Deep Learning for Image Reconstruction: Data Requirements and Its Application to Motion-Correction in MRI
MCML Authors
Abstract
Abstract
In the first part of this work, we study data requirements for deep learning-based image reconstruction. We establish empirical scaling laws for supervised learning, the sample complexity of self-supervised learning and investigate test-time-training for improved data efficiency. In the second part, we propose a novel deep learning-based method as well as a novel benchmark dataset for the challenging problem of 3D MRI reconstruction under patient motion.
BibTeXKey: Klu25