While most of these methods focus on designing novel reconstruction networks or new training strategies for a given undersampling pattern, e. g., Cartesian undersampling or Non-Cartesian sampling, to date, there is limited research aiming to learn and optimize k-space sampling strategies using deep neural networks.
1 code implementation • 11 Aug 2021 • Beibin Li, Nicholas Nuechterlein, Erin Barney, Claire Foster, Minah Kim, Monique Mahony, Adham Atyabi, Li Feng, Quan Wang, Pamela Ventola, Linda Shapiro, Frederick Shic
Identifying oculomotor behaviors relevant for eye-tracking applications is a critical but often challenging task.
Intersections where vehicles are permitted to turn and interact with vulnerable road users (VRUs) like pedestrians and cyclists are among some of the most challenging locations for automated and accurate recognition of road users' behavior.
The undersampled images are generated by a fixed undersampling pattern in the training, and the trained network is then applied to reconstruct new images acquired with the same pattern in the inference.