ROBUST NON-LOCAL REGULARIZATION FRAMEWORK FOR MOTION COMPENSATED DYNAMIC IMAGING WITHOUT EXPLICIT MOTION ESTIMATION.
ROBUST NON-LOCAL REGULARIZATION FRAMEWORK FOR MOTION COMPENSATED DYNAMIC IMAGING WITHOUT EXPLICIT MOTION ESTIMATION.
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DOI：
10.1109/isbi.2012.6235740
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Jacob M
中科院分区：
文献类型：
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作者：
Yang Z;Jacob M
我们引入了一种正则化重建方案，用于从欠采样傅里叶数据中恢复具有显着帧间运动的动态成像数据集。所提出的非局部正则化惩罚是 3D 数据集中图像块对之间距离的未加权总和。我们使用稳健的距离度量来计算图像块之间的距离；这些指标鼓励相似补丁之间的平滑，同时阻止不相似补丁的平均。因此，该算法能够利用相邻帧中的块对之间的相似性，即使它们由于运动而被很好地分开，尽管它不执行显式的运动估计。与当前的非局部正则化方案不同，所提出的惩罚不需要良好的初始猜测来估计权重。因此，这种方法很容易适用于加速动态成像问题，而在这些问题中很难获得良好的初始猜测。所提出的方案在数值体模和动态 MRI 数据集上的验证证明了所提出的方案相对于当前动态成像方案的优越性能。
We introduce an regularized reconstruction scheme to recover dynamic imaging datasets with significant inter frame motion from undersampled Fourier data. The proposed non-local regularization penalty is an unweighted sum of distances between image patch pairs in the 3-D dataset. We use robust distance metrics to compute the distance between image patches; these metrics encourage the smoothing between similar patches, while discouraging the averaging of dissimilar patches. Hence, this algorithm is capable of exploiting the similarities between patch pairs in adjacent frames even when they are well separated due to motion, eventhough it does not perform explicit motion estimation. Unlike current non-local regularization schemes, the proposed penalty does not need good initial guesses to estimate the weights. Hence, this approach is readily applicable to accelerated dynamic imaging problems, where good initial guesses are challenging to obtain. The validation of the proposed scheme on numerical phantoms and dynamic MRI datasets demonstrate the superior performance of the proposed scheme over current dynamic imaging schemes.