Comparison of reconstruction accuracy and efficiency among autocalibrating data-driven parallel imaging methods
Comparison of reconstruction accuracy and efficiency among autocalibrating data-driven parallel imaging methods
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DOI：
10.1002/mrm.21481
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发表时间：
2008-02-01
影响因子：
3.3
通讯作者：
Bammer, Roland
中科院分区：
文献类型：
--
作者：
Brau, Anja C. S.;Beatty, Philip J.;Bammer, Roland
自动校准的“数据驱动”并行成像（PI）方法近年来受到关注，因为它即使在具有挑战性的成像条件下也能够实现高质量的重建。这项工作的目的是对各种数据驱动的重建技术进行正式的比较研究，以评估它们在某些成像应用中的相对优势。在一个统一的理论框架内介绍了总共五种不同的重建方法，并使用一维（1D）加速的笛卡尔数据集在重建准确性和效率的特定指标方面进行了实验比较。结果表明，通过将重建过程视为两个离散的阶段，即校准阶段和合成阶段，可以定制重建路径，以利用某些数据域中的计算优势。提出了一种新的“分域”重建方法，该方法在k空间（k(x)，k(y)）中执行校准阶段，在混合（x，k(y)）空间中执行合成阶段，从而能够比以前使用传统技术更高效地执行高精度的二维邻域重建。这种分析可能有助于指导针对给定成像任务选择PI方法，以在最小的计算成本下实现高重建准确性。
The class of autocalibrating "data-driven" parallel imaging (PI) methods has gained attention in recent years due to its ability to achieve high quality reconstructions even under challenging imaging conditions. The aim of this work was to perform a formal comparative study of various data-driven reconstruction techniques to evaluate their relative merits for certain imaging applications. A total of five different reconstruction methods are presented within a consistent theoretical framework and experimentally compared in terms of the specific measures of reconstruction accuracy and efficiency using one-dimensional (1D)-accelerated Cartesian datasets. It is shown that by treating the reconstruction process as two discrete phases, a calibration phase and a synthesis phase, the reconstruction pathway can be tailored to exploit the computational advantages available in certain data domains. A new "split-domain" reconstruction method is presented that performs the calibration phase in k-space (k(x), k(y)) and the synthesis phase in a hybrid (x, k(y)) space, enabling highly accurate 2D neighborhood reconstructions to be performed more efficiently than previously possible with conventional techniques. This analysis may help guide the selection of PI methods for a given imaging task to achieve high reconstruction accuracy at minimal computational expense.