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混合方法:基于L1范数的应变张量超声弹性成像中的混合二阶连续性

MixTURE: L1-Norm-Based Mixed Second-Order Continuity in Strain Tensor Ultrasound Elastography.

作者信息

Ashikuzzaman Md, Sharma Arunima, Venkatayogi Nethra, Oluyemi Eniola, Myers Kelly, Ambinder Emily, Rivaz Hassan, Lediju Bell Muyinatu A

出版信息

IEEE Trans Ultrason Ferroelectr Freq Control. 2024 Nov;71(11):1389-1405. doi: 10.1109/TUFFC.2024.3449815. Epub 2024 Nov 27.

Abstract

Energy-based displacement tracking of ultrasound images can be implemented by optimizing a cost function consisting of a data term, a mechanical congruency term, and first- and second-order continuity terms. This approach recently provided a promising solution to 2-D axial and lateral displacement tracking in ultrasound strain elastography. However, the associated second-order regularizer only considers the unmixed second derivatives and disregards the mixed derivatives, thereby providing suboptimal noise suppression and limiting possibilities for total strain tensor imaging. We propose to improve axial, lateral, axial shear, and lateral shear strain estimation quality by formulating and optimizing a novel -norm-based second-order regularizer that penalizes both mixed and unmixed displacement derivatives. We name the proposed technique -MixTURE, which stands for -norm Mixed derivative for Total UltRasound Elastography. When compared with simulated ground-truth results, the mean structural similarity (MSSIM) obtained with -MixTURE ranged 0.53-0.86 and the mean absolute error (MAE) ranged 0.00053-0.005. In addition, the mean elastographic signal-to-noise ratio (SNR) achieved with simulated, experimental phantom, and in vivo breast datasets ranged 1.87-52.98, and the mean elastographic contrast-to-noise ratio (CNR) ranged 7.40-24.53. When compared with a closely related existing technique that does not consider the mixed derivatives, -MixTURE generally outperformed the MSSIM, MAE, SNR, and CNR by up to 37.96%, 67.82%, and 25.53% in the simulated, experimental phantom, and in vivo datasets, respectively. These results collectively highlight the ability of -MixTURE to deliver highly accurate axial, lateral, axial shear, and lateral shear strain estimates and advance the state-of-the-art in elastography-guided diagnostic and interventional decisions.

摘要

基于能量的超声图像位移跟踪可以通过优化一个由数据项、机械一致性项以及一阶和二阶连续性项组成的成本函数来实现。这种方法最近为超声应变弹性成像中的二维轴向和横向位移跟踪提供了一个有前景的解决方案。然而,相关的二阶正则化器仅考虑了未混合的二阶导数,而忽略了混合导数,从而提供了次优的噪声抑制,并限制了全应变张量成像的可能性。我们建议通过制定和优化一种基于新颖范数的二阶正则化器来提高轴向、横向、轴向剪切和横向剪切应变估计质量,该正则化器同时惩罚混合和未混合的位移导数。我们将所提出的技术命名为 -MixTURE,它代表用于全超声弹性成像的范数混合导数。与模拟的真实结果相比,使用 -MixTURE 获得的平均结构相似性(MSSIM)范围为 0.53 - 0.86,平均绝对误差(MAE)范围为 0.00053 - 0.005。此外,使用模拟、实验体模和体内乳腺数据集获得的平均弹性成像信噪比(SNR)范围为 1.87 - 52.98,平均弹性成像对比度噪声比(CNR)范围为 7.40 - 24.53。与一种不考虑混合导数的密切相关的现有技术相比,在模拟、实验体模和体内数据集中,-MixTURE 的 MSSIM、MAE、SNR 和 CNR 分别普遍高出多达 37.96%、67.82% 和 25.53%。这些结果共同突出了 -MixTURE 在提供高精度轴向、横向、轴向剪切和横向剪切应变估计方面的能力,并推动了弹性成像引导的诊断和介入决策的技术发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abe/11861389/ae41804e0cf0/nihms-2039051-f0010.jpg

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