Projects

MR research is not rocket science, but also very cool 😃!

Overview In recent years, deep learning methods have made significant advancements in fast MRI reconstruction, yielding many promising results. However, challenges are constantly rising. Supervised methods for MR reconstruction are costly because of the collection of k-space label data and a model for one specific application is hard to apply to other applications. Apart from that, worries about the uncertainty caused by undersampling strategies and deep learning algorithms have limited their usage in clinical practice until now, which could lead to hallucinations. The application of generative image priors is one way out.

I’m currently working on diffusion models and its application on MRI reconstruction.
I use python, C/C++, shell and Latex. I also a developer for BART.
I have spent much time with Tensorflow/Pytorch/JAX.
I have a proven track record of developing and deploying generative models

Feel free to email me if you are interested in my projects.

Autogressive image diffusion: generation of image sequences and application in MRI reconstruction

autoregressive, diffusion models, image sequences, inverse problem, MRI reconstruction

Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models

sampling posterior, Bayesian inference, diffusion models, uncertainty estimation, inverse problem, MR image reconstruction, Monte Carlo, Markov chain

Generative Image Priors for MRI Reconstruction Trained from Magnitude-Only Images

generative pretraining, image priors, diffusion models, inverse problem, MR image reconstruction, proximal operator, optimization

Deploy generative image priors for image reconstruction using BART

MR image reconstruction, model deployment, TensorFlow C API, TensorFlow computation graph

MRI reconstruction using deep Bayesian estimation

generative model, PixelCNN, Bayesian estimation, MR image reconstruction, optimization

Generalized deep learning-based proximal gradient descent for MR reconstruction

MR image reconstruction, proximal gradient descent, denoising autoencoder