A BLIND COMPRESSIVE SENSING FRAMEWORK FOR ACCELERATED DYNAMIC MRI.
A BLIND COMPRESSIVE SENSING FRAMEWORK FOR ACCELERATED DYNAMIC MRI.
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DOI：
10.1109/isbi.2012.6235741
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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：
Goud Lingala S;Jacob M
我们提出了一种新的盲压缩感知（BCS）框架工作，以恢复动态图像从欠采样测量。该方案将动态信号建模为从大型字典中选择的时间基函数的稀疏线性组合。字典和稀疏系数同时估计从欠采样的测量。由于该模型的自由度的数量远小于目前的低秩方法，该计划预计将提供改进的重建数据集具有相当大的帧间运动。我们开发了一个有效的优化最小化算法来解决动态图像。我们使用一个连续的策略，以尽量减少收敛到局部极小值的算法。BCS格式与低秩方法的数值比较表明，在存在运动的情况下，BCS格式的性能得到了显著改善。
We propose a novel blind compressive sensing (BCS) frame work to recover dynamic images from under-sampled measurements. This scheme models the the dynamic signal as a sparse linear combination of temporal basis functions, chosen from a large dictionary. The dictionary and the sparse coefficients are simultaneously estimated from the under-sampled measurements. Since the number of degrees of freedom of this model is much smaller than that of current low-rank methods, this scheme is expected to provide improved reconstructions for datasets with considerable inter-frame motion. We develop an efficient majorize-minimize algorithm to solve for the dynamic images. We use a continuation strategy to minimize the convergence of the algorithm to local minima. Numerical comparisons of the BCS scheme with low-rank methods demonstrate the significant improvement in performance in the presence of motion.