Toeplitz-based iterative image reconstruction for MRI with correction for magnetic field inhomogeneity
Toeplitz-based iterative image reconstruction for MRI with correction for magnetic field inhomogeneity
复制标题
DOI：
10.1109/tsp.2005.853152
复制
发表时间：
2005-09-01
影响因子：
5.4
通讯作者：
Noll, DC
中科院分区：
文献类型：
--
作者：
Fessler, JA;Lee, S;Noll, DC
在某些类型的磁共振(MR)成像中，特别是功能性脑扫描中，传统的傅立叶模型是不准确的。由不完善的主场和磁化率变化引起的磁场不均匀会在用传统傅立叶方法重建的图像中引起失真。这些伪影阻碍了功能性磁共振成像(FMRI)在靠近空气/组织界面的脑区的使用。最近，结合共轭梯度(CG)算法和非均匀FFT(NUFFT)运算的迭代方法被证明比共轭相位方法提供了相当大的改善的图像质量。然而，对于非笛卡尔k-空间轨迹，每次CG-NUFFT迭代都需要大量的k-空间内插；这些操作的计算代价很高，并且不适合快速的硬件实现。本文提出了一种基于CG算法和某些Toeplitz矩阵的场校正MR图像重建的快速迭代方法。这种CG-Toeplitz方法只需要对初始迭代进行k空间内插，此后只需要快速傅立叶变换(FFT)。仿真结果表明，所提出的CG-Toeplitz方法具有与CG-NUFFT方法相同的图像质量，并且显著减少了计算时间。
In some types of magnetic resonance (MR) imaging, particularly functional brain scans, the conventional Fourier model for the measurements is inaccurate. Magnetic field inhomogeneities, which are caused by imperfect main fields and by magnetic susceptibility variations, induce distortions in images that are reconstructed by conventional Fourier methods. These artifacts hamper the use of functional MR imaging (fMRI) in brain regions near air/tissue interfaces. Recently, iterative methods that combine the conjugate gradient (CG) algorithm with nonuniform FFT (NUFFT) operations have been shown to provide considerably improved image quality relative to the conjugate-phase method. However, for non-Cartesian k-space trajectories, each CG-NUFFT iteration requires numerous k-space interpolations; these are operations that are computationally expensive and poorly suited to fast hardware implementations. This paper proposes a faster iterative approach to field-corrected MR image reconstruction based on the CG algorithm and certain Toeplitz matrices. This CG-Toeplitz approach requires k-space interpolations only for the initial iteration; thereafter, only fast Fourier transforms (FFTs) are required. Simulation results show that the proposed CG-Toeplitz approach produces equivalent image quality as the CG-NUFFT method with significantly reduced computation time.