Fat water decomposition using globally optimal surface estimation (GOOSE) algorithm.
Fat water decomposition using globally optimal surface estimation (GOOSE) algorithm.
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DOI：
10.1002/mrm.25193
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发表时间：
2015-03
影响因子：
3.3
通讯作者：
Jacob, Mathews
中科院分区：
文献类型：
--
作者：
Cui, Chen;Wu, Xiaodong;Newell, John D.;Jacob, Mathews
本文重点研究了一种新的非迭代脂肪水分解算法，该算法对脂肪水交换和相关的模糊性更鲁棒。场图估计被重新表述为一个约束表面估计问题，以利用场的空间平滑性，从而最大限度地减少恢复中的模糊性。具体地，场图中的差异引起的相邻体素之间的频移被约束为在有限范围内。上述问题的离散化产生图优化方案，其中图的每个节点仅与少数其他节点连接。由于图的低连通性，使用非迭代图切割算法有效地解决了这个问题。保证了约束优化问题的全局最小值。将该算法的性能与最新方案的性能进行了比较。还根据参考数据进行了定量比较。所提出的算法被观察到产生更强大的脂肪水的估计与更少的脂肪水交换和更好的定量结果比其他国家的最先进的算法在一系列具有挑战性的应用。该算法能够大大减少具有挑战性的脂肪水分解问题的交换。实验表明，使用显式的平滑约束的字段地图估计和解决问题，使用全局收敛的图切割优化算法的好处。
This paper focuses on developing a novel non-iterative fat water decomposition algorithm more robust to fat water swaps and related ambiguities. Field map estimation is reformulated as a constrained surface estimation problem to exploit the spatial smoothness of the field, thus minimizing the ambiguities in the recovery. Specifically, the differences in the field map induced frequency shift between adjacent voxels are constrained to be in a finite range. The discretization of the above problem yields a graph optimization scheme, where each node of the graph is only connected with few other nodes. Thanks to the low graph connectivity, the problem is solved efficiently using a non-iterative graph cut algorithm. The global minimum of the constrained optimization problem is guaranteed. The performance of the algorithm is compared with that of state-of-the-art schemes. Quantitative comparisons are also made against reference data. The proposed algorithm is observed to yield more robust fat water estimates with fewer fat water swaps and better quantitative results than other state-of-the-art algorithms in a range of challenging applications. The proposed algorithm is capable of considerably reducing the swaps in challenging fat water decomposition problems. The experiments demonstrate the benefit of using explicit smoothness constraints in field map estimation and solving the problem using a globally convergent graph-cut optimization algorithm.
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影响因子：
3.3
作者：
MEYER, CH;PAULY, JM;NISHIMURA, DG
通讯作者：
NISHIMURA, DG
影响因子：
3.3
作者：
Hernando, D.;Haldar, J. P.;Liang, Z. -P.
通讯作者：
Liang, Z. -P.
DOI：
10.1109/tpami.2006.19
发表时间：
2006-01-01
影响因子：
23.6
作者：
Li, K;Wu, XD;Sonka, M
通讯作者：
Sonka, M
影响因子：
3.3
作者：
Hernando, Diego;Kellman, P.;Haldar, J. P.;Liang, Z-P.
通讯作者：
Liang, Z-P.
影响因子：
3.3
作者：
Reeder, SB;Pineda, AR;Pelc, NJ
通讯作者：
Pelc, NJ