Highly undersampled schemes in magnetic resonance fingerprinting (MRF) typically lead to aliasing artifacts in reconstructed images, thereby reducing quantitative imaging accuracy. Existing studies mainly focus on improving the reconstruction quality by incorporating temporal or spatial data priors. However, these methods seldom exploit the underlying MRF data structure driven by imaging physics and usually suffer from high computational complexity due to the high-dimensional nature of MRF data. In addition, data priors constructed in a pixel-wise manner struggle to incorporate non-local and non-linear correlations. To address these issues, we introduce a novel MRF reconstruction framework based on the graph embedding framework, exploiting non-linear and non-local redundancies in MRF data. Our work remodels MRF data and parameter maps as graph nodes, redefining the MRF reconstruction problem as a structure-preserved graph embedding problem. Furthermore, we propose a novel scheme for accurately estimating the underlying graph structure, demonstrating that the parameter nodes inherently form a low-dimensional representation of the high-dimensional MRF data nodes. The reconstruction framework is then built by preserving the intrinsic graph structure between MRF data nodes and parameter nodes and extended to exploiting the globality of graph structure. Our approach integrates the MRF data recovery and parameter map estimation into a single optimization problem, facilitating reconstructions geared toward quantitative accuracy. Moreover, by introducing graph representation, our methods substantially reduce the computational complexity, with the computational cost showing a minimal increase as the data acquisition length grows. Experiments show that the proposed method can reconstruct high-quality MRF data and multiple parameter maps within reduced computational time.

中文翻译：

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通过结构保留的图形嵌入框架改进 MRF 重建


磁共振指纹识别 （MRF） 中的高度欠采样方案通常会导致重建图像中出现混叠伪影，从而降低定量成像的准确性。现有研究主要集中在通过结合时间或空间数据先验来提高重建质量。然而，这些方法很少利用由成像物理学驱动的底层 MRF 数据结构，并且由于 MRF 数据的高维性质，通常具有很高的计算复杂性。此外，以像素方式构建的数据先验难以纳入非局部和非线性相关性。为了解决这些问题，我们引入了一种基于图嵌入框架的新型 MRF 重建框架，利用了 MRF 数据中的非线性和非局部冗余。我们的工作将 MRF 数据和参数映射重新建模为图节点，将 MRF 重建问题重新定义为结构保留的图嵌入问题。此外，我们提出了一种准确估计底层图结构的新方案，证明参数节点本质上形成了高维 MRF 数据节点的低维表示。然后，通过保留 MRF 数据节点和参数节点之间的内禀图结构来构建重建框架，并扩展到利用图结构的全局性。我们的方法将 MRF 数据恢复和参数映射估计集成到一个优化问题中，促进了面向定量准确性的重建。此外，通过引入图表示，我们的方法大大降低了计算复杂性，随着数据采集长度的增加，计算成本的增加幅度最小。 实验表明，所提方法能够在更短的时间内重建高质量的MRF数据和多参数映射。