Suppression of the Tissue Component With the Total Least-Squares Algorithm to Improve Second Harmonic Imaging of Ultrasound Contrast Agents

IF 3 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Imaging Systems and Technology Pub Date : 2024-11-14 DOI:10.1002/ima.23218

Jingying Zhu, Yufeng Zhang, Bingbing He, Zhiyao Li, Li Xiong, Xun Lang

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引用次数: 0

Abstract

The second harmonic (SH) of ultrasound contrast agents (UCAs) is widely used in contrast-enhanced ultrasound imaging; however, is affected by the nonlinearity of surrounding tissue. Suppression of the tissue component based on the total least-squares (STLS) algorithm is proposed to enhance the SH imaging of UCAs. The image blocks of pulse-inversion-based SH images before and after UCA injections are set as the reference and input of the total least-squares model, respectively. The optimal coefficients of the model are obtained by minimizing the Frobenius norm of perturbations in the input and output signals. After processing all image blocks, the complete SH image of UCAs is obtained by subtracting the optimal output of the model (i.e., the estimated tissue SH image) from the SH image after UCA injection. Simulation and in vivo experiments confirm that the STLS approach offers clearer capillaries. For in vivo experiments, the STLS-based contrast-to-tissue ratios and contrasts increase by 26.90% and 56.27%, as well as 26.99% and 56.43%, respectively, compared with those based on bubble-echo deconvolution and pulse inversion bubble-wavelet imaging methods. The STLS approach enhances the SH imaging of UCAs by effectively suppressing more tissue SH components, having the potential to provide more accurate diagnostic information for clinical applications.

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用最小二乘总算法抑制组织成分，改善超声造影剂的二次谐波成像效果

超声造影剂（UCAs）的二次谐波（SH）被广泛应用于造影剂增强超声成像，但它会受到周围组织非线性的影响。为了增强 UCA 的二次谐波成像，提出了基于总最小二乘（STLS）算法的组织成分抑制方法。UCA 注射前后基于脉冲反转的 SH 图像块分别被设定为总最小二乘模型的参考和输入。通过最小化输入和输出信号中扰动的 Frobenius 准则，获得模型的最佳系数。处理完所有图像块后，从 UCA 注射后的 SH 图像中减去模型的最佳输出（即估计的组织 SH 图像），就得到了完整的 UCA SH 图像。模拟和活体实验证实，STLS 方法能提供更清晰的毛细血管。在活体实验中，与基于气泡回波解卷积和脉冲反转气泡小波成像方法的对比度和对比度相比，基于 STLS 的对比度和对比度分别提高了 26.90% 和 56.27%，以及 26.99% 和 56.43%。STLS 方法能有效抑制更多的组织 SH 成分，从而增强 UCA 的 SH 成像，有望为临床应用提供更准确的诊断信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术

CiteScore

6.90

自引率

6.10%

发文量

138

审稿时长

3 months

期刊介绍： The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.