Reconstruction of dynamic image series from undersampled MRI data using data-driven model consistency condition (MOCCO).
Reconstruction of dynamic image series from undersampled MRI data using data-driven model consistency condition (MOCCO).
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DOI：
10.1002/mrm.25513
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发表时间：
2015-11
影响因子：
3.3
通讯作者：
Samsonov, Alexey A.
中科院分区：
文献类型：
--
作者：
Velikina, Julia V.;Samsonov, Alexey A.
关键词：
通过开发一种新的图像重建技术来加速动态MR成像，该技术使用从训练数据中预先估计的低秩时间信号模型。我们介绍了模型一致性条件（MOCCO）的技术，利用时间模型来正则化的重建，而不约束的解决方案是低秩的相关技术。这是通过使用数据驱动模型来设计用于压缩感测类型正则化的变换来实现的。在不过度惩罚偏离信号的情况下，对模型的一般遵守的强制执行允许恢复满秩解决方案。我们的方法进行了比较，标准的低秩的方法，利用模型为基础的降维幻影和患者检查的时间分辨对比增强血管造影术（CE MRA）和心脏电影成像。我们研究了所有方法对降秩和时间子空间建模误差的敏感性。与标准方法相比，MOCCO表现出对建模误差的敏感性降低。全秩MOCCO解决方案在高加速CE MRA（加速高达27）和心脏CINE（加速高达15）数据中显示出时间保真度和混叠/噪声抑制的显著改善。MOCCO克服了先前提出的基于预估时间模型的方法的几个重要缺陷，并允许从高度欠采样的CE-MRA和心脏CINE数据恢复高质量的图像。
To accelerate dynamic MR imaging through development of a novel image reconstruction technique using low-rank temporal signal models pre-estimated from training data. We introduce the MOdel Consistency COndition (MOCCO) technique that utilizes temporal models to regularize the reconstruction without constraining the solution to be low-rank as performed in related techniques. This is achieved by using a data-driven model to design a transform for compressed sensing-type regularization. The enforcement of general compliance with the model without excessively penalizing deviating signal allows recovery of a full-rank solution. Our method was compared to standard low-rank approach utilizing model-based dimensionality reduction in phantoms and patient examinations for time-resolved contrast-enhanced angiography (CE MRA) and cardiac CINE imaging. We studied sensitivity of all methods to rank-reduction and temporal subspace modeling errors. MOCCO demonstrated reduced sensitivity to modeling errors compared to the standard approach. Full-rank MOCCO solutions showed significantly improved preservation of temporal fidelity and aliasing/noise suppression in highly accelerated CE MRA (acceleration up to 27) and cardiac CINE (acceleration up to 15) data. MOCCO overcomes several important deficiencies of previously proposed methods based on pre-estimated temporal models and allows high quality image restoration from highly undersampled CE-MRA and cardiac CINE data.
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DOI：
10.1109/tbme.2013.2266096
发表时间：
2013-11
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Christodoulou AG;Zhang H;Zhao B;Hitchens TK;Ho C;Liang ZP
通讯作者：
Liang ZP
影响因子：
4.4
作者：
Eckert, MP;Bradley, AP
通讯作者：
Bradley, AP
影响因子：
3.3
作者：
Adluru, Ganesh;Awate, Suyash P.;DiBella, Edward V. R.
通讯作者：
DiBella, Edward V. R.
影响因子：
3
作者：
Daubechies, Ingrid;Devore, Ronald;Guentuerk, C. Sinan
通讯作者：
Guentuerk, C. Sinan
影响因子：
3.3
作者：
Jung, Hong;Park, Jaeseok;Ye, Jong Chul
通讯作者：
Ye, Jong Chul