超声经颅成像的像素响应优化波束形成方法

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Junyi Wang , Tianhua Zhou , Gaobo Zhang , Boyi Li , Xin Liu , Dean Ta
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引用次数: 0

摘要

在经颅超声成像中,声波在骨中的传播仍然是一个长期的挑战。作为一种高散射介质,颅骨在超声波场中会产生明显的畸变,引入复杂的像差,阻碍了精确的图像重建。传统的延迟和(DAS)算法独立处理像素,不能考虑像素间的关系,限制了它们纠正这种扭曲的能力。为了解决这个问题,我们提出了一种利用复合平面波的后向散射信号的像素响应优化(PRO)波束形成方法。通过构建像素响应矩阵和模拟虚拟声透镜,PRO将失真场与参考相位隔离并对齐,以恢复接近理想的传播。在牛股骨板和人类头骨上的实验证明了图像分辨率的提高、淹没信号的恢复和伪影的抑制。与DAS相比,PRO在半最大全宽度(FWHM)方面提高了90%,不需要预先假设,并且在通过骨骼的复杂情况下显示出很强的通用性。这一进展为未来的体内经颅脑成像应用带来了希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pixel-responsive optimization beamforming method for ultrasound transcranial imaging
The propagation of acoustic waves through bone remains a longstanding challenge in transcranial ultrasound imaging. As a highly scattering medium, the skull causes significant distortions in the ultrasonic wavefield, introducing complex aberrations that hinder precise image reconstruction. Conventional delay-and-sum (DAS) algorithms, which process pixels independently, fail to account for inter-pixel relationships, limiting their ability to correct such distortions. To address this issue, we propose a Pixel-Responsive Optimization (PRO) Beamforming Method that leverages backscattered signals from compound plane waves. By constructing a pixel-response matrix and simulating a virtual acoustic lens, PRO isolates and aligns distorted fields with reference phases to restore near-ideal propagation. Experiments on bovine femur plates and a human skull demonstrate improved image resolution, recovery of submerged signals, and artifact suppression. PRO achieves up to a 90% improvement in full-width at half-maximum (FWHM) compared to DAS, requiring no prior assumptions and showing strong generalizability in complex scenarios through bone. This advancement holds promise for future in vivo transcranial brain imaging applications.
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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