Lin Claire Yilin, Fessler Jeffrey A
Department of Mathematics, University of Michigan, Ann Arbor, MI, 48109 USA.
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 USA.
IEEE Trans Comput Imaging. 2020;6:1451-1458. doi: 10.1109/TCI.2020.3031082. Epub 2020 Oct 15.
Magnetic field inhomogeneity estimation is important in some types of magnetic resonance imaging (MRI), including field-corrected reconstruction for fast MRI with long readout times, and chemical shift based water-fat imaging. Regularized field map estimation methods that account for phase wrapping and noise involve nonconvex cost functions that require iterative algorithms. Most existing minimization techniques were computationally or memory intensive for 3D datasets, and are designed for single-coil MRI. This paper considers 3D MRI with optional consideration of coil sensitivity, and addresses the multi-echo field map estimation and water-fat imaging problem. Our efficient algorithm uses a preconditioned nonlinear conjugate gradient method based on an incomplete Cholesky factorization of the Hessian of the cost function, along with a monotonic line search. Numerical experiments show the computational advantage of the proposed algorithm over state-of-the-art methods with similar memory requirements.
磁场不均匀性估计在某些类型的磁共振成像（MRI）中很重要，包括具有长读出时间的快速MRI的场校正重建以及基于化学位移的水脂成像。考虑相位缠绕和噪声的正则化场图估计方法涉及需要迭代算法的非凸成本函数。大多数现有的最小化技术对于三维数据集来说计算量或内存需求很大，并且是为单线圈MRI设计的。本文考虑了可选择考虑线圈灵敏度的三维MRI，并解决了多回波场图估计和水脂成像问题。我们的高效算法使用基于成本函数海森矩阵不完全Cholesky分解的预处理非线性共轭梯度法，以及单调线搜索。数值实验表明，与具有相似内存需求的现有方法相比，该算法具有计算优势。
