Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3024-3028. doi: 10.1109/EMBC48229.2022.9871360.
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 with Cartesian sampling designs demonstrate the effectiveness of the proposed method.
磁共振成像（MRI）是一种强大的成像方式，它彻底改变了医学和生物学。高维 MRI 的成像速度通常受到限制，这限制了其实际应用。最近，低秩张量模型已被用于通过稀疏采样实现快速磁共振成像。大多数现有方法使用一些预定义的采样设计，而对于低秩张量成像，主动传感尚未得到探索。在本文中，我们引入了一种用于快速磁共振成像的主动低秩张量模型。我们提出了一种基于委员会查询模型的主动采样方法，利用低秩张量结构的优势。在具有笛卡尔采样设计的 3-D MRI 数据集上的数值实验证明了所提出方法的有效性。
