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Adaptive attenuation correction during H-scan ultrasound imaging using K-means clustering
Ultrasonics ( IF 4.2 ) Pub Date : 2020-03-01 , DOI: 10.1016/j.ultras.2019.105987
Haowei Tai 1 , Mawia Khairalseed 2 , Kenneth Hoyt 3
Affiliation  

H-scan ultrasound (US) imaging (where the 'H' stands for Hermite) is a novel non-invasive, low cost and real-time technology. Like traditional US, H-scan US suffers from frequency-dependent attenuation that must be corrected to have acceptable image quality for tissue characterization. The goal of this research was to develop a novel attenuation correction method based on adaptive K-means clustering. To properly isolate these signals, a lateral moving window approach applied to adaptively adjust GH filters based on the changing of RF vector spectrums. Then the signal isolated via the same filter will be combined together via overlap-add technology to keep the information loss minimum. Experimental data was collected using a Verasonics 256 US scanner equipped with a L11-4v linear array transducer. In vivo data indicates that H-scan US imaging after adaptive attenuation correction can optimally re-scale the GH kernels and match to the changing spectrum undergoing attenuation (i.e. high frequency shift). This approach produces H-scan US images with more uniform spatial intensity and outperforms global attenuation correction strategies. Overall, this approach will improve the ability of H-scan US imaging to estimate acoustic scatterer size and will improve its clinical use for tissue characterization when imaging complex tissues.

中文翻译:

使用 K 均值聚类进行 H 扫描超声成像期间的自适应衰减校正

H 扫描超声 (US) 成像(其中“H”代表 Hermite)是一种新型的非侵入性、低成本和实时技术。与传统的 US 一样,H 扫描 US 受到频率相关衰减的影响,必须对其进行校正以获得可接受的组织表征图像质量。本研究的目标是开发一种基于自适应 K 均值聚类的新型衰减校正方法。为了正确隔离这些信号,应用横向移动窗口方法根据 RF 矢量频谱的变化自适应调整 GH 滤波器。然后通过相同滤波器隔离的信号将通过重叠相加技术组合在一起,以保持信息损失最小。使用配备 L11-4v 线性阵列换能器的 Verasonics 256 US 扫描仪收集实验数据。体内数据表明,自适应衰减校正后的 H 扫描 US 成像可以最佳地重新缩放 GH 内核,并与经历衰减(即​​高频偏移)的不断变化的频谱相匹配。这种方法产生具有更均匀空间强度的 H 扫描 US 图像,并且优于全局衰减校正策略。总体而言,这种方法将提高 H 扫描 US 成像估计声散射体大小的能力,并将改进其在复杂组织成像时的组织表征的临床应用。
更新日期:2020-03-01
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