Correlation-Weighted Sparse Representation for Robust Liver DCE-MRI Decomposition Registration
Correlation-Weighted Sparse Representation for Robust Liver DCE-MRI Decomposition Registration
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稳健肝脏 DCE-MRI 分解配准的相关加权稀疏表示
DOI：
10.1109/tmi.2019.2906493
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发表时间：
2019-03
影响因子：
10.6
通讯作者：
Feng Qianjin
中科院分区：
文献类型：
--
作者：
Zhou Yujia;Sun Yuhang;Yang Wei;Lu Zhentai;Huang Meiyan;Lu Lijun;Zhang Yu;Feng Yanqiu;Chen Wufan;Feng Qianjin
由于造影剂引起的强度变化，对肝脏动态对比增强磁共振（DCE-MR）成像进行精确的运动校正仍然具有挑战性。这种变化导致了传统的基于灰度的配准方法的失败。为了解决这个问题，我们提出了一个相关加权稀疏表示框架，以分离造影剂从原始肝脏DCE-MR图像。该框架允许随着时间的推移而没有强度变化的运动分量的鲁棒配准。现有的稀疏编码技术从手动标记的字典中恢复仅包含造影剂（称为对比度增强分量）的3D图像，所述手动标记的字典的列具有与原始3D体积相同的大小（3D-t模式）。恢复目标（3D体积）的高维数和未增强图像与增强图像之间的不可分割性使得精确编码变得困难。在本文中，我们重建一个理想的时间-强度曲线只包含造影剂（称为造影剂曲线），并恢复它从转置字典（t-3D模式），其列已被更新为原始的时间-强度曲线。目标（1D曲线）的低维度和造影剂曲线与非造影剂曲线之间的显著组间差异可以估计一系列纯造影剂曲线。一个“相关加权”的约束被引入到一个编码子集的选择与更多的造影剂曲线，导致一个有效的和准确的稀疏恢复过程。然后，对比度增强分量可以通过求解稀疏系数的映射和理想曲线来估计，并从原始DCE-MRI中减去。最后，我们配准去增强的图像，并将获得的变形场应用于原始DCE-MRI，以达到运动校正的目的。我们对模拟和真实的肝脏DCE-MRI数据进行了实验。实验结果表明，该方法与现有的DCE-MRI配准方法相比，具有更好的配准性能和更低的计算效率。
Conducting an accurate motion correction of liver dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging remains challenging because of intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, we propose a correlation-weighted sparse representation framework to separate the contrast agent from original liver DCE-MR images. This framework allows the robust registration of motion components over time without intensity variances. Existing sparse coding techniques recover a 3D image containing only contrast agents (named contrast enhancement component) from a manually labeled dictionary, whose column has the same size with the original 3D volume (3D-t mode). The high dimension of the recovery target (3D volume) and the indistinguishability between the unenhanced and enhanced images make accurate coding difficult. In this paper, we predefine an ideal time-intensity curve containing only contrast agents (named contrast agent curve) and recover it from the transpose dictionary (t-3D mode), whose column has been updated into the original time-intensity curves. The low dimension of the target (1D curve) and the significant intergroup difference between contrast agent curves and non-contrast agent curves can estimate a series of pure contrast agent curves. A “correlation-weighted” constraint is introduced for the selection of a coding subset with more contrast agent curves, leading to an efficient and accurate sparse recovery process. Then, the contrast enhancement component can be estimated by the solved sparse coefficients’ map and the ideal curve and subtracted from the original DCE-MRI. Finally, we register the de-enhanced images and apply the obtained deformation fields for the original DCE-MRI to achieve the goal of motion correction. We conduct the experiments on both simulated and real liver DCE-MRI data. Compared with other state-of-the-art DCE-MRI registration methods, the experimental results show that our method achieves a better registration performance with less computational efficiency.
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影响因子：
10.9
作者：
Hamy, Valentin;Dikaios, Nikolaos;Atkinson, David
通讯作者：
Atkinson, David
影响因子：
4.4
作者：
Tamada, Tsutomu;Ito, Katsuyoshi;Yamashita, Takenori
通讯作者：
Yamashita, Takenori
影响因子：
4.4
作者：
Aronhime, Shimon;Calcagno, Claudia;Jajamovich, Guido H.;Dyvorne, Hadrien Arezki;Robson, Philip;Dieterich, Douglas;Fiel, M. Isabel;Martel-Laferriere, Valerie;Chatterji, Manjil;Rusinek, Henry;Taouli, Bachir
通讯作者：
Taouli, Bachir
基于稳健主成分分析的肝脏 DCE-MRI 歧管空间配准
影响因子：
4.6
作者：
Feng Q;Zhou Y;Li X;Mei Y;Lu Z;Zhang Y;Feng Y;Liu Y;Yang W;Chen W
通讯作者：
Chen W
DOI：
--
发表时间：
2011
期刊：
--
影响因子：
--
作者：
Jun Liu;S. Ji;Jieping Ye
通讯作者：
Jun Liu;S. Ji;Jieping Ye