IMPROVED ULTRASONIC IMAGING VIA SYSTEM IDENTIFICATION

描述(改编自申请者摘要)：当前超声成像 技术导致了图像，尽管换能器取得了进步 技术，仍然包含着高度的扭曲。超声图像 是通过测量超声子波的反射而获得的 从放置在皮肤上的换能器传输的。深度变化 皮下组织的声阻抗产生的反射可以是 用于对组织边界进行成像。横向扭曲， 垂直于超声波的传播路径，是由于 超声波束的衍射展开度，并可用 相控阵聚焦并将主超声波束引导到所需的 方向。超声图像失真的另一个重要来源 发生在沿超声传播路径的轴向上，并且是 由于出射超声波的卷积产生的混响 小波与介入组织反射剖面。最多的尝试是 对轴向失真的补偿涉及到某种形式的反卷积 其中反射信号由逆滤波滤波，以尝试 提高图像的轴向分辨率。有很多问题 这是反卷积方法所固有的，已经得到了很好的记录。 其中包括由于非最小相位而导致的无法实现的逆滤波 小波，对噪声高度敏感，关于组织的不切实际的假设 和传感器特性，以及统计平稳性要求- 这意味着反卷积并不容易用来成像 时变的组织结构。这里提出的方法解决了这些问题 自适应系统辨识在超声检测中的应用问题 图像。这种方法的意义在于，与反卷积不同 方法，系统识别不对组织或 换能器的特性，并且对噪声的敏感性低得多。这个 系统识别的概念也特别适合于 时变的组织结构。提出了一种新的系统辨识算法 描述了特别适合这项任务的，因为它有很高的 对测量噪声不敏感。用于设置超声波的设备 寻找成像实验室。该设备包括超声波换能器、 用于激励换能器和调节接收器的脉冲发生器/接收器 超声波、用于保持换能器和靶的水箱、组件 这允许非常精确地控制传感器/目标位置，并且 用于进行高速数据采集和显示的数字示波器。 这项研究旨在提高A型扫描的轴向分辨率 系统标识。长期计划要求扩大这一系统 识别概念到B超和相控阵技术。

DESCRIPTION (Adapted from applicant's abstract): Current ultrasonic imaging technology results in images which, despite advances in transducer technology, still contain a high level of distortion. The ultrasonic image is obtained by measuring the reflections of an ultrasound wavelet transmitted from a transducer positioned on the skin. Depth variations in the acoustic impedance of subdermal tissues produce reflections which can be used to image tissue boundaries. Distortion in the lateral direction, perpendicular to the propagation path of the ultrasound, is due to diffraction spreading of the ultrasound beam and can be compensated by using phased arrays to focus and steer the main ultrasonic beam in the desired direction. Another significant source of distortion in ultrasonic images occurs on the axial direction, along the ultrasound propagation path, and is due to reverberation resulting from a convolution of the outgoing ultrasonic wavelet with the intervening tissue reflection profile. Most attempts at compensating for axial distortion have involved some form of deconvolution where the reflected signal is filtered by an inverse filter in an attempt to increase the axial resolution of the image. There are a number of problems inherent in the deconvolution approach which have been well-documented. These include unrealizable inverse filters due to non-minimum phase wavelets, a high sensitivity to noise, unrealistic assumptions about tissue and transducer characteristics, and a statistical stationarity requirement - implying that deconvolution does not readily lend itself to imaging time-varying tissue structures. The method proposed here addresses these problems by using adaptive system identification to measure ultrasonic images. The significance of this approach is that, unlike deconvolution methods, system identification makes no assumptions about the tissue or transducer characteristics, and is much less susceptible to noise. The system identification concept is also particularly well suited for time-varying tissue structures. A new system identification algorithm is described which is particularly well-suited for this task given its high insensitivity to measurement noise. Equipment for setting up an ultrasound imaging lab is sought. The equipment includes ultrasound transducers, a pulser/receiver for exciting the transducer and conditioning the received ultrasound, a water tank for holding the transducer and target, an assembly that allows very accurate control of transducer/target position, and a digital oscilloscope for doing high speed data acquisition and display. This research seeks to improve the axial resolution of A-mode scans using system identification. Long-term plans call for extending the system identification concept to B-scans and phased array technology.