To this end, we solve the linear inverse problem of undersampled MRI reconstruction in a variational setting.
We propose a novel learning-based framework for image reconstruction particularly designed for training without ground truth data, which has three major building blocks: energy-based learning, a patch-based Wasserstein loss functional, and shared prior learning.
no code implementations • 30 Jun 2020 • Elena A. Kaye, Emily A. Aherne, Cihan Duzgol, Ida Häggström, Erich Kobler, Yousef Mazaheri, Maggie M Fung, Zhigang Zhang, Ricardo Otazo, Herbert A. Vargas, Oguz Akin
Compared to the reference images, the denoised images received higher image quality scores (p < 0. 0001).
In this work, we combine the variational formulation of inverse problems with deep learning by introducing the data-driven general-purpose total deep variation regularizer.
Diverse inverse problems in imaging can be cast as variational problems composed of a task-specific data fidelity term and a regularization term.
We investigate a well-known phenomenon of variational approaches in image processing, where typically the best image quality is achieved when the gradient flow process is stopped before converging to a stationary point.
Due to its high computational performance, i. e., reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow.