Doneva Mariya, Amthor Thomas, Koken Peter, Sommer Karsten, Börnert Peter
Philips Research Hamburg, Roentgenstrasse 24, Hamburg 22335, Germany.
Philips Research Hamburg, Roentgenstrasse 24, Hamburg 22335, Germany.
Magn Reson Imaging. 2017 Sep;41:41-52. doi: 10.1016/j.mri.2017.02.007. Epub 2017 Mar 3.
An iterative reconstruction method for undersampled magnetic resonance fingerprinting data is presented. The method performs the reconstruction entirely in k-space and is related to low rank matrix completion methods. A low dimensional data subspace is estimated from a small number of k-space locations fully sampled in the temporal direction and used to reconstruct the missing k-space samples before MRF dictionary matching. Performing the iterations in k-space eliminates the need for applying a forward and an inverse Fourier transform in each iteration required in previously proposed iterative reconstruction methods for undersampled MRF data. A projection onto the low dimensional data subspace is performed as a matrix multiplication instead of a singular value thresholding typically used in low rank matrix completion, further reducing the computational complexity of the reconstruction. The method is theoretically described and validated in phantom and in-vivo experiments. The quality of the parameter maps can be significantly improved compared to direct matching on undersampled data.
提出了一种用于欠采样磁共振指纹数据的迭代重建方法。该方法完全在k空间中进行重建，并且与低秩矩阵补全方法相关。从在时间方向上完全采样的少量k空间位置估计低维数据子空间，并在MRF字典匹配之前用于重建缺失的k空间样本。在k空间中执行迭代消除了在先前提出的用于欠采样MRF数据的迭代重建方法的每次迭代中应用正向和反向傅里叶变换的需要。对低维数据子空间的投影作为矩阵乘法执行，而不是通常在低秩矩阵补全中使用的奇异值阈值化，进一步降低了重建的计算复杂度。该方法在体模和体内实验中进行了理论描述和验证。与对欠采样数据进行直接匹配相比，参数图的质量可以得到显著提高。
