The temporal super-resolution of the dynamic ultrasound imaging, a means to observe rapid heart movements, is considered an important subject in medical diagnosis of cardiac conditions. Here, a new technique based on the acquisition scheme using the matrix completion (MC) theory is offered for the temporal super-resolution of the two-dimensional (2D) and three-dimensional (3D) ultrasound imaging. MC mentions the problem of completing a low-rank matrix when only a subset of its elements can be observed. Here, the lower scan lines are acquired. Whereby, the proposed method uses temporal and spatial information of the radio frequency (RF) image sequences for the reconstruction of skipped RF lines. This is performed using the construction of the MC images and then reconstruction of them by the MC theory. The results of the proposed method are compared with the compressive sensing (CS) reconstruction methods. The qualitative and quantitative evaluations of 2D and 3D data demonstrate that in the proposed method, which uses the spatial and temporal relation of RF images and the MC theory, the reconstruction is more accurate, and the reconstruction error is lower. The computational complexity of this method is very low. It also does not require hardware adjustments. Therefore, it can be easily implemented in current ultrasound-imaging devices with the frame-rate enhancement. For instance, the frame rate up to two times the original sequence is feasible using the proposed methods, while root mean square error is decreased by about 35% and 30% for 2D and 3D data, respectively, compared with the CS reconstruction method.

中文翻译：

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使用矩阵补全的超声成像的时间超分辨率

动态超声成像的时间超分辨率是一种观察快速心脏运动的手段，被认为是心脏病医学诊断中的重要课题。在这里，为二维 (2D) 和三维 (3D) 超声成像的时间超分辨率提供了一种基于使用矩阵完成 (MC) 理论的采集方案的新技术。MC 提到了在只能观察到其元素的子集时完成低秩矩阵的问题。这里，获取较低的扫描线。因此，所提出的方法使用射频 (RF) 图像序列的时间和空间信息来重建跳过的 RF 线。这是使用 MC 图像的构造然后通过 MC 理论重建它们来执行的。将所提出的方法的结果与压缩感知（CS）重建方法进行了比较。2D和3D数据的定性和定量评估表明，所提出的方法利用RF图像的时空关系和MC理论，重建更准确，重建误差更低。这种方法的计算复杂度非常低。它也不需要硬件调整。因此，它可以很容易地在当前具有帧率增强的超声成像设备中实现。例如，使用所提出的方法可以实现高达原始序列两倍的帧速率，而与 CS 重建方法相比，2D 和 3D 数据的均方根误差分别降低了约 35% 和 30%。