IEEE Trans Med Imaging. 2020 May;39(5):1681-1689. doi: 10.1109/TMI.2019.2954751. Epub 2019 Nov 21.
Quantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equations. Repeated Bloch simulations can be avoided by matching the measured signal with a precomputed signal dictionary on a discrete parameter grid (i.e. lookup table) as used in MR Fingerprinting. However, accurate estimation requires discretizing each parameter with a high resolution and consequently high computational and memory costs for dictionary generation, storage, and matching. Here, we reduce the required parameter resolution by approximating the signal between grid points through B-spline interpolation. The interpolant and its gradient are evaluated efficiently which enables a least-squares fitting method for parameter mapping. The resolution of each parameter was minimized while obtaining a user-specified interpolation accuracy. The method was evaluated by phantom and in-vivo experiments using fully-sampled and undersampled unbalanced (FISP) MR fingerprinting acquisitions. Bloch simulations incorporated relaxation effects (T,T) , proton density (PD ) , receiver phase ( φ ), transmit field inhomogeneity ( B ), and slice profile. Parameter maps were compared with those obtained from dictionary matching, where the parameter resolution was chosen to obtain similar signal (interpolation) accuracy. For both the phantom and the in-vivo acquisition, the proposed method approximated the parameter maps obtained through dictionary matching while reducing the parameter resolution in each dimension ( T,T,B ) by - on average - an order of magnitude. In effect, the applied dictionary was reduced from 1.47GB to 464KB . Furthermore, the proposed method was equally robust against undersampling artifacts as dictionary matching. Dictionary fitting with B-spline interpolation reduces the computational and memory costs of dictionary-based methods and is therefore a promising method for multi-parametric mapping.
定量 MRI 方法可以同时估计多个物理参数，通常需要通过 Bloch 方程定义的计算复杂信号模型进行拟合。通过将测量信号与离散参数网格（即查找表）上的预计算信号字典匹配，可以避免重复的 Bloch 模拟，这在磁共振指纹成像中得到了应用。然而，准确的估计需要以高分辨率离散每个参数，因此字典生成、存储和匹配的计算和存储成本都很高。在这里，我们通过 B 样条插值在网格点之间逼近信号来降低所需的参数分辨率。可以有效地评估插值函数及其梯度，从而为参数映射提供最小二乘拟合方法。在获得指定的插值精度的同时，最小化了每个参数的分辨率。该方法通过使用完全采样和欠采样不平衡（FISP）磁共振指纹成像采集的体模和体内实验进行了评估。Bloch 模拟结合了弛豫效应（T、T）、质子密度（PD）、接收器相位（φ）、发射场不均匀性（B）和切片轮廓。将参数图与通过字典匹配获得的参数图进行了比较，其中选择参数分辨率以获得相似的信号（插值）精度。对于体模和体内采集，所提出的方法在降低每个维度（T、T、B）的参数分辨率的同时，近似了通过字典匹配获得的参数图（平均）降低了一个数量级。实际上，应用的字典从 1.47GB 减少到 464KB。此外，所提出的方法与字典匹配一样对欠采样伪影具有同等的鲁棒性。B 样条插值的字典拟合降低了基于字典的方法的计算和存储成本，因此是一种很有前途的多参数映射方法。
