Fast algorithms for GS-model-based image reconstruction in data-sharing Fourier imaging
Fast algorithms for GS-model-based image reconstruction in data-sharing Fourier imaging
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DOI：
10.1109/tmi.2003.815896
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发表时间：
2003-08-01
影响因子：
10.6
通讯作者：
Pelc, NJ
中科院分区：
文献类型：
--
作者：
Liang, ZP;Madore, B;Pelc, NJ
许多成像实验涉及获取图像的时间序列。为了提高成像速度，已经提出了几种“数据共享”方法，它们收集一个(或几个)高分辨率参考(S)和一系列简化的数据集。在图像重建中，使用了两种方法，即“钥孔”和基于广义序列重建的简化编码成像(RIGR)。Keyhole简单地用参考数据集(S)中的数据填充未测量的高频数据，而RIGR使用广义序列(GS)模型来恢复未测量数据，该模型的基函数是基于参考图像(S)构造的。这种对应关系提出了一种基于GS的图像重建的快速算法(和两个扩展)。提出的算法具有与Keyhole算法相同的计算复杂度，但更能捕捉高分辨率的动态信号变化。
Many imaging experiments involve acquiring a time series of images. To improve imaging speed, several "data-sharing" methods have been proposed, which collect one (or a few) high-resolution reference(s) and a sequence of reduced data sets. In image reconstruction, two methods, known as "Keyhole" and reduced-encoding imaging by generalized-series reconstruction (RIGR), have been used. Keyhole fills in the unmeasured high-frequency data simply with those from the reference data set(s), whereas RIGR recovers the unmeasured data using a generalized series (GS) model, of which the basis functions are constructed based on the reference image(s). This correspondence presents a fast algorithm (and two extensions) for GS-based image reconstruction. The proposed algorithms have the same computational complexity as the Keyhole algorithm, but are more capable of capturing high-resolution dynamic signal changes.