Zhang Xiaodi, Zhou Zechen, Chen Shiyang, Chen Shuo, Li Rui, Hu Xiaoping
The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Health Sciences Research Building, 1760 Haygood Drive, Suite W200, Atlanta, GA 30322, USA; Center for Biomedical Imaging Research, Tsinghua University, Beijing 100084, China.
Center for Biomedical Imaging Research, Tsinghua University, Beijing 100084, China.
Magn Reson Imaging. 2017 Sep;41:53-62. doi: 10.1016/j.mri.2017.04.004. Epub 2017 Apr 19.
Magnetic resonance fingerprinting (MR fingerprinting or MRF) is a newly introduced quantitative magnetic resonance imaging technique, which enables simultaneous multi-parameter mapping in a single acquisition with improved time efficiency. The current MRF reconstruction method is based on dictionary matching, which may be limited by the discrete and finite nature of the dictionary and the computational cost associated with dictionary construction, storage and matching. In this paper, we describe a reconstruction method based on Kalman filter for MRF, which avoids the use of dictionary to obtain continuous MR parameter measurements. With this Kalman filter framework, the Bloch equation of inversion-recovery balanced steady state free-precession (IR-bSSFP) MRF sequence was derived to predict signal evolution, and acquired signal was entered to update the prediction. The algorithm can gradually estimate the accurate MR parameters during the recursive calculation. Single pixel and numeric brain phantom simulation were implemented with Kalman filter and the results were compared with those from dictionary matching reconstruction algorithm to demonstrate the feasibility and assess the performance of Kalman filter algorithm. The results demonstrated that Kalman filter algorithm is applicable for MRF reconstruction, eliminating the need for a pre-define dictionary and obtaining continuous MR parameter in contrast to the dictionary matching algorithm.
磁共振指纹识别（MR指纹识别或MRF）是一种新引入的定量磁共振成像技术，它能够在单次采集中实现多参数同时映射，提高了时间效率。当前的MRF重建方法基于字典匹配，这可能会受到字典的离散性和有限性以及与字典构建、存储和匹配相关的计算成本的限制。在本文中，我们描述了一种基于卡尔曼滤波器的MRF重建方法，该方法避免使用字典来获得连续的MR参数测量值。在这个卡尔曼滤波器框架下，推导了反转恢复平衡稳态自由进动（IR-bSSFP）MRF序列的布洛赫方程以预测信号演变，并将采集到的信号输入以更新预测。该算法在递归计算过程中可以逐步估计准确的MR参数。使用卡尔曼滤波器进行了单像素和数字脑模型仿真，并将结果与字典匹配重建算法的结果进行比较，以证明卡尔曼滤波器算法的可行性并评估其性能。结果表明，卡尔曼滤波器算法适用于MRF重建，与字典匹配算法相比，无需预定义字典并可获得连续的MR参数。
