迭代重建
成像体模
正规化(语言学)
压缩传感
计算机科学
算法
反问题
k-空间
共轭梯度法
迭代法
先验概率
数学
人工智能
物理
代数重建技术
数学优化
计算机视觉
重建算法
贝叶斯概率
傅里叶变换
数学分析
光学
作者
Michael Lustig,John M. Pauly
摘要
A new approach to autocalibrating, coil-by-coil parallel imaging reconstruction, is presented. It is a generalized reconstruction framework based on self-consistency. The reconstruction problem is formulated as an optimization that yields the most consistent solution with the calibration and acquisition data. The approach is general and can accurately reconstruct images from arbitrary k-space sampling patterns. The formulation can flexibly incorporate additional image priors such as off-resonance correction and regularization terms that appear in compressed sensing. Several iterative strategies to solve the posed reconstruction problem in both image and k-space domain are presented. These are based on a projection over convex sets and conjugate gradient algorithms. Phantom and in vivo studies demonstrate efficient reconstructions from undersampled Cartesian and spiral trajectories. Reconstructions that include off-resonance correction and nonlinear l(1)-wavelet regularization are also demonstrated.
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