MRI Style Transfer using GAN

To build a Generative Adversarial Network model to generate artificial MRI images of different contrast levels (T1-weighted vs T2-weighted Images) from existing MRI scans.

It can save MRI scan time while still enabling expert radiologists to properly give opinions using both T1-weighted as well as T2-weighted images.

Table of Contents

• Dataset Info
• Hardware Accelerator Used
• Project Collaborators

Dataset Information

The training data consists of both T1-weighted as well as T2-weighted images in pair.

Hardware Accelerator Used

Nvidia V100 Tensor Core GPU

• CUDA Version: 12.0
• Driver: NVIDIA-SMI 525.85.12
• GPU RAM: 16 GB

Project Collaborators

• Sachin Shekhar
• Bipul Kumar
• Divya Tyagi