In silico proof of concept for reconstruction of blood flow velocity with 4D phase contrast MRI and variational autoencoders

In this work, we investigate a new deep learning method for the reconstruction of blood velocity from 4D phase contrast MRI measurements based on variational autoencoders. The principle of this approach is to leverage lower resolution velocity measurements to train a variational autoencoder. The approach identifies optimal latent variables within the latent space and uses the generative process of the variational autoencoder to enhance reconstruction accuracy. Realistic simulations demonstrate that the proposed deep learning approach outperforms classical regularisation methods under undersampled and noisy conditions. The nonlinear encoding of the velocity field achieves better reconstruction results and compensates for missing information.