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孔径域模型图像重建的计算高效实现。

Computationally Efficient Implementation of Aperture Domain Model Image Reconstruction.

出版信息

IEEE Trans Ultrason Ferroelectr Freq Control. 2019 Oct;66(10):1546-1559. doi: 10.1109/TUFFC.2019.2924824. Epub 2019 Jun 26.

Abstract

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.

摘要

孔径域模型图像重建(ADMIRE)是一种有用的工具,可以减轻由声波混叠引起的超声成像伪影。然而,其冗长的运行时间使其实用性受到影响。为了克服这一缺点,我们评估了具有类似性能的简化模型方法。我们还评估了其他加速 ADMIRE 的方法,包括使用不同程度的短时傅里叶变换(STFT)窗口重叠,并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。我们使用模拟数据、实验体模和体内肝扫描来评估这些简化模型。然后,我们使用全模型、GSO、SVD 和 ICA-四阶盲识别(ICA-FOBI)模型测试了 ADMIRE 的性能。模拟、实验体模和体内数据的结果表明,使用 ICA-FOBI 方法简化的模型最有希望加速 ADMIRE 的实现。在体内肝数据中,与延迟求和(DAS)相比,全模型、GSO、SVD 和 ICA-FOBI 模型的对比度相对改善分别为 6.29±0.25dB、11.88±0.90dB、9.01±0.67dB 和 6.36±0.27dB;而在同一顺序下的对比度噪声比(CNR)改善值分别为 0.04±0.06dB、-1.70±0.17dB、-1.51±0.19dB 和 0.12±0.07dB。使用简化模型方法可以将 ADMIRE 的计算复杂度降低三个数量级。与以前使用的 90%窗口重叠相比,使用 50%的 STFT 窗口重叠可以将 ADMIRE 的串行运行时间减少一个数量级以上,而不会对图像质量造成明显损失。基于这些发现,ICA-FOBI 模型和 50%STFT 窗口重叠的结合使 ADMIRE 算法更具计算效率。

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