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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >Computationally Efficient Implementation of Aperture Domain Model Image Reconstruction
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Computationally Efficient Implementation of Aperture Domain Model Image Reconstruction

机译:计算有效地实现Aperture域模型图像重建

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Aperture domain model image reconstruction (ADMIRE) is a useful tool to mitigate ultrasound imaging artifacts caused by acoustic clutter. However, its lengthy run-time impairs its usefulness. To overcome this drawback, we evaluated the reduced model methods with otherwise similar performance to ADMIRE. We also assessed other approaches to speed up ADMIRE, including the use of different levels of short-time Fourier transform (STFT) window overlap and examining the degrees of freedom of the model fit. In this study, we conducted an analysis of the reduced models, including those using Gram-Schmidt orthonormalization (GSO), singular value decomposition (SVD), and independent component analysis (ICA). We evaluated these reduced models using the data from simulations, experimental phantoms, and in vivo liver scans. We then tested ADMIRE's performance using full, GSO, SVD, and ICA-fourth-order blind identification (ICA-FOBI) models. The results from simulations, experimental phantoms, and in vivo data indicate that a model reduced using the ICA-FOBI method is the most promising for accelerating ADMIRE implementation. In the in vivo liver data, the improvements in contrast relative to delay-and-sum (DAS) using a full model, GSO, SVD, and ICA-FOBI models are 6.29 +/- 0.25 dB, 11.88 +/- 0.90 dB, 9.01 +/- 0.67 dB, and 6.36 +/- 0.27 dB, respectively; whereas, the contrast-to-noise ratio (CNR) improvement values in the same order are 0.04 +/- 0.06 dB, -1.70 +/- 0.17 dB, -1.51 +/- 0.19 dB, and 0.12 +/- 0.07 dB, respectively. The implementation of ADMIRE using the reduced model methods can decrease ADMIRE's computational complexity over three orders of magnitude. The use of a 50% STFT window overlap reduces ADMIRE's serial run time by more than one order of magnitude without any remarkable loss of image quality, when compared to the use of a 90% window overlap used previously. Based on these findings, a combination of the ICA-FOBI model and the use of a 50% STFT window overlap makes the ADMIRE algorithm more computationally efficient.
机译:孔径域模型图像重建(ADMIRE)是一种有用的工具,可减轻由声杂波引起的超声成像伪像。但是,其运行时间过长会削弱其实用性。为了克服此缺点,我们评估了简化的模型方法,其性能与ADMIRE相似。我们还评估了其他加快ADMIRE的方法,包括使用不同级别的短时傅立叶变换(STFT)窗口重叠以及检查模型拟合的自由度。在这项研究中,我们对简化模型进行了分析,包括使用Gram-Schmidt正交归一化(GSO),奇异值分解(SVD)和独立成分分析(ICA)的模型。我们使用来自模拟,实验体模和体内肝脏扫描的数据评估了这些简化的模型。然后,我们使用完整的,GSO,SVD和ICA四阶盲识别(ICA-FOBI)模型测试了ADMIRE的性能。仿真,实验模型和体内数据的结果表明,使用ICA-FOBI方法简化的模型对于加速ADMIRE实施是最有希望的。在体内肝脏数据中,相对于使用完整模型,GSO,SVD和ICA-FOBI模型的延迟总和(DAS)而言,对比度的改进为6.29 +/- 0.25 dB,11.88 +/- 0.90 dB,分别为9.01 +/- 0.67 dB和6.36 +/- 0.27 dB;相反,对比度与噪声比(CNR)的改善顺序相同,分别为0.04 +/- 0.06 dB,-1.70 +/- 0.17 dB,-1.51 +/- 0.19 dB和0.12 +/- 0.07 dB,分别。使用简化模型方法实施ADMIRE可以将ADMIRE的计算复杂度降低三个数量级。与先前使用的90%窗口重叠相比,使用50%STFT窗口重叠可将ADMIRE的串行运行时间减少一个数量级以上,而不会显着降低图像质量。基于这些发现,ICA-FOBI模型与50%STFT窗口重叠的结合使ADMIRE算法的计算效率更高。

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