论文标题
3D MRI中有效的正规现场图估计
Efficient Regularized Field Map Estimation in 3D MRI
论文作者
论文摘要
在某些类型的磁共振成像（MRI）中，磁场不均匀性估计很重要，包括长时间读取时间的快速MRI的现场校正重建，以及基于化学移位的水脂成像。考虑到相位包装和噪声的正则化现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是计算或3D数据集的内存密集型，并且专为单线圈MRI而设计。本文考虑了3D MRI，可选地考虑线圈灵敏度，并解决了多回波场图估计和水脂成像问题。我们的有效算法基于成本函数的Hessian的不完整的Cholesky分解，使用了预处理的非线性共轭梯度方法，以及单调线路搜索。数值实验显示了所提出的算法比具有相似内存要求的最先进方法的计算优势。
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.
