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Cardiac MRI Image Synthesis using Diffusion Models

This project implements a Denoising Diffusion Probabilistic Model (DDPM) for generating high-resolution cardiac MRI images. The framework leverages attention-enhanced UNet architectures and progressive noise scheduling to reconstruct realistic grayscale images from noise.

Overview

  • Architecture: Modified UNet with attention blocks.
  • Dataset: Cardiac cine MRI slices in grayscale.
  • Diffusion Framework: diffusers library with custom scheduler and training logic.
  • Training: Mixed precision with accelerate, cosine learning rate decay, and checkpoint resumption.
  • Output: Synthesized images and models saved per defined intervals.

Components

  • main.py: Launches training with configuration, dataloaders, and scheduler.
  • model.py: Defines the attention-augmented UNet structure.
  • train.py: Training loop with logging, evaluation, and checkpointing.
  • generate.py: Generates synthetic MRI samples using a trained model.
  • dataset.py: Custom PyTorch Dataset with augmentations.
  • config.py: Training hyperparameters and device setup.
  • utils.py: Utility functions for evaluation, checkpoint loading, and image grid creation.

Usage

python main.py