Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learning
Wanyu Bian Albert Jang Fang Liu
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 sequences with different image
contrasts. The experiment results demonstrate the ability of our new
meta-learning reconstruction method to successfully reconstruct
highly-undersampled k-space data from multiple MRI datasets simultaneously,
outperforming other compelling reconstruction methods previously developed for
single-task learning.