Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learning

arXiv:2403.19966v1 Announce Type: cross
Abstract: Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging. The trained deep learning model typically lacks generalizability, and the dissimilarity among image datasets with different types of contrast leads to suboptimal learning performance. This paper proposes a meta-learning approach to efficiently learn image features from multiple MR image datasets. Our algorithm can perform multi-task learning to simultaneously reconstruct MR images acquired using different imaging …

abstract acquired arxiv contrast cs.cv data datasets deep learning eess.iv image image datasets imaging leads math.oc meta meta-learning mri paper performance type types