论文标题
GFB-MRF：平行的空间和Bloch歧管正则迭代重建方法用于MR指纹
GFB-MRF: A parallel spatial and Bloch manifold regularized iterative reconstruction method for MR Fingerprinting
论文作者
论文摘要
磁共振指纹（MRF）基于一系列非常高的采样图像来重建组织图。为了能够执行MRF重建，最先进的MRF方法依赖于MR Physics（Bloch方程）等先验，并且还可能使用一些其他的低级别或空间正则化。但是，据我们所知，这三个正规化并未在联合重建中一起应用。原因是，将有效的多个正规化纳入单个MRF优化算法中确实具有挑战性。结果，这些方法中的大多数对噪声都不强大，尤其是当序列长度短时。在本文中，我们提出了一个新方法家族，其中除了Bloch歧管正规化之外，在图像上还应用了空间和低级别的正规化。我们显示了数字幻影和NIST幻影扫描，以及志愿者扫描，提出的方法可以显着改善估计的组织图的质量。
Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on a sequence of very highly undersampled images. In order to be able to perform MRF reconstruction, state-of-the-art MRF methods rely on priors such as the MR physics (Bloch equations) and might also use some additional low-rank or spatial regularization. However to our knowledge these three regularizations are not applied together in a joint reconstruction. The reason is that it is indeed challenging to incorporate effectively multiple regularizations in a single MRF optimization algorithm. As a result most of these methods are not robust to noise especially when the sequence length is short. In this paper, we propose a family of new methods where spatial and low-rank regularizations, in addition to the Bloch manifold regularization, are applied on the images. We show on digital phantom and NIST phantom scans, as well as volunteer scans that the proposed methods bring significant improvement in the quality of the estimated tissue maps.
