适于腹部成像的行-柱超声阵列

Jian-yu Lu, Xiaoning Jiang
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引用次数: 0

摘要

Fully-addressable二维(2 d)阵列超声波传感器有问题,比如大量的元素,很难用一个成像系统互连,每个数组元素导致声阻抗高的低信噪比(信噪比),医学超声成像系统和高成本,尤其是对一个二维数组的大小接近300毫米,300毫米和一百万多个元素成像等大面积大病人和孕妇的腹部。尽管存在上述问题,但要在人工干预最少的情况下重建用于人工智能(AI)辅助医疗诊断的大型3D体图像,需要大尺寸的阵列,这在缺乏训练有素的医疗专业人员操作超声成像系统的国家或地区尤其理想。为了解决与大型完全可寻址二维阵列换能器相关的问题,已经提出了行列(RC)阵列,以减少二维阵列的元素数量,并降低每个阵列元素的声阻抗以提高信噪比。虽然RC阵列可以解决全可寻址二维阵列的一些问题,大大简化了成像系统,但阵列必须是刚性的,以避免由于阵列变形而引入相位像差。为了解决这一问题,本文提出了36个子阵列,每个子阵列为25.6 mm × 25.6 mm,形成一个尺寸约为281.6 mm × 281.6 mm的大型合格RC阵列。为了通过进一步减少元件数量来简化成像系统,在子阵列之间引入了25.6 mm的柔性材料填充间隙。为了在成像中覆盖子阵列之间的空间,在每个子阵列上应用了一个发散度为300度的二维声透镜。为了确定每个子阵列在图像重建空间中的位置,在每个子阵列上放置3个标记,以便读位相机确定子阵列的位置。用有限衍射阵列光束法对子阵列产生的超声场进行了计算机模拟计算,结果表明,在70 mm深度和0°转向角下,中心频率为2.5 mhz的子阵列的波束宽度约为2.6 mm。因此,在垂直于超声波传播的平面上,脉冲回波波束尺寸约为2.6 mm × 2.6 mm。这表明,所提出的RC阵列能够在人工智能辅助的医疗诊断中以最小的人为干预进行大体积的3D成像。虽然与完全可寻址的二维阵列相比,RC阵列在发射和接收上都只能聚焦于一维,导致图像分辨率较低,旁瓣较高(图像对比度降低),但本文提出的RC阵列是一个很好的折衷方案,可以用于大患者腹部或孕妇腹部等大面积的智能成像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conformable Row-Column Ultrasound Arrays for Abdominal Imaging
Fully-addressable two-dimensional (2D) array ultrasound transducers have issues such as a large number of elements, difficult to interconnect with an imaging system, high acoustic impedance of each array element which leads to a low signal-to-noise ratio (SNR), and high cost for medical ultrasound imaging systems, especially, for a 2D array of a size close to 300 mm by 300 mm with more than one million elements for imaging of a large area such as the abdomen of big patients and pregnant women. Despite the issues above, an array of a large size is necessary to reconstruct a large 3D volumetric image for artificial intelligent (AI) assisted medical diagnoses with minimal human interventions, which is especially desirable in countries or areas where there are few highly trained medical professionals to operate ultrasound imaging systems.To address the issues associated with large fully-addressable 2D array transducers, row-column (RC) arrays have been proposed to reduce the number of elements of the 2D arrays and reduce the acoustic impedance of each array element to increase SNR.Although a RC array can address some issues of the fully-addressable 2D arrays and simplify the imaging system greatly, the array must be rigid to avoid introducing phase aberrations due to deformation of the array. To solve the problem, in this paper, 36 sub-arrays of 25.6 mm by 25.6 mm each are proposed to form a large conformable RC array of a size of about 281.6 mm by 281.6 mm. To simplify the imaging system by further reducing the number of elements, a 25.6-mm gap filled with flexible materials between the sub-arrays is introduced. To cover the space between the sub-arrays in imaging, a 2D acoustic lens of 30o divergence is applied to each sub-array. To determine the position of each sub-array in the space for image reconstruction, 3 markers are placed on each sub-array to allow a position-reading camera to determine the position of the sub-array.Computer simulation with limited-diffraction array beam method was performed to calculate the ultrasound fields produced by a sub-array and the results show that the beam width of each sub-array of 2.5-MHz center frequency is about 2.6 mm at a depth of 70 mm and 0° steering angle. Thus, the pulse-echo beam dimension is about 2.6 mm by 2.6 mm in the plane that is perpendicular to the ultrasound wave propagation.This demonstrates that the proposed RC array is capable of 3D imaging of a large volume for AI-assisted medical diagnoses with minimal human intervention. Although, as compared to the fully-addressable 2D arrays, the RC arrays can only focus in one dimension in both transmission and reception, leading to lower image resolution and higher sidelobe (reduced image contrast), the proposed RC array is a good compromise and make it feasible for intelligent imaging of a large area such as the abdomen of big patients or pregnant women.
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