T2 relaxometry with indirect echo compensation from highly undersampled data.
T2 relaxometry with indirect echo compensation from highly undersampled data.
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DOI：
10.1002/mrm.24540
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发表时间：
2013-10
影响因子：
3.3
通讯作者：
Altbach, Maria I.
中科院分区：
文献类型：
--
作者：
Huang, Chuan;Bilgin, Ali;Barr, Tomoe;Altbach, Maria I.
关键词：
开发一种算法，可根据高度欠采样的多回波自旋回波 (MESE) 数据快速准确地估计 T2。该算法将基于模型的重建与基于切片解析扩展相位图 (SEPG) 模型的信号衰减相结合，目标是通过间接回波补偿从高度欠采样的径向 MESE 数据重建 T2 图。为了避免与 SEPG 模型的非线性相关的问题，使用主成分分解来线性化信号模型。所提出的通过基于 PCA 的线性化和间接回波补偿 (CURLIE) 算法进行的 CUrve 重建用于根据高度欠采样的数据估计 T2 曲线。 T2 图是通过将曲线拟合到 SEPG 模型获得的。当不考虑间接回声时，体模结果显示 T2 偏差（1.9% 至 18.4%）。当 CURLIE 重建与 SEPG 拟合一起进行时，即使对于高度欠采样（4% 采样），T2 偏差也会降低（<3.2%）。大脑、肝脏和心脏的体内实验遵循与模型相同的趋势。 CURLIE 重建与 SEPG 拟合相结合，能够根据高度欠采样的 MESE 径向数据进行准确的 T2 估计，从而产生快速的 T2 映射方法，而不会因间接回波引起的误差。
To develop an algorithm for fast and accurate T2 estimation from highly undersampled multi-echo spin-echo (MESE) data. The algorithm combines a model-based reconstruction with a signal decay based on the slice-resolved extended phase graph (SEPG) model with the goal of reconstructing T2 maps from highly undersampled radial MESE data with indirect echo compensation. To avoid problems associated with the nonlinearity of the SEPG model, principal component decomposition is used to linearize the signal model. The proposed CUrve Reconstruction via pca-based Linearization with Indirect Echo compensation (CURLIE) algorithm is used to estimate T2 curves from highly undersampled data. T2 maps are obtained by fitting the curves to the SEPG model. Results on phantoms showed T2 biases (1.9% to 18.4%) when indirect echoes are not taken into account. The T2 biases were reduced (<3.2%) when the CURLIE reconstruction was performed along with SEPG fitting even for high degrees of undersampling (4% sampled). Experiments in vivo for brain, liver and heart followed the same trend as the phantoms. The CURLIE reconstruction combined with SEPG fitting enables accurate T2 estimation from highly undersampled MESE radial data thus, yielding a fast T2 mapping method without errors caused by indirect echoes.
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影响因子：
5.9
作者：
Cieszanowski, A;Szeszkowski, W;Pruszynski, B
通讯作者：
Pruszynski, B
影响因子：
3.3
作者：
Huang, Chuan;Graff, Christian G.;Clarkson, Eric W.;Bilgin, Ali;Altbach, Maria I.
通讯作者：
Altbach, Maria I.
影响因子：
3.3
作者：
Doneva, Mariya;Boernert, Peter;Mertins, Alfred
通讯作者：
Mertins, Alfred
影响因子：
3.3
作者：
Deoni, SCL;Peters, TM;Rutt, BK
通讯作者：
Rutt, BK
影响因子：
3.3
作者：
Kim, Daniel;Jensen, Jens H.;Wu, Ed X.;Sheth, Sujit S.;Brittenham, Gary M.
通讯作者：
Brittenham, Gary M.