标题： 时空图用于动态MRI重建 显示英文标题

标题： Spatiotemporal Maps for Dynamic MRI Reconstruction

摘要： 部分可分离函数（PSF）模型在动态MRI重建中被广泛采用，也是许多重建方法的基础信号模型，包括那些依赖于低秩假设的方法。尽管PSF模型在某些应用中提供了动态MRI信号的简洁表示，但在体素在不同空间位置表现出不同时间/频谱特性的场景中，其表示能力往往会下降。在本工作中，我们通过提出一种新的模型——时空映射（STMs）来解决这一限制，该模型利用了(k, t)-空间的自回归特性。STM模型将时空MRI信号分解为多个成分的和，每个成分由一个空间函数和一个依赖于空间位置的时间函数的乘积组成。所提出的模型可以看作是PSF模型的扩展，其中的时间函数与空间位置无关。我们表明，可以通过使用先进的信号处理和随机线性代数技术，从自校准数据中高效地计算出时空映射，使STMs能够作为加速动态MRI许多重建框架的一部分。作为概念验证示例，我们展示了STMs可用于重建2D单通道动物胃肠道MRI数据和3D多通道人功能MRI数据。 显示英文摘要

摘要： The partially separable functions (PSF) model is commonly adopted in dynamic MRI reconstruction, as is the underlying signal model in many reconstruction methods including the ones relying on low-rank assumptions. Even though the PSF model offers a parsimonious representation of the dynamic MRI signal in several applications, its representation capabilities tend to decrease in scenarios where voxels present different temporal/spectral characteristics at different spatial locations. In this work we account for this limitation by proposing a new model, called spatiotemporal maps (STMs), that leverages autoregressive properties of (k, t)-space. The STM model decomposes the spatiotemporal MRI signal into a sum of components, each one consisting of a product between a spatial function and a temporal function that depends on the spatial location. The proposed model can be interpreted as an extension of the PSF model whose temporal functions are independent of the spatial location. We show that spatiotemporal maps can be efficiently computed from autocalibration data by using advanced signal processing and randomized linear algebra techniques, enabling STMs to be used as part of many reconstruction frameworks for accelerated dynamic MRI. As proof-of-concept illustrations, we show that STMs can be used to reconstruct both 2D single-channel animal gastrointestinal MRI data and 3D multichannel human functional MRI data.