论文标题
使用低级张量的加速MRI进行主动采样
Active Sampling for Accelerated MRI with Low-Rank Tensors
论文作者
论文摘要
磁共振成像（MRI）是一种强大的成像方式，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，这限制了其实际效用。最近，已经利用低量张量模型来实现稀疏采样的快速MR成像。大多数现有方法都使用一些预定义的采样设计，并且尚未探索用于低级张量成像的主动传感。在本文中，我们引入了一种用于快速MR成像的主动低级张量模型。我们提出了一种基于查询模型的主动采样方法，利用了低级张量结构的益处。 3-D MRI数据集的数值实验证明了该方法的有效性。
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.
