T2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing.
T2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing.
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DOI：
10.1002/mrm.23128
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发表时间：
2012-05
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;Graff, Christian G.;Clarkson, Eric W.;Bilgin, Ali;Altbach, Maria I.
最近，人们对定量MR参数的兴趣与日俱增，以提高诊断和治疗水平。参数映射需要以不同的定时采集多幅图像，通常导致采集时间较长。虽然通过获取欠采样数据可以减少采集时间，但从欠采样数据中获得准确的参数估计是一个具有挑战性的问题，特别是对于具有高空间频率含量的结构。在这项工作中，主成分分析(PCA)与一种基于模型的算法相结合，从高度欠采样的放射状MRI数据中重建选定的主成分系数图。这种新的方法将优化问题的成本函数线性化，从而产生更准确和可靠的MR参数映射估计。本文提出的基于压缩感知的主成分系数图重建算法(REPCOM)在体模和活体实验中得到了验证，并与之前针对欠采样数据开发的两种算法进行了比较。
Recently, there has been an increased interest in quantitative MR parameters to improve diagnosis and treatment. Parameter mapping requires multiple images acquired with different timings usually resulting in long acquisition times. While acquisition time can be reduced by acquiring undersampled data, obtaining accurate estimates of parameters from undersampled data is a challenging problem, in particular for structures with high spatial frequency content. In this work, Principal Component Analysis (PCA) is combined with a model-based algorithm to reconstruct maps of selected principal component coefficients from highly undersampled radial MRI data. This novel approach linearizes the cost function of the optimization problem yielding a more accurate and reliable estimation of MR parameter maps. The proposed algorithm - REconstruction of Principal COmponent coefficient Maps (REPCOM) using Compressed Sensing - is demonstrated in phantoms and in vivo and compared to two other algorithms previously developed for undersampled data.
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影响因子：
5.9
作者：
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通讯作者：
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