Magnetic resonance imaging (MRI) is a powerful imaging modality that revolutionizes medicine and biology. The imaging speed of high-dimensional MRI is often limited, which constrains its practical utility. Recently, low-rank tensor models have been exploited to enable fast MR imaging with sparse sampling. Most existing methods use some pre-defined sampling design, and active sensing has not been explored for low-rank tensor imaging. In this paper, we introduce an active low-rank tensor model for fast MR imaging. We propose an active sampling method based on a Query-by-Committee model, making use of the benefits of low-rank tensor structure. Numerical experiments on a 3-D MRI data set demonstrate the effectiveness of the proposed method.
@article{arxiv.2012.12496,
title = {Active Sampling for Accelerated MRI with Low-Rank Tensors},
author = {Zichang He and Bo Zhao and Zheng Zhang},
journal= {arXiv preprint arXiv:2012.12496},
year = {2021}
}