupati]

@viusic If you are looking for contribution opportunities to get started with, I would suggest you take a look at the beginner-friendly issues