SimCLR-MRI Pre-trained Encoder (SeqAug)
This repository contains a pre-trained 3D CNN encoder for MRI analysis. The model was trained using contrastive learning (SimCLR) with Bloch equation simulations to generate multi-contrast views of the same anatomy.
Model Description
The encoder is a 3D CNN with 5 convolutional blocks (64, 128, 256, 512, 768 channels), outputting 768-dimensional features. This SeqAug variant treats different simulated MRI sequences as strong augmentations during contrastive learning, encouraging sequence-robust representations.
Training Procedure
- Pre-training Data: 51 qMRI datasets (22 healthy, 29 stroke subjects)
- Augmentations: Bloch simulation-based sequence augmentation + standard transformations
- Input: 3D MRI volumes (96×96×96)
- Output: 768-dimensional feature vectors
Intended Uses
This encoder is particularly suited for:
- Cross-sequence transfer learning
- Multi-contrast MRI analysis
- Sequence-robust feature extraction
