论文标题
使用具有物理动机的正则化的压缩感测以及压缩感测的多型线圈磁共振成像
Multi-coil Magnetic Resonance Imaging with Compressed Sensing Using Physically Motivated Regularization
论文作者
论文摘要
随着多线圈成像和压缩感应的出现，已经创建了许多基于模型的重建算法。他们根据物理学，观察到的现象学和启发式方法结合了许多不同的正则化功能。此外，存在几种尝试同时估计灵敏度图和图像的迭代方法。在本手稿中，我们介绍了几种基于迭代模型的算法的概括。我们设计了这种概括的无校准实例，该实例仅根据物理学和小波域中稀疏性的被公认的压缩感测现象结合了正则化项。我们将新的合并优化问题的结果与模拟和真实数据集上的现有方法进行了比较。我们表明，使用新方法重建的图像标题为“多线圈压缩感应（MCC）”，在所有研究的情况下都具有比现有方法更高的质量。
With the advent of multi-coil imaging and compressed sensing, a number of model based reconstruction algorithms have been created. They incorporate a multitude of different regularization functions based on physics, observed phenomenology, and heuristics. Moreover, several iterative methods exist that attempt to simultaneously estimate the sensitivity maps and the image. In this manuscript, we present a generalization of several existing iterative model based algorithms. We devise a calibrationless instance of this generalization that only incorporates regularization terms based on physics and the accepted compressed sensing phenomenology of sparsity in the wavelet domain. We compare the results of the new amalgamated optimization problem with existing methods on both simulated and real datasets. We show that the images reconstructed using the new method, entitled Multi-coil Compressed Sensing (MCCS), are of higher quality than existing methods in all cases studied.
