A rapid 3D fat-water decomposition method using globally optimal surface estimation (R-GOOSE).
A rapid 3D fat-water decomposition method using globally optimal surface estimation (R-GOOSE).
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DOI：
10.1002/mrm.26843
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发表时间：
2018-04
影响因子：
3.3
通讯作者：
Jacob M
中科院分区：
文献类型：
--
作者：
Cui C;Shah A;Wu X;Jacob M
为了改进我们以前的工作GOOSE脂肪水分解的图模型，以更高的计算效率和定量精度。新的推广的GOOSE脂肪水分解算法，继承了GOOSE的全局收敛保证，从而最大限度地减少脂肪水交换和相位包裹，介绍。提出了两种非等距图优化框架：一种称为快速GOOSE（R-GOOSE）的单步框架和一种称为多尺度快速GOOSE（m-RGOOSE）的多步框架。这两个框架都需要比GOOSE少得多的图层，从而使计算时间和内存需求减少一个数量级，使其易于适用于多维图水应用程序。在2012年ISMRM挑战数据集上，将新框架的定量准确性和计算时间与GOOSE进行了比较。这两个框架在所有数据集中实现了与GOOSE相同的高准确性。与GOOSE中的100层相比，新的图模型中只使用了8层，多分辨率框架中每个数据集的计算时间降低了一个数量级，约为5秒，而单步框架的平均运行时间也达到了8秒。与GOOSE相比，该方法提供了更低的运行时间和更高的精度的脂肪水分解结果。
To improve the graph model of our previous work GOOSE for fat-water decomposition with higher computational efficiency and quantitative accuracy. Novel generalizations of the GOOSE fat water decomposition algorithm, which inherit the global convergence guarantees of GOOSE thus minimizing fat-water swaps and phase wraps, are introduced. Two non-equidistant graph optimization frameworks: a single-step framework termed as rapid GOOSE (R-GOOSE), and a multi-step framework termed as multi-scale rapid GOOSE (m-RGOOSE) are proposed. Both frameworks require considerably fewer graph layers than GOOSE, resulting in an order of magnitude reduction in computational time and memory demand, making it readily applicable to multidimensional graph water applications. The quantitative accuracy and computational time of the novel frameworks are compared with GOOSE on the 2012 ISMRM Challenge datasets. Both frameworks accomplish the same level of high accuracy as GOOSE among all datasets. Compared to 100 layers in GOOSE, only 8 layers used in the new graph model, computational time is lowered by an order of magnitude to around five seconds for each dataset in the multi-resolution framework, while the single-step framework also achieves an average runtime of eight seconds. The proposed method provides fat-water decomposition results with a lower run-time and higher accuracy compared to GOOSE.
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影响因子：
3.3
作者：
MEYER, CH;PAULY, JM;NISHIMURA, DG
通讯作者：
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影响因子：
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