Dynamic Filtering of Adherent and Non-adherent Microbubble Signals Using Singular Value Thresholding and Normalized Singular Spectrum Area Techniques.
Dynamic Filtering of Adherent and Non-adherent Microbubble Signals Using Singular Value Thresholding and Normalized Singular Spectrum Area Techniques.
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DOI：
10.1016/j.ultrasmedbio.2021.06.019
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发表时间：
2021-11
影响因子：
2.9
通讯作者：
Mauldin FW Jr
中科院分区：
文献类型：
--
作者：
Herbst EB;Klibanov AL;Hossack JA;Mauldin FW Jr
超声分子成像技术取决于三种类型的信号的分离和鉴定：静态组织，粘附的微泡和非粘附微泡中的分离和鉴定。后肢肿瘤模型（n = 24）。使用带有L12-5的Verasonics Vantage 256成像系统，使用合成孔径虚拟源元素（SAVSE）成像的定制脉冲反转（PI）序列被用于收集用微生物抑制静态组织的小鼠肿瘤的对比度。将Microbubbles信号分类为粘附或具有高精度的非贴材（接收器的操作特征区域（ROC AUC）= 0.97），匹配差分靶向增强的分类性能（DTE）。可以用于自动分段和对比度信号进行分类。
Ultrasound molecular imaging techniques rely upon the separation and identification of three types of signals: static tissue, adherent microbubbles, and non-adherent microbubbles. In this study, the image filtering techniques of singular value thresholding (SVT) and normalized singular spectrum area (NSSA) were combined to isolate and identify vascular endothelial growth factor receptor 2 (VEGFR2) targeted microbubbles in a mouse hindlimb tumor model (n = 24). Using a Verasonics Vantage 256 imaging system with an L12–5 transducer, a custom-programmed pulse inversion (PI) sequence employing synthetic aperture virtual source element (SAVSE) imaging was used to collect contrast images of mouse tumors perfused with microbubbles. SVT was used to suppress static tissue signals by 9.6 dB while retaining adherent and non-adherent microbubble signals. NSSA was used to classify microbubble signals as adherent or non-adherent with high accuracy (Receiver operating characteristic area under the curve (ROC AUC) = 0.97), matching the classification performance of differential targeted enhancement (dTE). The combined SVT+NSSA filtering method also outperformed dTE in differentiating MB signals from all other signals (ROC AUC = 0.89) without necessitating the destruction of the contrast agent. The results from this study show that SVT and NSSA can be used to automatically segment and classify contrast signals. This filtering method with potential real-time capability could be used in future diagnostic settings to improve workflow and speed the clinical uptake of ultrasound molecular imaging techniques.