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A fast and high frame rate adaptive beamforming using DCT-based RF-line recovery in line-by-line ultrasound imaging

机译:快速和高帧速率自适应波束成形,使用基于DCT的RF线恢复在线超声成像

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Ultrasound imaging is an important modality used in medical imaging. One of the significant stages in the ultrasound imaging is the beamforming process. This article proposes a new technique for reducing the overall computational time of adaptive linear ultrasound imaging. The method uses the discrete cosine transform-based reconstruction for missing data imputation. The novelty of the paper is that we do not need to beam-form the total scan lines, so the time of image construction can be saved significantly. In other words, a fraction of the total scan lines is selected for beamforming and the others are assumed to have values as Not-a-Number (NaN). The proposed reconstruction technique tries to assign appropriate values to the NaN ones. We applied the proposed method to the simulated and experimental radio frequency (RF) datasets for resolution and contrast evaluation. Results showed that the proposed technique is near to the minimum variance (MV) method in terms of resolution and contrast, and has less computational time for image formation compared to the MV. As some quantitative examples in some experiments we have formed only 50% and 33% of the total lines and reconstructed the rest, then we have been able to increase the frame rate twice and three times, respectively, which can be very useful in many applications, especially in echocardiography imaging. In addition, since the execution time of the reconstruction algorithm is not very significant, we were also able to increase the speed by two and three times while achieving an error of less than 10% compared to the case of using all image lines.
机译:超声成像是医学成像中使用的重要态度。超声成像中的一个重要阶段是波束形成过程。本文提出了一种降低自适应线性超声成像的整体计算时间的新技术。该方法采用基于离散的余弦变换基于缺失数据归档的重建。本文的新奇是我们不需要束形成总扫描线,因此可以显着地节省图像结构的时间。换句话说,选择用于波束成形的总扫描线的一部分,并且假设其他人被认为具有不值(NaN)的值。所提出的重建技术试图将适当的值分配给纳米。我们将所提出的方法应用于模拟和实验射频(RF)数据集以进行分辨率和对比度评估。结果表明,在分辨率和对比度方面,该技术靠近最小方差(MV)方法,并且与MV相比,图像形成的计算时间较少。作为一些实验中的一些定量例子,我们只形成了总线的50%和33%并重建了其余的,然后我们已经分别增加了两次帧速率和三次,这在许多应用中都非常有用,特别是在超声心动图成像中。另外,由于重建算法的执行时间不是很大,因此我们还能够将速度提高两次和三次,同时与使用所有图像线的情况相比,实现误差小于10%。

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