Conference Paper

A diffusion transition process for accelerated MRI reconstruction that is anchored on low-frequency k-space data.

A diffusion model for MRI reconstruction whose forward process progressively removes k-space frequencies while adding Gaussian noise, mimicking undersampled acquisitions.

Super-Resolution Diffusion Bridges (SRDB) learn a direct mapping from low- to high-resolution MR images instead of starting from Gaussian noise, improving structural consistency.

Diff-Med-Synth combines denoising diffusion models with GANs to generate realistic and diverse medical images from noise with fast sampling.

ResDiff combines a resolution-decreasing operator with diffusion noise scheduling, achieving competitive MRI reconstructions in far less sampling time.

A personalized federated learning approach that trains one unified MRI contrast synthesis model across institutions while adapting to each site and task.

A federated MRI contrast translation model that adaptively normalizes generator feature maps with site-specific latent variables to stay reliable under domain shifts.

Adding skip connections to GAN generators counters the performance drop of deeper networks in one-to-one MRI contrast synthesis.