# Active Sampling for Accelerated MRI with Low-Rank Tensors

arXiv: https://arxiv.org/abs/2012.12496 · HTML: https://mlsanity.com/paper/2012.12496/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Adaptive Sampling for Discovery](https://mlsanity.com/paper/2205.14829/) (2022)
- [High-Dimensional Uncertainty Quantification via Tensor Regression with Rank Determination and Adaptive Sampling](https://mlsanity.com/paper/2103.17236/) (2021)
- [SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis](https://mlsanity.com/paper/2609.05174/) (2026)

## Abstract (arXiv)

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.